feat: dimension pareto cache-based computation, filter propagation, and MSD events cache isolation

Reject History: - Compute dimension pareto (package/type/workflow/workcenter/equipment) from cached DataFrame instead of re-querying Oracle per dimension change - Propagate supplementary filters and trend date selection to dimension pareto - Add staleness tracking to prevent race conditions on rapid dimension switches - Add WORKFLOWNAME to detail and export outputs - Fix button hover visibility with CSS specificity MSD (製程不良追溯分析): - Separate raw events caching from aggregation computation so changing loss_reasons uses EventFetcher per-domain cache (fast) and recomputes aggregation with current filters instead of returning stale cached results - Exclude loss_reasons from MSD seed cache key since seed resolution does not use it, avoiding unnecessary Oracle re-queries - Add suspect context panel, analysis summary, upstream station/spec filters - Add machine bar click drill-down and filtered attribution charts Query Tool: - Support batch container_ids in lot CSV export (history/materials/rejects/holds) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-25 09:02:39 +08:00
parent 983737ca1a
commit 86984cfeb1
28 changed files with 1768 additions and 86 deletions
--- a/frontend/src/mid-section-defect/App.vue
+++ b/frontend/src/mid-section-defect/App.vue
@@ -10,7 +10,9 @@ import KpiCards from './components/KpiCards.vue';
 import MultiSelect from './components/MultiSelect.vue';
 import ParetoChart from './components/ParetoChart.vue';
 import TrendChart from './components/TrendChart.vue';
+import AnalysisSummary from './components/AnalysisSummary.vue';
 import DetailTable from './components/DetailTable.vue';
+import SuspectContextPanel from './components/SuspectContextPanel.vue';

 ensureMesApiAvailable();

@@ -170,6 +172,12 @@ const filteredByMachineData = computed(() => {
  return filtered.length > 0 ? buildMachineChartFromAttribution(filtered) : [];
 });

+const suspectMachineNames = computed(() => {
+  const data = filteredByMachineData.value;
+  if (!Array.isArray(data)) return [];
+  return data.filter((d) => d.name && d.name !== '其他').map((d) => d.name);
+});
+
 const isForward = computed(() => committedFilters.value.direction === 'forward');
 const committedStation = computed(() => {
  const key = committedFilters.value.station || '測試';
@@ -198,7 +206,25 @@ const eventsAggregation = computed(() => trace.stage_results.events?.aggregation
 const showAnalysisSkeleton = computed(() => hasQueried.value && loading.querying && !eventsAggregation.value);
 const showAnalysisCharts = computed(() => hasQueried.value && (Boolean(eventsAggregation.value) || restoredFromCache.value));

-const skeletonChartCount = computed(() => (isForward.value ? 4 : 6));
+const skeletonChartCount = computed(() => (isForward.value ? 4 : 5));
+
+const totalAncestorCount = computed(() => trace.stage_results.lineage?.total_ancestor_count || analysisData.value?.total_ancestor_count || 0);
+
+const summaryQueryParams = computed(() => {
+  const snap = committedFilters.value;
+  const params = {
+    queryMode: snap.queryMode || 'date_range',
+    startDate: snap.startDate,
+    endDate: snap.endDate,
+    lossReasons: snap.lossReasons || [],
+  };
+  if (snap.queryMode === 'container') {
+    params.containerInputType = snap.containerInputType || 'lot';
+    params.resolvedCount = resolutionInfo.value?.resolved_count || 0;
+    params.notFoundCount = resolutionInfo.value?.not_found?.length || 0;
+  }
+  return params;
+});

 function emptyAnalysisData() {
  return {
@@ -364,6 +390,7 @@ async function loadAnalysis() {
      analysisData.value = {
        ...analysisData.value,
        ...eventsAggregation.value,
+        total_ancestor_count: trace.stage_results.lineage?.total_ancestor_count || 0,
      };
    }

@@ -429,6 +456,21 @@ function exportCsv() {
  document.body.removeChild(link);
 }

+// Suspect context panel state
+const suspectPanelMachine = ref(null);
+
+function handleMachineBarClick({ name, dataIndex }) {
+  if (!name || name === '其他') return;
+  const attribution = analysisData.value?.attribution;
+  if (!Array.isArray(attribution)) return;
+  const match = attribution.find(
+    (rec) => rec.EQUIPMENT_NAME === name,
+  );
+  if (match) {
+    suspectPanelMachine.value = suspectPanelMachine.value?.EQUIPMENT_NAME === name ? null : match;
+  }
+}
+
 const _abortControllers = new Map();
 function createAbortSignal(key = 'default') {
  const prev = _abortControllers.get(key);
@@ -542,6 +584,14 @@ void initPage();

      <transition name="trace-fade">
        <div v-if="showAnalysisCharts">
+          <AnalysisSummary
+            v-if="!isForward"
+            :query-params="summaryQueryParams"
+            :kpi="analysisData.kpi"
+            :total-ancestor-count="totalAncestorCount"
+            :station-label="committedStation"
+          />
+
          <KpiCards
            :kpi="analysisData.kpi"
            :loading="false"
@@ -552,35 +602,40 @@ void initPage();
          <div class="charts-section">
            <template v-if="!isForward">
              <div class="charts-row">
-                <ParetoChart title="依上游機台歸因" :data="filteredByMachineData">
-                  <template #header-extra>
-                    <div class="chart-inline-filters">
-                      <MultiSelect
-                        v-if="upstreamStationOptions.length > 1"
-                        :model-value="upstreamStationFilter"
-                        :options="upstreamStationOptions"
-                        placeholder="全部站點"
-                        @update:model-value="upstreamStationFilter = $event; upstreamSpecFilter = []"
-                      />
-                      <MultiSelect
-                        v-if="upstreamSpecOptions.length > 1"
-                        :model-value="upstreamSpecFilter"
-                        :options="upstreamSpecOptions"
-                        placeholder="全部型號"
-                        @update:model-value="upstreamSpecFilter = $event"
-                      />
-                    </div>
-                  </template>
-                </ParetoChart>
+                <div class="chart-with-panel">
+                  <ParetoChart title="依上游機台歸因" :data="filteredByMachineData" enable-click @bar-click="handleMachineBarClick">
+                    <template #header-extra>
+                      <div class="chart-inline-filters">
+                        <MultiSelect
+                          v-if="upstreamStationOptions.length > 1"
+                          :model-value="upstreamStationFilter"
+                          :options="upstreamStationOptions"
+                          placeholder="全部站點"
+                          @update:model-value="upstreamStationFilter = $event; upstreamSpecFilter = []"
+                        />
+                        <MultiSelect
+                          v-if="upstreamSpecOptions.length > 1"
+                          :model-value="upstreamSpecFilter"
+                          :options="upstreamSpecOptions"
+                          placeholder="全部型號"
+                          @update:model-value="upstreamSpecFilter = $event"
+                        />
+                      </div>
+                    </template>
+                  </ParetoChart>
+                  <SuspectContextPanel
+                    :machine="suspectPanelMachine"
+                    @close="suspectPanelMachine = null"
+                  />
+                </div>
+                <ParetoChart title="依原物料歸因" :data="analysisData.charts?.by_material" />
+              </div>
+              <div class="charts-row">
+                <ParetoChart title="依源頭批次歸因" :data="analysisData.charts?.by_wafer_root" />
                <ParetoChart title="依不良原因" :data="analysisData.charts?.by_loss_reason" />
              </div>
              <div class="charts-row">
                <ParetoChart title="依偵測機台" :data="analysisData.charts?.by_detection_machine" />
-                <ParetoChart title="依製程 (WORKFLOW)" :data="analysisData.charts?.by_workflow" />
-              </div>
-              <div class="charts-row">
-                <ParetoChart title="依封裝 (PACKAGE)" :data="analysisData.charts?.by_package" />
-                <ParetoChart title="依 TYPE" :data="analysisData.charts?.by_pj_type" />
              </div>
            </template>
            <template v-else>
@@ -606,6 +661,7 @@ void initPage();
        :loading="detailLoading"
        :pagination="detailPagination"
        :direction="committedFilters.direction"
+        :suspect-machines="suspectMachineNames"
        @export-csv="exportCsv"
        @prev-page="prevPage"
        @next-page="nextPage"
--- a/frontend/src/mid-section-defect/components/AnalysisSummary.vue
+++ b/frontend/src/mid-section-defect/components/AnalysisSummary.vue
@@ -0,0 +1,163 @@
+<script setup>
+import { ref, watch } from 'vue';
+
+const STORAGE_KEY = 'msd:summary-collapsed';
+
+const props = defineProps({
+  queryParams: {
+    type: Object,
+    default: () => ({}),
+  },
+  kpi: {
+    type: Object,
+    default: () => ({}),
+  },
+  totalAncestorCount: {
+    type: Number,
+    default: 0,
+  },
+  stationLabel: {
+    type: String,
+    default: '測試',
+  },
+});
+
+const collapsed = ref(false);
+
+// Restore from sessionStorage
+try {
+  const saved = sessionStorage.getItem(STORAGE_KEY);
+  if (saved === 'true') collapsed.value = true;
+} catch { /* unavailable */ }
+
+watch(collapsed, (val) => {
+  try {
+    sessionStorage.setItem(STORAGE_KEY, val ? 'true' : 'false');
+  } catch { /* quota */ }
+});
+
+function toggle() {
+  collapsed.value = !collapsed.value;
+}
+
+function formatNumber(v) {
+  if (v == null || v === 0) return '0';
+  return Number(v).toLocaleString();
+}
+</script>
+
+<template>
+  <section class="summary-panel">
+    <div class="summary-header" @click="toggle">
+      <h3 class="summary-title">分析摘要</h3>
+      <span class="summary-toggle">{{ collapsed ? '▸ 展開' : '▾ 收起' }}</span>
+    </div>
+
+    <div v-show="!collapsed" class="summary-body">
+      <div class="summary-grid">
+        <!-- Query context -->
+        <div class="summary-block">
+          <h4 class="block-title">查詢條件</h4>
+          <ul class="block-list">
+            <li>偵測站：{{ stationLabel }}</li>
+            <template v-if="queryParams.queryMode === 'container'">
+              <li>輸入方式：{{ queryParams.containerInputType === 'lot' ? 'LOT ID' : queryParams.containerInputType }}</li>
+              <li v-if="queryParams.resolvedCount != null">解析數量：{{ queryParams.resolvedCount }} 筆</li>
+              <li v-if="queryParams.notFoundCount > 0">未找到：{{ queryParams.notFoundCount }} 筆</li>
+            </template>
+            <template v-else>
+              <li>日期範圍：{{ queryParams.startDate }} ~ {{ queryParams.endDate }}</li>
+            </template>
+            <li>不良原因：{{ queryParams.lossReasons?.length ? queryParams.lossReasons.join(', ') : '全部' }}</li>
+          </ul>
+        </div>
+
+        <!-- Data scope -->
+        <div class="summary-block">
+          <h4 class="block-title">數據範圍</h4>
+          <ul class="block-list">
+            <li>偵測站 LOT 總數：{{ formatNumber(kpi.lot_count) }}</li>
+            <li>總投入：{{ formatNumber(kpi.total_input) }} pcs</li>
+            <li>報廢 LOT 數：{{ formatNumber(kpi.defective_lot_count) }}</li>
+            <li>報廢總數：{{ formatNumber(kpi.total_defect_qty) }} pcs</li>
+            <li>血緣追溯涵蓋上游 LOT：{{ formatNumber(totalAncestorCount) }}</li>
+          </ul>
+        </div>
+
+        <!-- Methodology -->
+        <div class="summary-block summary-block-wide">
+          <h4 class="block-title">歸因方法說明</h4>
+          <p class="block-text">
+            分析涵蓋所有經過偵測站的 LOT（包含無不良者），針對每筆 LOT 回溯血緣（split / merge chain）找到關聯的上游因子。
+            歸因不良率 = 關聯 LOT 的報廢合計 / 關聯 LOT 的投入合計 × 100%。
+            同一筆不良可歸因於多個上游因子（非互斥）。
+            柏拉圖柱高 = 歸因不良數（含重疊），橙線 = 歸因不良率。
+          </p>
+        </div>
+      </div>
+    </div>
+  </section>
+</template>
+
+<style scoped>
+.summary-panel {
+  border: 1px solid var(--border-color, #e5e7eb);
+  border-radius: 8px;
+  background: var(--bg-secondary, #f9fafb);
+  margin-bottom: 16px;
+}
+.summary-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 10px 16px;
+  cursor: pointer;
+  user-select: none;
+}
+.summary-title {
+  font-size: 14px;
+  font-weight: 600;
+  margin: 0;
+  color: var(--text-primary, #1f2937);
+}
+.summary-toggle {
+  font-size: 12px;
+  color: var(--text-tertiary, #9ca3af);
+}
+.summary-body {
+  padding: 0 16px 14px;
+}
+.summary-grid {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 12px 24px;
+}
+.summary-block {
+  min-width: 0;
+}
+.summary-block-wide {
+  grid-column: 1 / -1;
+}
+.block-title {
+  font-size: 12px;
+  font-weight: 600;
+  color: var(--text-secondary, #6b7280);
+  margin: 0 0 6px;
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+}
+.block-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  font-size: 13px;
+  color: var(--text-primary, #374151);
+  line-height: 1.7;
+}
+.block-text {
+  font-size: 12px;
+  color: var(--text-secondary, #6b7280);
+  line-height: 1.6;
+  margin: 0;
+}
+</style>
--- a/frontend/src/mid-section-defect/components/DetailTable.vue
+++ b/frontend/src/mid-section-defect/components/DetailTable.vue
@@ -20,6 +20,10 @@ const props = defineProps({
    type: String,
    default: 'backward',
  },
+  suspectMachines: {
+    type: Array,
+    default: () => [],
+  },
 });

 const emit = defineEmits(['export-csv', 'prev-page', 'next-page']);
@@ -38,7 +42,8 @@ const COLUMNS_BACKWARD = [
  { key: 'DEFECT_QTY', label: '不良數', width: '70px', numeric: true },
  { key: 'DEFECT_RATE', label: '不良率(%)', width: '90px', numeric: true },
  { key: 'ANCESTOR_COUNT', label: '上游LOT數', width: '80px', numeric: true },
-  { key: 'UPSTREAM_MACHINES', label: '上游機台', width: '200px' },
+  { key: 'UPSTREAM_MACHINE_COUNT', label: '上游台數', width: '80px', numeric: true },
+  { key: 'SUSPECT_HITS', label: '嫌疑命中', width: '200px', custom: true },
 ];

 const COLUMNS_FORWARD = [
@@ -103,6 +108,27 @@ function formatCell(value, col) {
  if (col.numeric) return Number(value).toLocaleString();
  return value;
 }
+
+function getSuspectHits(row) {
+  const upstreamMachines = row.UPSTREAM_MACHINES;
+  if (!Array.isArray(upstreamMachines) || upstreamMachines.length === 0) return null;
+  const suspects = props.suspectMachines;
+  if (!suspects || suspects.length === 0) return null;
+
+  const suspectSet = new Set(suspects);
+  const machineNames = upstreamMachines.map((m) => m.machine || m);
+  const uniqueNames = [...new Set(machineNames)];
+  const hits = uniqueNames.filter((name) => suspectSet.has(name));
+
+  if (hits.length === 0) return null;
+
+  return {
+    hitNames: hits,
+    hitCount: hits.length,
+    totalCount: uniqueNames.length,
+    fullMatch: hits.length === uniqueNames.length,
+  };
+}
 </script>

 <template>
@@ -136,7 +162,14 @@ function formatCell(value, col) {
          <tbody>
            <tr v-for="(row, idx) in sortedData" :key="idx">
              <td v-for="col in activeColumns" :key="col.key" :class="{ numeric: col.numeric }">
-                {{ formatCell(row[col.key], col) }}
+                <template v-if="col.key === 'SUSPECT_HITS'">
+                  <span v-if="getSuspectHits(row)" :class="{ 'hit-full': getSuspectHits(row).fullMatch }" class="suspect-cell">
+                    {{ getSuspectHits(row).hitNames.join(', ') }}
+                    <span class="hit-ratio">({{ getSuspectHits(row).hitCount }}/{{ getSuspectHits(row).totalCount }})</span>
+                  </span>
+                  <span v-else class="no-hit">-</span>
+                </template>
+                <template v-else>{{ formatCell(row[col.key], col) }}</template>
              </td>
            </tr>
            <tr v-if="!sortedData.length">
@@ -156,3 +189,21 @@ function formatCell(value, col) {
    </div>
  </section>
 </template>
+
+<style scoped>
+.suspect-cell {
+  font-size: 12px;
+  color: var(--text-primary, #374151);
+}
+.hit-ratio {
+  color: var(--text-tertiary, #9ca3af);
+  margin-left: 4px;
+}
+.hit-full {
+  color: #059669;
+  font-weight: 600;
+}
+.no-hit {
+  color: var(--text-tertiary, #9ca3af);
+}
+</style>
--- a/frontend/src/mid-section-defect/components/ParetoChart.vue
+++ b/frontend/src/mid-section-defect/components/ParetoChart.vue
@@ -1,5 +1,5 @@
 <script setup>
-import { computed } from 'vue';
+import { computed, ref } from 'vue';
 import VChart from 'vue-echarts';
 import { use } from 'echarts/core';
 import { CanvasRenderer } from 'echarts/renderers';
@@ -7,10 +7,11 @@ import { BarChart, LineChart } from 'echarts/charts';
 import {
  GridComponent,
  LegendComponent,
+  MarkLineComponent,
  TooltipComponent,
 } from 'echarts/components';

-use([CanvasRenderer, BarChart, LineChart, GridComponent, LegendComponent, TooltipComponent]);
+use([CanvasRenderer, BarChart, LineChart, GridComponent, LegendComponent, MarkLineComponent, TooltipComponent]);

 const props = defineProps({
  title: {
@@ -21,15 +22,51 @@ const props = defineProps({
    type: Array,
    default: () => [],
  },
+  enableClick: {
+    type: Boolean,
+    default: false,
+  },
+});
+
+const emit = defineEmits(['bar-click']);
+
+// Sort toggle: 'qty' (default) or 'rate'
+const sortMode = ref('qty');
+
+function toggleSort() {
+  sortMode.value = sortMode.value === 'qty' ? 'rate' : 'qty';
+}
+
+const sortedData = computed(() => {
+  if (!props.data || !props.data.length) return [];
+  if (sortMode.value === 'qty') return props.data;
+
+  // Re-sort by defect_rate and recalculate cumulative %
+  const sorted = [...props.data].sort((a, b) => (b.defect_rate || 0) - (a.defect_rate || 0));
+  const totalDefects = sorted.reduce((s, d) => s + (d.defect_qty || 0), 0);
+  let cumsum = 0;
+  return sorted.map((item) => {
+    cumsum += item.defect_qty || 0;
+    return {
+      ...item,
+      cumulative_pct: totalDefects > 0 ? Math.round((cumsum / totalDefects) * 1e4) / 100 : 0,
+    };
+  });
+});
+
+const totalLotCount = computed(() => {
+  if (!props.data || !props.data.length) return 0;
+  return props.data.reduce((s, d) => s + (d.lot_count || 0), 0);
 });

 const chartOption = computed(() => {
-  if (!props.data || !props.data.length) return null;
+  const data = sortedData.value;
+  if (!data.length) return null;

-  const names = props.data.map((d) => d.name);
-  const defectQty = props.data.map((d) => d.defect_qty);
-  const cumulativePct = props.data.map((d) => d.cumulative_pct);
-  const defectRate = props.data.map((d) => d.defect_rate);
+  const names = data.map((d) => d.name);
+  const defectQty = data.map((d) => d.defect_qty);
+  const cumulativePct = data.map((d) => d.cumulative_pct);
+  const defectRate = data.map((d) => d.defect_rate);

  return {
    animationDuration: 350,
@@ -39,11 +76,16 @@ const chartOption = computed(() => {
      formatter(params) {
        const idx = params[0]?.dataIndex;
        if (idx == null) return '';
-        const item = props.data[idx];
+        const item = data[idx];
+        const total = totalLotCount.value;
        let html = `<b>${item.name}</b><br/>`;
        html += `不良數: ${(item.defect_qty || 0).toLocaleString()}<br/>`;
        html += `投入數: ${(item.input_qty || 0).toLocaleString()}<br/>`;
        html += `不良率: ${(item.defect_rate || 0).toFixed(2)}%<br/>`;
+        if (item.lot_count != null) {
+          const pct = total > 0 ? ((item.lot_count / total) * 100).toFixed(1) : '0.0';
+          html += `關聯 LOT 數: ${item.lot_count} (${pct}%)<br/>`;
+        }
        html += `累計占比: ${(item.cumulative_pct || 0).toFixed(1)}%`;
        return html;
      },
@@ -112,16 +154,38 @@ const chartOption = computed(() => {
        symbolSize: 6,
        lineStyle: { color: '#ef4444', width: 2, type: 'dashed' },
        itemStyle: { color: '#ef4444' },
+        markLine: {
+          silent: true,
+          symbol: 'none',
+          label: { show: true, position: 'insideEndTop', formatter: '80%', fontSize: 10, color: '#94a3b8' },
+          lineStyle: { color: '#94a3b8', type: 'dotted', width: 1 },
+          data: [{ yAxis: 80 }],
+        },
      },
    ],
  };
 });
+
+function handleChartClick(params) {
+  if (!props.enableClick) return;
+  if (params.componentType === 'series' && params.seriesType === 'bar') {
+    emit('bar-click', { name: params.name, dataIndex: params.dataIndex });
+  }
+}
 </script>

 <template>
  <div class="chart-card">
    <div class="chart-header">
      <h3 class="chart-title">{{ title }}</h3>
+      <button
+        type="button"
+        class="sort-toggle"
+        :title="sortMode === 'qty' ? '切換為依不良率排序' : '切換為依不良數排序'"
+        @click="toggleSort"
+      >
+        {{ sortMode === 'qty' ? '依數量' : '依比率' }}
+      </button>
      <slot name="header-extra" />
    </div>
    <VChart
@@ -129,7 +193,25 @@ const chartOption = computed(() => {
      class="chart-canvas"
      :option="chartOption"
      autoresize
+      @click="handleChartClick"
    />
    <div v-else class="chart-empty">暫無資料</div>
  </div>
 </template>
+
+<style scoped>
+.sort-toggle {
+  font-size: 11px;
+  padding: 1px 6px;
+  border: 1px solid var(--border-color, #d1d5db);
+  border-radius: 4px;
+  background: var(--bg-secondary, #f9fafb);
+  color: var(--text-secondary, #6b7280);
+  cursor: pointer;
+  margin-left: 6px;
+  white-space: nowrap;
+}
+.sort-toggle:hover {
+  background: var(--bg-tertiary, #f3f4f6);
+}
+</style>
--- a/frontend/src/mid-section-defect/components/SuspectContextPanel.vue
+++ b/frontend/src/mid-section-defect/components/SuspectContextPanel.vue
@@ -0,0 +1,214 @@
+<script setup>
+import { ref, watch, onMounted, onBeforeUnmount } from 'vue';
+
+import { apiGet } from '../../core/api.js';
+
+const props = defineProps({
+  machine: {
+    type: Object,
+    default: null,
+  },
+});
+
+const emit = defineEmits(['close']);
+
+const jobsLoading = ref(false);
+const jobs = ref([]);
+const jobsError = ref('');
+
+watch(() => props.machine, async (val) => {
+  jobs.value = [];
+  jobsError.value = '';
+  if (!val?.EQUIPMENT_ID) return;
+  jobsLoading.value = true;
+  try {
+    const result = await apiGet(`/api/query-tool/equipment-recent-jobs/${encodeURIComponent(val.EQUIPMENT_ID)}`);
+    if (Array.isArray(result?.data)) {
+      jobs.value = result.data;
+    }
+  } catch (err) {
+    jobsError.value = err.message || '載入維修紀錄失敗';
+  } finally {
+    jobsLoading.value = false;
+  }
+}, { immediate: true });
+
+function handleOutsideClick(e) {
+  const el = e.target.closest('.suspect-panel');
+  if (!el) emit('close');
+}
+
+onMounted(() => {
+  setTimeout(() => document.addEventListener('click', handleOutsideClick), 0);
+});
+onBeforeUnmount(() => {
+  document.removeEventListener('click', handleOutsideClick);
+});
+
+function formatDate(v) {
+  if (!v) return '-';
+  return String(v).slice(0, 16).replace('T', ' ');
+}
+
+function formatNumber(v) {
+  if (v == null) return '0';
+  return Number(v).toLocaleString();
+}
+</script>
+
+<template>
+  <div v-if="machine" class="suspect-panel" @click.stop>
+    <div class="panel-header">
+      <h4 class="panel-title">{{ machine.EQUIPMENT_NAME }}</h4>
+      <button type="button" class="panel-close" @click="emit('close')">&times;</button>
+    </div>
+
+    <div class="panel-section">
+      <h5 class="section-label">歸因摘要</h5>
+      <table class="attr-table">
+        <tbody>
+          <tr><td class="attr-key">站點</td><td>{{ machine.WORKCENTER_GROUP || '-' }}</td></tr>
+          <tr><td class="attr-key">機型</td><td>{{ machine.RESOURCEFAMILYNAME || '-' }}</td></tr>
+          <tr><td class="attr-key">歸因不良率</td><td>{{ machine.DEFECT_RATE != null ? Number(machine.DEFECT_RATE).toFixed(2) + '%' : '-' }}</td></tr>
+          <tr><td class="attr-key">歸因不良數</td><td>{{ formatNumber(machine.DEFECT_QTY) }}</td></tr>
+          <tr><td class="attr-key">歸因投入數</td><td>{{ formatNumber(machine.INPUT_QTY) }}</td></tr>
+          <tr><td class="attr-key">關聯 LOT 數</td><td>{{ formatNumber(machine.DETECTION_LOT_COUNT) }}</td></tr>
+        </tbody>
+      </table>
+    </div>
+
+    <div class="panel-section">
+      <h5 class="section-label">近期維修紀錄</h5>
+      <div v-if="jobsLoading" class="jobs-loading">載入中...</div>
+      <div v-else-if="jobsError" class="jobs-error">{{ jobsError }}</div>
+      <div v-else-if="jobs.length === 0" class="jobs-empty">近 30 天無維修紀錄</div>
+      <table v-else class="jobs-table">
+        <thead>
+          <tr>
+            <th>JOB ID</th>
+            <th>狀態</th>
+            <th>型號</th>
+            <th>維修區間</th>
+          </tr>
+        </thead>
+        <tbody>
+          <tr v-for="job in jobs" :key="job.JOBID">
+            <td>{{ job.JOBID || '-' }}</td>
+            <td>{{ job.JOBSTATUS || '-' }}</td>
+            <td>{{ job.JOBMODELNAME || '-' }}</td>
+            <td class="job-interval">
+              <span>{{ formatDate(job.CREATEDATE) }}</span>
+              <span v-if="job.COMPLETEDATE" class="interval-sep">→</span>
+              <span v-if="job.COMPLETEDATE">{{ formatDate(job.COMPLETEDATE) }}</span>
+              <span v-else class="interval-ongoing">進行中</span>
+            </td>
+          </tr>
+        </tbody>
+      </table>
+    </div>
+  </div>
+</template>
+
+<style scoped>
+.suspect-panel {
+  position: absolute;
+  top: 0;
+  right: -320px;
+  width: 300px;
+  background: var(--bg-primary, #fff);
+  border: 1px solid var(--border-color, #e5e7eb);
+  border-radius: 8px;
+  box-shadow: 0 4px 16px rgba(0, 0, 0, 0.12);
+  z-index: 100;
+  font-size: 13px;
+}
+.panel-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 10px 14px;
+  border-bottom: 1px solid var(--border-color, #e5e7eb);
+}
+.panel-title {
+  margin: 0;
+  font-size: 14px;
+  font-weight: 600;
+}
+.panel-close {
+  background: none;
+  border: none;
+  font-size: 18px;
+  cursor: pointer;
+  color: var(--text-tertiary, #9ca3af);
+  padding: 0 4px;
+  line-height: 1;
+}
+.panel-section {
+  padding: 10px 14px;
+}
+.panel-section + .panel-section {
+  border-top: 1px solid var(--border-color, #f3f4f6);
+}
+.section-label {
+  font-size: 11px;
+  font-weight: 600;
+  color: var(--text-secondary, #6b7280);
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+  margin: 0 0 8px;
+}
+.attr-table {
+  width: 100%;
+  border-collapse: collapse;
+}
+.attr-table td {
+  padding: 3px 0;
+  line-height: 1.4;
+}
+.attr-key {
+  color: var(--text-secondary, #6b7280);
+  width: 90px;
+  white-space: nowrap;
+}
+.jobs-table {
+  width: 100%;
+  border-collapse: collapse;
+  font-size: 12px;
+}
+.jobs-table th {
+  text-align: left;
+  font-weight: 600;
+  color: var(--text-secondary, #6b7280);
+  padding: 4px 6px 4px 0;
+  border-bottom: 1px solid var(--border-color, #e5e7eb);
+  font-size: 11px;
+}
+.jobs-table td {
+  padding: 4px 6px 4px 0;
+  border-bottom: 1px solid var(--border-color, #f3f4f6);
+}
+.job-interval {
+  font-size: 11px;
+  line-height: 1.5;
+}
+.interval-sep {
+  margin: 0 2px;
+  color: var(--text-tertiary, #9ca3af);
+}
+.interval-ongoing {
+  color: #f59e0b;
+  font-size: 10px;
+  font-weight: 500;
+}
+.jobs-loading,
+.jobs-empty {
+  color: var(--text-tertiary, #9ca3af);
+  font-size: 12px;
+  padding: 4px 0;
+}
+.jobs-error {
+  color: #ef4444;
+  font-size: 12px;
+  padding: 4px 0;
+}
+</style>
--- a/frontend/src/mid-section-defect/style.css
+++ b/frontend/src/mid-section-defect/style.css
@@ -463,6 +463,10 @@ body {
  grid-template-columns: 1fr;
 }

+.chart-with-panel {
+  position: relative;
+}
+
 .chart-card {
  background: var(--msd-card-bg);
  border-radius: 10px;
--- a/frontend/src/reject-history/App.vue
+++ b/frontend/src/reject-history/App.vue
@@ -62,6 +62,9 @@ const page = ref(1);
 const detailReason = ref('');
 const selectedTrendDates = ref([]);
 const trendLegendSelected = ref({ '扣帳報廢量': true, '不扣帳報廢量': true });
+const paretoDimension = ref('reason');
+const dimensionParetoItems = ref([]);
+const dimensionParetoLoading = ref(false);

 // ---- Data state ----
 const summary = ref({
@@ -197,6 +200,8 @@ async function executePrimaryQuery() {
    page.value = 1;
    detailReason.value = '';
    selectedTrendDates.value = [];
+    paretoDimension.value = 'reason';
+    dimensionParetoItems.value = [];

    // Apply initial data
    analyticsRawItems.value = Array.isArray(result.analytics_raw)
@@ -301,6 +306,7 @@ function onTrendDateClick(dateStr) {
  }
  page.value = 1;
  void refreshView();
+  refreshDimensionParetoIfActive();
 }

 function onTrendLegendChange(selected) {
@@ -308,6 +314,7 @@ function onTrendLegendChange(selected) {
  page.value = 1;
  updateUrlState();
  void refreshView();
+  refreshDimensionParetoIfActive();
 }

 function onParetoClick(reason) {
@@ -323,6 +330,59 @@ function handleParetoScopeToggle(checked) {
  updateUrlState();
 }

+let activeDimRequestId = 0;
+
+async function fetchDimensionPareto(dim) {
+  if (dim === 'reason' || !queryId.value) return;
+  activeDimRequestId += 1;
+  const myId = activeDimRequestId;
+  dimensionParetoLoading.value = true;
+  try {
+    const params = {
+      query_id: queryId.value,
+      start_date: committedPrimary.startDate,
+      end_date: committedPrimary.endDate,
+      dimension: dim,
+      metric_mode: paretoMetricMode.value === 'defect' ? 'defect' : 'reject_total',
+      pareto_scope: committedPrimary.paretoTop80 ? 'top80' : 'all',
+      include_excluded_scrap: committedPrimary.includeExcludedScrap,
+      exclude_material_scrap: committedPrimary.excludeMaterialScrap,
+      exclude_pb_diode: committedPrimary.excludePbDiode,
+      packages: supplementaryFilters.packages.length > 0 ? supplementaryFilters.packages : undefined,
+      workcenter_groups: supplementaryFilters.workcenterGroups.length > 0 ? supplementaryFilters.workcenterGroups : undefined,
+      reason: supplementaryFilters.reason || undefined,
+      trend_dates: selectedTrendDates.value.length > 0 ? selectedTrendDates.value : undefined,
+    };
+    const resp = await apiGet('/api/reject-history/reason-pareto', { params, timeout: API_TIMEOUT });
+    if (myId !== activeDimRequestId) return;
+    const result = unwrapApiResult(resp, '查詢維度 Pareto 失敗');
+    dimensionParetoItems.value = result.data?.items || [];
+  } catch (err) {
+    if (myId !== activeDimRequestId) return;
+    dimensionParetoItems.value = [];
+    errorMessage.value = err.message || '查詢維度 Pareto 失敗';
+  } finally {
+    if (myId === activeDimRequestId) {
+      dimensionParetoLoading.value = false;
+    }
+  }
+}
+
+function refreshDimensionParetoIfActive() {
+  if (paretoDimension.value !== 'reason') {
+    void fetchDimensionPareto(paretoDimension.value);
+  }
+}
+
+function onDimensionChange(dim) {
+  paretoDimension.value = dim;
+  if (dim === 'reason') {
+    dimensionParetoItems.value = [];
+  } else {
+    void fetchDimensionPareto(dim);
+  }
+}
+
 function onSupplementaryChange(filters) {
  supplementaryFilters.packages = filters.packages || [];
  supplementaryFilters.workcenterGroups = filters.workcenterGroups || [];
@@ -331,6 +391,7 @@ function onSupplementaryChange(filters) {
  detailReason.value = '';
  selectedTrendDates.value = [];
  void refreshView();
+  refreshDimensionParetoIfActive();
 }

 function removeFilterChip(chip) {
@@ -347,6 +408,7 @@ function removeFilterChip(chip) {
    selectedTrendDates.value = [];
    page.value = 1;
    void refreshView();
+    refreshDimensionParetoIfActive();
    return;
  }

@@ -354,6 +416,7 @@ function removeFilterChip(chip) {
    supplementaryFilters.reason = '';
    page.value = 1;
    void refreshView();
+    refreshDimensionParetoIfActive();
    return;
  }

@@ -363,6 +426,7 @@ function removeFilterChip(chip) {
    );
    page.value = 1;
    void refreshView();
+    refreshDimensionParetoIfActive();
    return;
  }

@@ -372,6 +436,7 @@ function removeFilterChip(chip) {
    );
    page.value = 1;
    void refreshView();
+    refreshDimensionParetoIfActive();
    return;
  }
 }
@@ -556,6 +621,11 @@ const filteredParetoItems = computed(() => {
  return items.slice(0, Math.max(top80Count, Math.min(5, items.length)));
 });

+const activeParetoItems = computed(() => {
+  if (paretoDimension.value !== 'reason') return dimensionParetoItems.value;
+  return filteredParetoItems.value;
+});
+
 const activeFilterChips = computed(() => {
  const chips = [];

@@ -871,12 +941,15 @@ onMounted(() => {
      />

      <ParetoSection
-        :items="filteredParetoItems"
+        :items="activeParetoItems"
        :detail-reason="detailReason"
        :selected-dates="selectedTrendDates"
        :metric-label="paretoMetricLabel"
-        :loading="loading.querying"
+        :loading="loading.querying || dimensionParetoLoading"
+        :dimension="paretoDimension"
+        :show-dimension-selector="committedPrimary.mode === 'date_range'"
        @reason-click="onParetoClick"
+        @dimension-change="onDimensionChange"
      />

      <DetailTable
--- a/frontend/src/reject-history/components/DetailTable.vue
+++ b/frontend/src/reject-history/components/DetailTable.vue
@@ -41,6 +41,7 @@ function formatNumber(value) {
            <th>Package</th>
            <th>FUNCTION</th>
            <th class="col-left">TYPE</th>
+            <th>WORKFLOW</th>
            <th>PRODUCT</th>
            <th>原因</th>
            <th>EQUIPMENT</th>
@@ -66,6 +67,7 @@ function formatNumber(value) {
            <td>{{ row.PRODUCTLINENAME }}</td>
            <td>{{ row.PJ_FUNCTION || '' }}</td>
            <td class="col-left">{{ row.PJ_TYPE }}</td>
+            <td>{{ row.WORKFLOWNAME || '' }}</td>
            <td>{{ row.PRODUCTNAME || '' }}</td>
            <td>{{ row.LOSSREASONNAME }}</td>
            <td>{{ row.EQUIPMENTNAME || '' }}</td>
@@ -82,7 +84,7 @@ function formatNumber(value) {
            <td class="cell-nowrap">{{ row.TXN_TIME || row.TXN_DAY }}</td>
          </tr>
          <tr v-if="!items || items.length === 0">
-            <td :colspan="showRejectBreakdown ? 17 : 12" class="placeholder">No data</td>
+            <td :colspan="showRejectBreakdown ? 18 : 13" class="placeholder">No data</td>
          </tr>
        </tbody>
      </table>
--- a/frontend/src/reject-history/components/ParetoSection.vue
+++ b/frontend/src/reject-history/components/ParetoSection.vue
@@ -9,18 +9,34 @@ import VChart from 'vue-echarts';

 use([CanvasRenderer, BarChart, LineChart, GridComponent, TooltipComponent, LegendComponent]);

+const DIMENSION_OPTIONS = [
+  { value: 'reason', label: '不良原因' },
+  { value: 'package', label: 'PACKAGE' },
+  { value: 'type', label: 'TYPE' },
+  { value: 'workflow', label: 'WORKFLOW' },
+  { value: 'workcenter', label: '站點' },
+  { value: 'equipment', label: '機台' },
+];
+
 const props = defineProps({
  items: { type: Array, default: () => [] },
  detailReason: { type: String, default: '' },
  selectedDates: { type: Array, default: () => [] },
  metricLabel: { type: String, default: '報廢量' },
  loading: { type: Boolean, default: false },
+  dimension: { type: String, default: 'reason' },
+  showDimensionSelector: { type: Boolean, default: false },
 });

-const emit = defineEmits(['reason-click']);
+const emit = defineEmits(['reason-click', 'dimension-change']);

 const hasData = computed(() => Array.isArray(props.items) && props.items.length > 0);

+const dimensionLabel = computed(() => {
+  const opt = DIMENSION_OPTIONS.find((o) => o.value === props.dimension);
+  return opt ? opt.label : '報廢原因';
+});
+
 function formatNumber(value) {
  return Number(value || 0).toLocaleString('zh-TW');
 }
@@ -108,7 +124,7 @@ const chartOption = computed(() => {
 });

 function handleChartClick(params) {
-  if (params?.seriesType !== 'bar') {
+  if (params?.seriesType !== 'bar' || props.dimension !== 'reason') {
    return;
  }
  const reason = props.items?.[params.dataIndex]?.reason;
@@ -122,9 +138,17 @@ function handleChartClick(params) {
  <section class="card">
    <div class="card-header pareto-header">
      <div class="card-title">
-        {{ metricLabel }} vs 報廢原因（Pareto）
+        {{ metricLabel }} vs {{ dimensionLabel }}（Pareto）
        <span v-for="d in selectedDates" :key="d" class="pareto-date-badge">{{ d }}</span>
      </div>
+      <select
+        v-if="showDimensionSelector"
+        class="dimension-select"
+        :value="dimension"
+        @change="emit('dimension-change', $event.target.value)"
+      >
+        <option v-for="opt in DIMENSION_OPTIONS" :key="opt.value" :value="opt.value">{{ opt.label }}</option>
+      </select>
    </div>
    <div class="card-body pareto-layout">
      <div class="pareto-chart-wrap">
@@ -135,7 +159,7 @@ function handleChartClick(params) {
        <table class="detail-table pareto-table">
          <thead>
            <tr>
-              <th>原因</th>
+              <th>{{ dimensionLabel }}</th>
              <th>{{ metricLabel }}</th>
              <th>占比</th>
              <th>累積</th>
@@ -148,9 +172,10 @@ function handleChartClick(params) {
              :class="{ active: detailReason === item.reason }"
            >
              <td>
-                <button class="reason-link" type="button" @click="$emit('reason-click', item.reason)">
+                <button v-if="dimension === 'reason'" class="reason-link" type="button" @click="$emit('reason-click', item.reason)">
                  {{ item.reason }}
                </button>
+                <span v-else>{{ item.reason }}</span>
              </td>
              <td>{{ formatNumber(item.metric_value) }}</td>
              <td>{{ formatPct(item.pct) }}</td>
--- a/frontend/src/reject-history/style.css
+++ b/frontend/src/reject-history/style.css
@@ -228,16 +228,27 @@
  line-height: 1;
 }

-.btn-export {
+.btn.btn-primary:hover {
+  background: var(--primary-dark);
+  color: #fff;
+}
+
+.btn.btn-secondary:hover {
+  background: #5a6268;
+  color: #fff;
+}
+
+.btn.btn-export {
  background: #0f766e;
  color: #fff;
 }

-.btn-export:hover {
+.btn.btn-export:hover {
  background: #0b5e59;
+  color: #fff;
 }

-.btn-export:disabled {
+.btn.btn-export:disabled {
  opacity: 0.7;
  cursor: not-allowed;
 }
@@ -296,6 +307,17 @@
  gap: 12px;
 }

+.dimension-select {
+  font-size: 12px;
+  padding: 3px 8px;
+  border: 1px solid var(--border-color, #d1d5db);
+  border-radius: 4px;
+  background: var(--bg-primary, #fff);
+  color: var(--text-primary, #374151);
+  cursor: pointer;
+  margin-left: auto;
+}
+
 .pareto-date-badge {
  display: inline-block;
  margin-left: 8px;
--- a/frontend/src/shared-composables/useTraceProgress.js
+++ b/frontend/src/shared-composables/useTraceProgress.js
@@ -7,7 +7,7 @@ ensureMesApiAvailable();
 const DEFAULT_STAGE_TIMEOUT_MS = 60000;
 const PROFILE_DOMAINS = Object.freeze({
  query_tool: ['history', 'materials', 'rejects', 'holds', 'jobs'],
-  mid_section_defect: ['upstream_history'],
+  mid_section_defect: ['upstream_history', 'materials'],
  mid_section_defect_forward: ['upstream_history', 'downstream_rejects'],
 });

@@ -168,6 +168,7 @@ export function useTraceProgress({ profile } = {}) {
          lineage: {
            ancestors: lineagePayload?.ancestors || {},
            children_map: lineagePayload?.children_map || {},
+            seed_roots: lineagePayload?.seed_roots || {},
          },
        },
        { timeout: DEFAULT_STAGE_TIMEOUT_MS, signal: controller.signal },
--- a/src/mes_dashboard/routes/query_tool_routes.py
+++ b/src/mes_dashboard/routes/query_tool_routes.py
@@ -596,6 +596,49 @@ def get_workcenter_groups_list():
        return jsonify({'error': f'載入站點群組失敗: {str(exc)}'}), 500


+# ============================================================
+# Equipment Recent Jobs API (for suspect context panel)
+# ============================================================
+
+@query_tool_bp.route('/api/query-tool/equipment-recent-jobs/<equipment_id>', methods=['GET'])
+def get_equipment_recent_jobs(equipment_id):
+    """Get recent JOB records for a specific equipment (last 30 days, top 5).
+
+    Used by the suspect machine context panel in mid-section-defect analysis.
+    """
+    from mes_dashboard.sql import SQLLoader
+    from mes_dashboard.core.database import read_sql_df
+
+    equipment_id = str(equipment_id or '').strip()
+    if not equipment_id:
+        return jsonify({'error': '請指定設備ID'}), 400
+
+    try:
+        sql = SQLLoader.load("query_tool/equipment_recent_jobs")
+        df = read_sql_df(sql, {'equipment_id': equipment_id})
+
+        if df is None or df.empty:
+            return jsonify({'data': [], 'total': 0})
+
+        data = []
+        for _, row in df.iterrows():
+            data.append({
+                'JOBID': str(row.get('JOBID') or ''),
+                'JOBSTATUS': str(row.get('JOBSTATUS') or ''),
+                'JOBMODELNAME': str(row.get('JOBMODELNAME') or ''),
+                'CREATEDATE': str(row.get('CREATEDATE') or ''),
+                'COMPLETEDATE': str(row.get('COMPLETEDATE') or ''),
+                'CAUSECODENAME': str(row.get('CAUSECODENAME') or ''),
+                'REPAIRCODENAME': str(row.get('REPAIRCODENAME') or ''),
+                'RESOURCENAME': str(row.get('RESOURCENAME') or ''),
+            })
+
+        return jsonify({'data': data, 'total': len(data)})
+
+    except Exception as exc:
+        return jsonify({'error': f'載入維修紀錄失敗: {str(exc)}'}), 500
+
+
 # ============================================================
 # CSV Export API
 # ============================================================
--- a/src/mes_dashboard/routes/reject_history_routes.py
+++ b/src/mes_dashboard/routes/reject_history_routes.py
@@ -13,6 +13,7 @@ from mes_dashboard.core.cache import cache_get, cache_set, make_cache_key
 from mes_dashboard.core.rate_limit import configured_rate_limit
 from mes_dashboard.services.reject_dataset_cache import (
    apply_view,
+    compute_dimension_pareto,
    execute_primary_query,
    export_csv_from_cache,
 )
@@ -22,6 +23,7 @@ from mes_dashboard.services.reject_history_service import (
    get_filter_options,
    query_analytics,
    query_list,
+    query_dimension_pareto,
    query_reason_pareto,
    query_summary,
    query_trend,
@@ -298,11 +300,30 @@ def api_reject_history_reason_pareto():

    metric_mode = request.args.get("metric_mode", "reject_total").strip().lower() or "reject_total"
    pareto_scope = request.args.get("pareto_scope", "top80").strip().lower() or "top80"
+    dimension = request.args.get("dimension", "reason").strip().lower() or "reason"
+    query_id = request.args.get("query_id", "").strip()

    try:
-        result = query_reason_pareto(
+        # Prefer cache-based computation when query_id is available
+        if query_id:
+            result = compute_dimension_pareto(
+                query_id=query_id,
+                dimension=dimension,
+                metric_mode=metric_mode,
+                pareto_scope=pareto_scope,
+                packages=_parse_multi_param("packages") or None,
+                workcenter_groups=_parse_multi_param("workcenter_groups") or None,
+                reason=request.args.get("reason", "").strip() or None,
+                trend_dates=_parse_multi_param("trend_dates") or None,
+            )
+            if result is not None:
+                return jsonify({"success": True, "data": result, "meta": {}})
+            # Cache expired, fall through to Oracle query
+
+        result = query_dimension_pareto(
            start_date=start_date,
            end_date=end_date,
+            dimension=dimension,
            metric_mode=metric_mode,
            pareto_scope=pareto_scope,
            workcenter_groups=_parse_multi_param("workcenter_groups") or None,
--- a/src/mes_dashboard/routes/trace_routes.py
+++ b/src/mes_dashboard/routes/trace_routes.py
@@ -121,6 +121,11 @@ def _hash_payload(payload: Any) -> str:


 def _seed_cache_key(profile: str, params: Dict[str, Any]) -> str:
+    if profile == PROFILE_MID_SECTION_DEFECT:
+        # loss_reasons does not affect seed resolution; exclude it so that
+        # changing the reason filter hits the cache instead of re-querying Oracle.
+        filtered = {k: v for k, v in params.items() if k != "loss_reasons"}
+        return f"trace:seed:{profile}:{_hash_payload(filtered)}"
    return f"trace:seed:{profile}:{_hash_payload(params)}"


@@ -305,6 +310,7 @@ def _build_lineage_response(
    merge_edges: Optional[Dict[str, List[str]]] = None,
    typed_nodes: Optional[Dict[str, Dict[str, Any]]] = None,
    typed_edges: Optional[List[Dict[str, Any]]] = None,
+    seed_roots: Optional[Dict[str, str]] = None,
 ) -> Dict[str, Any]:
    normalized_ancestors: Dict[str, List[str]] = {}
    all_nodes = set(container_ids)
@@ -319,12 +325,20 @@ def _build_lineage_response(
        normalized_ancestors[seed] = normalized_list
        all_nodes.update(normalized_list)

+    # Count unique ancestor CIDs excluding seeds themselves
+    seed_set = set(container_ids)
+    ancestor_only = all_nodes - seed_set
+    total_ancestor_count = len(ancestor_only)
+
    response: Dict[str, Any] = {
        "stage": "lineage",
        "ancestors": normalized_ancestors,
        "merges": {},
        "total_nodes": len(all_nodes),
+        "total_ancestor_count": total_ancestor_count,
    }
+    if seed_roots:
+        response["seed_roots"] = seed_roots
    if cid_to_name:
        response["names"] = {
            cid: name for cid, name in cid_to_name.items()
@@ -386,6 +400,19 @@ def _parse_lineage_payload(payload: Dict[str, Any]) -> Optional[Dict[str, Any]]:
    return None


+def _parse_lineage_roots(payload: Dict[str, Any]) -> Optional[Dict[str, str]]:
+    """Extract seed_roots mapping from lineage payload."""
+    lineage = payload.get("lineage")
+    if isinstance(lineage, dict):
+        roots = lineage.get("seed_roots")
+        if isinstance(roots, dict):
+            return roots
+    direct_roots = payload.get("seed_roots")
+    if isinstance(direct_roots, dict):
+        return direct_roots
+    return None
+
+
 def _build_msd_aggregation(
    payload: Dict[str, Any],
    domain_results: Dict[str, Dict[str, List[Dict[str, Any]]]],
@@ -407,11 +434,13 @@ def _build_msd_aggregation(
    loss_reasons = parse_loss_reasons_param(raw_loss_reasons)

    lineage_ancestors = _parse_lineage_payload(payload)
+    lineage_roots = _parse_lineage_roots(payload)
    seed_container_ids = _normalize_strings(payload.get("seed_container_ids", []))
    if not seed_container_ids and isinstance(lineage_ancestors, dict):
        seed_container_ids = _normalize_strings(list(lineage_ancestors.keys()))

    upstream_events = domain_results.get("upstream_history", {})
+    materials_events = domain_results.get("materials", {})
    downstream_events = domain_results.get("downstream_rejects", {})
    station = str(params.get("station") or "測試").strip()
    direction = str(params.get("direction") or "backward").strip()
@@ -422,7 +451,9 @@ def _build_msd_aggregation(
        loss_reasons=loss_reasons,
        seed_container_ids=seed_container_ids,
        lineage_ancestors=lineage_ancestors,
+        lineage_roots=lineage_roots,
        upstream_events_by_cid=upstream_events,
+        materials_events_by_cid=materials_events,
        downstream_events_by_cid=downstream_events,
        station=station,
        direction=direction,
@@ -534,6 +565,7 @@ def lineage():
                merge_edges=reverse_graph.get("merge_edges"),
                typed_nodes=reverse_graph.get("nodes"),
                typed_edges=reverse_graph.get("edges"),
+                seed_roots=reverse_graph.get("seed_roots"),
            )
            response["roots"] = list(container_ids)
        else:
@@ -589,10 +621,16 @@ def events():
            400,
        )

+    # For MSD profile, skip the events-level cache so that aggregation is
+    # always recomputed with the current loss_reasons.  EventFetcher still
+    # provides per-domain Redis caching, so raw Oracle queries are avoided.
+    is_msd = (profile == PROFILE_MID_SECTION_DEFECT)
+
    events_cache_key = _events_cache_key(profile, domains, container_ids)
-    cached = cache_get(events_cache_key)
-    if cached is not None:
-        return jsonify(cached)
+    if not is_msd:
+        cached = cache_get(events_cache_key)
+        if cached is not None:
+            return jsonify(cached)

    logger.info(
        "trace events profile=%s domains=%s cid_count=%s correlation_cache_key=%s",
@@ -645,5 +683,6 @@ def events():
        response["code"] = "EVENTS_PARTIAL_FAILURE"
        response["failed_domains"] = sorted(failed_domains)

-    cache_set(events_cache_key, response, ttl=TRACE_CACHE_TTL_SECONDS)
+    if not is_msd:
+        cache_set(events_cache_key, response, ttl=TRACE_CACHE_TTL_SECONDS)
    return jsonify(response)
--- a/src/mes_dashboard/services/lineage_engine.py
+++ b/src/mes_dashboard/services/lineage_engine.py
@@ -801,6 +801,7 @@ class LineageEngine:
            all_nodes.add(child)

        recomputed_ancestors: Dict[str, Set[str]] = {}
+        recomputed_roots: Dict[str, str] = {}
        for seed in seed_cids:
            visited: Set[str] = set()
            stack = list(pm.get(seed, []))
@@ -816,6 +817,15 @@ class LineageEngine:
                    if gp and gp not in visited:
                        stack.append(gp)
            recomputed_ancestors[seed] = visited
+            # Root = ancestor with no further parents; if no ancestors, seed is its own root
+            if visited:
+                root_cid = next(
+                    (cid for cid in visited if not pm.get(cid)),
+                    next(iter(visited)),
+                )
+            else:
+                root_cid = seed
+            recomputed_roots[seed] = cid_to_name.get(root_cid, root_cid)

        typed_nodes = LineageEngine._build_nodes_payload(all_nodes, snapshots, cid_to_name, wafer_ids)
        typed_edges = _to_edge_payload(split_edges + merge_payload_edges + semantic_edges)
@@ -825,6 +835,7 @@ class LineageEngine:
            "cid_to_name": cid_to_name,
            "parent_map": pm,
            "merge_edges": me,
+            "seed_roots": recomputed_roots,
            "nodes": typed_nodes,
            "edges": typed_edges,
        }
--- a/src/mes_dashboard/services/mid_section_defect_service.py
+++ b/src/mes_dashboard/services/mid_section_defect_service.py
@@ -72,12 +72,9 @@ ANALYSIS_LOCK_POLL_INTERVAL_SECONDS = 0.5
 TOP_N = 10

 # Dimension column mapping for backward attribution charts
+# by_material and by_wafer_root are handled separately (different attribution source)
 DIMENSION_MAP = {
-    'by_station': 'WORKCENTER_GROUP',
    'by_machine': 'EQUIPMENT_NAME',
-    'by_workflow': 'WORKFLOW',
-    'by_package': 'PRODUCTLINENAME',
-    'by_pj_type': 'PJ_TYPE',
    'by_detection_machine': 'DETECTION_EQUIPMENTNAME',
 }

@@ -102,7 +99,10 @@ CSV_COLUMNS_BACKWARD = [
    ('DEFECT_QTY', '不良數'),
    ('DEFECT_RATE', '不良率(%)'),
    ('ANCESTOR_COUNT', '上游LOT數'),
+    ('UPSTREAM_MACHINE_COUNT', '上游台數'),
    ('UPSTREAM_MACHINES', '上游機台'),
+    ('UPSTREAM_MATERIALS', '上游原物料'),
+    ('WAFER_ROOT', '源頭批次'),
 ]

 # CSV export column config (forward)
@@ -296,7 +296,9 @@ def build_trace_aggregation_from_events(
    loss_reasons: Optional[List[str]] = None,
    seed_container_ids: Optional[List[str]] = None,
    lineage_ancestors: Optional[Dict[str, Any]] = None,
+    lineage_roots: Optional[Dict[str, str]] = None,
    upstream_events_by_cid: Optional[Dict[str, List[Dict[str, Any]]]] = None,
+    materials_events_by_cid: Optional[Dict[str, List[Dict[str, Any]]]] = None,
    downstream_events_by_cid: Optional[Dict[str, List[Dict[str, Any]]]] = None,
    station: str = '測試',
    direction: str = 'backward',
@@ -308,7 +310,9 @@ def build_trace_aggregation_from_events(
            loss_reasons=loss_reasons,
            seed_container_ids=seed_container_ids,
            lineage_ancestors=lineage_ancestors,
+            lineage_roots=lineage_roots,
            upstream_events_by_cid=upstream_events_by_cid,
+            materials_events_by_cid=materials_events_by_cid,
            downstream_events_by_cid=downstream_events_by_cid,
            station=station,
            direction=direction,
@@ -393,6 +397,7 @@ def build_trace_aggregation_from_events(
        fallback_seed_ids=list(detection_data.keys()),
    )
    normalized_upstream = _normalize_upstream_event_records(upstream_events_by_cid or {})
+    normalized_materials = _normalize_materials_event_records(materials_events_by_cid or {})

    attribution = _attribute_defects(
        detection_data,
@@ -400,11 +405,25 @@ def build_trace_aggregation_from_events(
        normalized_upstream,
        normalized_loss_reasons,
    )
-    detail = _build_detail_table(filtered_df, normalized_ancestors, normalized_upstream)
+    mat_attribution = _attribute_materials(
+        detection_data, normalized_ancestors, normalized_materials, normalized_loss_reasons,
+    )
+    root_attribution = _attribute_wafer_roots(
+        detection_data, lineage_roots or {}, normalized_loss_reasons,
+    )
+    detail = _build_detail_table(
+        filtered_df, normalized_ancestors, normalized_upstream,
+        materials_by_cid=normalized_materials,
+        roots=lineage_roots,
+    )

    return {
        'kpi': _build_kpi(filtered_df, attribution, normalized_loss_reasons),
-        'charts': _build_all_charts(attribution, detection_data),
+        'charts': _build_all_charts(
+            attribution, detection_data,
+            materials_attribution=mat_attribution,
+            wafer_root_attribution=root_attribution,
+        ),
        'daily_trend': _build_daily_trend(filtered_df, normalized_loss_reasons),
        'available_loss_reasons': available_loss_reasons,
        'genealogy_status': genealogy_status,
@@ -418,7 +437,9 @@ def _build_trace_aggregation_container_mode(
    loss_reasons: Optional[List[str]] = None,
    seed_container_ids: Optional[List[str]] = None,
    lineage_ancestors: Optional[Dict[str, Any]] = None,
+    lineage_roots: Optional[Dict[str, str]] = None,
    upstream_events_by_cid: Optional[Dict[str, List[Dict[str, Any]]]] = None,
+    materials_events_by_cid: Optional[Dict[str, List[Dict[str, Any]]]] = None,
    downstream_events_by_cid: Optional[Dict[str, List[Dict[str, Any]]]] = None,
    station: str = '測試',
    direction: str = 'backward',
@@ -512,6 +533,7 @@ def _build_trace_aggregation_container_mode(
        fallback_seed_ids=list(detection_data.keys()),
    )
    normalized_upstream = _normalize_upstream_event_records(upstream_events_by_cid or {})
+    normalized_materials = _normalize_materials_event_records(materials_events_by_cid or {})

    attribution = _attribute_defects(
        detection_data,
@@ -519,11 +541,25 @@ def _build_trace_aggregation_container_mode(
        normalized_upstream,
        normalized_loss_reasons,
    )
-    detail = _build_detail_table(filtered_df, normalized_ancestors, normalized_upstream)
+    mat_attribution = _attribute_materials(
+        detection_data, normalized_ancestors, normalized_materials, normalized_loss_reasons,
+    )
+    root_attribution = _attribute_wafer_roots(
+        detection_data, lineage_roots or {}, normalized_loss_reasons,
+    )
+    detail = _build_detail_table(
+        filtered_df, normalized_ancestors, normalized_upstream,
+        materials_by_cid=normalized_materials,
+        roots=lineage_roots,
+    )

    return {
        'kpi': _build_kpi(filtered_df, attribution, normalized_loss_reasons),
-        'charts': _build_all_charts(attribution, detection_data),
+        'charts': _build_all_charts(
+            attribution, detection_data,
+            materials_attribution=mat_attribution,
+            wafer_root_attribution=root_attribution,
+        ),
        'daily_trend': [],
        'available_loss_reasons': available_loss_reasons,
        'genealogy_status': genealogy_status,
@@ -635,7 +671,20 @@ def export_csv(
        return

    for row in result.get('detail', []):
-        writer.writerow([row.get(col, '') for col, _ in columns])
+        csv_row = []
+        for col, _ in columns:
+            value = row.get(col, '')
+            # Flatten structured list fields for CSV
+            if col == 'UPSTREAM_MACHINES' and isinstance(value, list):
+                value = ', '.join(
+                    f"{m.get('station', '')}/{m.get('machine', '')}" for m in value
+                )
+            elif col == 'UPSTREAM_MATERIALS' and isinstance(value, list):
+                value = ', '.join(
+                    f"{m.get('part', '')}/{m.get('lot', '')}" for m in value
+                )
+            csv_row.append(value)
+        writer.writerow(csv_row)
        yield output.getvalue()
        output.seek(0)
        output.truncate(0)
@@ -988,7 +1037,9 @@ def _run_backward_pipeline(
        'available_loss_reasons': available_loss_reasons,
        'charts': _build_all_charts(attribution, detection_data),
        'daily_trend': _build_daily_trend(filtered_df, loss_reasons),
-        'detail': _build_detail_table(filtered_df, ancestors, upstream_by_cid),
+        'detail': _build_detail_table(
+            filtered_df, ancestors, upstream_by_cid,
+        ),
        'genealogy_status': genealogy_status,
        'attribution': attribution,
    }
@@ -1263,6 +1314,29 @@ def _normalize_upstream_event_records(
    return dict(result)


+def _normalize_materials_event_records(
+    events_by_cid: Dict[str, List[Dict[str, Any]]],
+) -> Dict[str, List[Dict[str, Any]]]:
+    """Normalize EventFetcher materials payload into attribution-ready records."""
+    result: Dict[str, List[Dict[str, Any]]] = defaultdict(list)
+    for cid, events in events_by_cid.items():
+        cid_value = _safe_str(cid)
+        if not cid_value:
+            continue
+        for event in events:
+            part = _safe_str(event.get('MATERIALPARTNAME'))
+            if not part:
+                continue
+            result[cid_value].append({
+                'MATERIALPARTNAME': part,
+                'MATERIALLOTNAME': _safe_str(event.get('MATERIALLOTNAME')),
+                'QTYCONSUMED': _safe_int(event.get('QTYCONSUMED')),
+                'WORKCENTERNAME': _safe_str(event.get('WORKCENTERNAME')),
+                'EQUIPMENTNAME': _safe_str(event.get('EQUIPMENTNAME')),
+            })
+    return dict(result)
+
+
 def _normalize_downstream_event_records(
    events_by_cid: Dict[str, List[Dict[str, Any]]],
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -1441,6 +1515,119 @@ def _attribute_defects(
    return attribution


+def _attribute_materials(
+    detection_data: Dict[str, Dict[str, Any]],
+    ancestors: Dict[str, Set[str]],
+    materials_by_cid: Dict[str, List[Dict[str, Any]]],
+    loss_reasons: Optional[List[str]] = None,
+) -> List[Dict[str, Any]]:
+    """Attribute detection station defects to upstream materials.
+
+    Symmetric to ``_attribute_defects`` but keyed by
+    ``(MATERIALPARTNAME, MATERIALLOTNAME)``.
+    """
+    material_to_detection: Dict[Tuple[str, str], Set[str]] = defaultdict(set)
+
+    for det_cid, data in detection_data.items():
+        ancestor_set = ancestors.get(det_cid, set())
+        all_cids = ancestor_set | {det_cid}
+
+        for anc_cid in all_cids:
+            for record in materials_by_cid.get(anc_cid, []):
+                part = _safe_str(record.get('MATERIALPARTNAME') or record.get('material_part_name'))
+                lot = _safe_str(record.get('MATERIALLOTNAME') or record.get('material_lot_name'))
+                if not part:
+                    continue
+                material_key = (part, lot) if lot else (part, '')
+                material_to_detection[material_key].add(det_cid)
+
+    attribution = []
+    for material_key, det_lot_set in material_to_detection.items():
+        part_name, lot_name = material_key
+
+        total_trackinqty = sum(
+            detection_data[cid]['trackinqty'] for cid in det_lot_set
+            if cid in detection_data
+        )
+
+        total_rejectqty = 0
+        for cid in det_lot_set:
+            if cid not in detection_data:
+                continue
+            by_reason = detection_data[cid]['rejectqty_by_reason']
+            if loss_reasons:
+                for reason in loss_reasons:
+                    total_rejectqty += by_reason.get(reason, 0)
+            else:
+                total_rejectqty += sum(by_reason.values())
+
+        rate = round(total_rejectqty / total_trackinqty * 100, 4) if total_trackinqty else 0.0
+
+        display_name = f"{part_name} ({lot_name})" if lot_name else part_name
+        attribution.append({
+            'MATERIAL_KEY': display_name,
+            'MATERIAL_PART_NAME': part_name,
+            'MATERIAL_LOT_NAME': lot_name,
+            'DETECTION_LOT_COUNT': len(det_lot_set),
+            'INPUT_QTY': total_trackinqty,
+            'DEFECT_QTY': total_rejectqty,
+            'DEFECT_RATE': rate,
+        })
+
+    attribution.sort(key=lambda x: x['DEFECT_RATE'], reverse=True)
+    return attribution
+
+
+def _attribute_wafer_roots(
+    detection_data: Dict[str, Dict[str, Any]],
+    roots: Dict[str, str],
+    loss_reasons: Optional[List[str]] = None,
+) -> List[Dict[str, Any]]:
+    """Attribute detection station defects to wafer root ancestors.
+
+    ``roots`` maps seed container_id → root container_name.
+    """
+    root_to_detection: Dict[str, Set[str]] = defaultdict(set)
+
+    for det_cid in detection_data:
+        root_name = roots.get(det_cid)
+        if not root_name:
+            # Self-root: use the lot's own container name
+            root_name = detection_data[det_cid].get('containername', det_cid)
+        root_to_detection[root_name].add(det_cid)
+
+    attribution = []
+    for root_name, det_lot_set in root_to_detection.items():
+        total_trackinqty = sum(
+            detection_data[cid]['trackinqty'] for cid in det_lot_set
+            if cid in detection_data
+        )
+
+        total_rejectqty = 0
+        for cid in det_lot_set:
+            if cid not in detection_data:
+                continue
+            by_reason = detection_data[cid]['rejectqty_by_reason']
+            if loss_reasons:
+                for reason in loss_reasons:
+                    total_rejectqty += by_reason.get(reason, 0)
+            else:
+                total_rejectqty += sum(by_reason.values())
+
+        rate = round(total_rejectqty / total_trackinqty * 100, 4) if total_trackinqty else 0.0
+
+        attribution.append({
+            'ROOT_CONTAINER_NAME': root_name,
+            'DETECTION_LOT_COUNT': len(det_lot_set),
+            'INPUT_QTY': total_trackinqty,
+            'DEFECT_QTY': total_rejectqty,
+            'DEFECT_RATE': rate,
+        })
+
+    attribution.sort(key=lambda x: x['DEFECT_RATE'], reverse=True)
+    return attribution
+
+
 # ============================================================
 # Forward Defect Attribution Engine
 # ============================================================
@@ -1563,9 +1750,13 @@ def _build_kpi(

    affected_machines = sum(1 for a in attribution if a['DEFECT_QTY'] > 0)

+    # Count LOTs that have at least one defect matching selected reasons
+    defective_lot_count = int(defect_rows['CONTAINERID'].nunique()) if not defect_rows.empty else 0
+
    return {
        'total_input': total_input,
        'lot_count': lot_count,
+        'defective_lot_count': defective_lot_count,
        'total_defect_qty': total_defect_qty,
        'total_defect_rate': total_defect_rate,
        'top_loss_reason': top_reason,
@@ -1707,12 +1898,27 @@ def _build_loss_reason_chart(
 def _build_all_charts(
    attribution: List[Dict[str, Any]],
    detection_data: Dict[str, Dict[str, Any]],
+    *,
+    materials_attribution: Optional[List[Dict[str, Any]]] = None,
+    wafer_root_attribution: Optional[List[Dict[str, Any]]] = None,
 ) -> Dict[str, List[Dict]]:
    """Build chart data for all backward dimensions."""
    charts = {}
    for key, dim_col in DIMENSION_MAP.items():
        charts[key] = _build_chart_data(attribution, dim_col)

+    # Materials attribution chart (keyed by MATERIAL_KEY)
+    if materials_attribution:
+        charts['by_material'] = _build_chart_data(materials_attribution, 'MATERIAL_KEY')
+    else:
+        charts['by_material'] = []
+
+    # Wafer root attribution chart (keyed by ROOT_CONTAINER_NAME)
+    if wafer_root_attribution:
+        charts['by_wafer_root'] = _build_chart_data(wafer_root_attribution, 'ROOT_CONTAINER_NAME')
+    else:
+        charts['by_wafer_root'] = []
+
    loss_rows = []
    for cid, data in detection_data.items():
        trackinqty = data['trackinqty']
@@ -1913,8 +2119,11 @@ def _build_detail_table(
    df: pd.DataFrame,
    ancestors: Dict[str, Set[str]],
    upstream_by_cid: Dict[str, List[Dict[str, Any]]],
+    *,
+    materials_by_cid: Optional[Dict[str, List[Dict[str, Any]]]] = None,
+    roots: Optional[Dict[str, str]] = None,
 ) -> List[Dict[str, Any]]:
-    """Build LOT-level detail table with upstream machine info (backward)."""
+    """Build LOT-level detail table with structured upstream info (backward)."""
    if df.empty:
        return []

@@ -1935,6 +2144,9 @@ def _build_detail_table(
        if reason and qty > 0:
            lot_defects[cid][reason] += qty

+    materials_by_cid = materials_by_cid or {}
+    roots = roots or {}
+
    result = []
    for _, row in lots.iterrows():
        cid = row['CONTAINERID']
@@ -1942,43 +2154,65 @@ def _build_detail_table(
        ancestor_set = ancestors.get(cid, set())
        all_cids = ancestor_set | {cid}

-        upstream_machines = set()
+        # Structured upstream machines
+        seen_machines: Set[Tuple[str, str]] = set()
+        upstream_machines_list: List[Dict[str, str]] = []
        for anc_cid in all_cids:
            for rec in upstream_by_cid.get(anc_cid, []):
-                upstream_machines.add(f"{rec['workcenter_group']}/{rec['equipment_name']}")
+                pair = (rec['workcenter_group'], rec['equipment_name'])
+                if pair not in seen_machines:
+                    seen_machines.add(pair)
+                    upstream_machines_list.append({
+                        'station': rec['workcenter_group'],
+                        'machine': rec['equipment_name'],
+                    })
+
+        # Structured upstream materials
+        seen_materials: Set[Tuple[str, str]] = set()
+        upstream_materials_list: List[Dict[str, str]] = []
+        for anc_cid in all_cids:
+            for rec in materials_by_cid.get(anc_cid, []):
+                part = rec.get('MATERIALPARTNAME', '')
+                lot_name = rec.get('MATERIALLOTNAME', '')
+                pair = (part, lot_name)
+                if pair not in seen_materials and part:
+                    seen_materials.add(pair)
+                    upstream_materials_list.append({'part': part, 'lot': lot_name})
+
+        # Wafer root
+        wafer_root = roots.get(cid, '')
+
+        base = {
+            'CONTAINERNAME': _safe_str(row.get('CONTAINERNAME')),
+            'PJ_TYPE': _safe_str(row.get('PJ_TYPE')),
+            'PRODUCTLINENAME': _safe_str(row.get('PRODUCTLINENAME')),
+            'WORKFLOW': _safe_str(row.get('WORKFLOW')),
+            'FINISHEDRUNCARD': _safe_str(row.get('FINISHEDRUNCARD')),
+            'DETECTION_EQUIPMENTNAME': _safe_str(row.get('DETECTION_EQUIPMENTNAME')),
+            'INPUT_QTY': input_qty,
+            'ANCESTOR_COUNT': len(ancestor_set),
+            'UPSTREAM_MACHINES': upstream_machines_list,
+            'UPSTREAM_MACHINE_COUNT': len(seen_machines),
+            'UPSTREAM_MATERIALS': upstream_materials_list,
+            'WAFER_ROOT': wafer_root,
+        }

        reasons = lot_defects.get(cid, {})
        if reasons:
            for reason, qty in sorted(reasons.items()):
                rate = round(qty / input_qty * 100, 4) if input_qty else 0.0
                result.append({
-                    'CONTAINERNAME': _safe_str(row.get('CONTAINERNAME')),
-                    'PJ_TYPE': _safe_str(row.get('PJ_TYPE')),
-                    'PRODUCTLINENAME': _safe_str(row.get('PRODUCTLINENAME')),
-                    'WORKFLOW': _safe_str(row.get('WORKFLOW')),
-                    'FINISHEDRUNCARD': _safe_str(row.get('FINISHEDRUNCARD')),
-                    'DETECTION_EQUIPMENTNAME': _safe_str(row.get('DETECTION_EQUIPMENTNAME')),
-                    'INPUT_QTY': input_qty,
+                    **base,
                    'LOSS_REASON': reason,
                    'DEFECT_QTY': qty,
                    'DEFECT_RATE': rate,
-                    'ANCESTOR_COUNT': len(ancestor_set),
-                    'UPSTREAM_MACHINES': ', '.join(sorted(upstream_machines)),
                })
        else:
            result.append({
-                'CONTAINERNAME': _safe_str(row.get('CONTAINERNAME')),
-                'PJ_TYPE': _safe_str(row.get('PJ_TYPE')),
-                'PRODUCTLINENAME': _safe_str(row.get('PRODUCTLINENAME')),
-                'WORKFLOW': _safe_str(row.get('WORKFLOW')),
-                'FINISHEDRUNCARD': _safe_str(row.get('FINISHEDRUNCARD')),
-                'DETECTION_EQUIPMENTNAME': _safe_str(row.get('DETECTION_EQUIPMENTNAME')),
-                'INPUT_QTY': input_qty,
+                **base,
                'LOSS_REASON': '',
                'DEFECT_QTY': 0,
                'DEFECT_RATE': 0.0,
-                'ANCESTOR_COUNT': len(ancestor_set),
-                'UPSTREAM_MACHINES': ', '.join(sorted(upstream_machines)),
            })

    return result
--- a/src/mes_dashboard/services/reject_dataset_cache.py
+++ b/src/mes_dashboard/services/reject_dataset_cache.py
@@ -570,6 +570,7 @@ def _paginate_detail(
                "WORKCENTER_GROUP": _normalize_text(row.get("WORKCENTER_GROUP")),
                "WORKCENTERNAME": _normalize_text(row.get("WORKCENTERNAME")),
                "SPECNAME": _normalize_text(row.get("SPECNAME")),
+                "WORKFLOWNAME": _normalize_text(row.get("WORKFLOWNAME")),
                "EQUIPMENTNAME": _normalize_text(row.get("EQUIPMENTNAME")),
                "PRODUCTLINENAME": _normalize_text(row.get("PRODUCTLINENAME")),
                "PJ_TYPE": _normalize_text(row.get("PJ_TYPE")),
@@ -622,6 +623,128 @@ def _extract_available_filters(df: pd.DataFrame) -> dict:
    }


+# ============================================================
+# Dimension Pareto from cache
+# ============================================================
+
+# Dimension → DF column mapping (matches _DIMENSION_COLUMN_MAP in reject_history_service)
+_DIM_TO_DF_COLUMN = {
+    "reason": "LOSSREASONNAME",
+    "package": "PRODUCTLINENAME",
+    "type": "PJ_TYPE",
+    "workflow": "WORKFLOWNAME",
+    "workcenter": "WORKCENTER_GROUP",
+    "equipment": "PRIMARY_EQUIPMENTNAME",
+}
+
+
+def compute_dimension_pareto(
+    *,
+    query_id: str,
+    dimension: str = "reason",
+    metric_mode: str = "reject_total",
+    pareto_scope: str = "top80",
+    packages: Optional[List[str]] = None,
+    workcenter_groups: Optional[List[str]] = None,
+    reason: Optional[str] = None,
+    trend_dates: Optional[List[str]] = None,
+) -> Optional[Dict[str, Any]]:
+    """Compute dimension pareto from cached DataFrame (no Oracle query)."""
+    df = _get_cached_df(query_id)
+    if df is None:
+        return None
+
+    dim_col = _DIM_TO_DF_COLUMN.get(dimension, "LOSSREASONNAME")
+    if dim_col not in df.columns:
+        return {"items": [], "dimension": dimension, "metric_mode": metric_mode}
+
+    # Apply supplementary filters
+    filtered = _apply_supplementary_filters(
+        df,
+        packages=packages,
+        workcenter_groups=workcenter_groups,
+        reason=reason,
+    )
+    if filtered is None or filtered.empty:
+        return {"items": [], "dimension": dimension, "metric_mode": metric_mode}
+
+    # Apply trend date filter
+    if trend_dates and "TXN_DAY" in filtered.columns:
+        date_set = set(trend_dates)
+        filtered = filtered[
+            filtered["TXN_DAY"].apply(lambda d: _to_date_str(d) in date_set)
+        ]
+        if filtered.empty:
+            return {"items": [], "dimension": dimension, "metric_mode": metric_mode}
+
+    # Determine metric column
+    if metric_mode == "defect":
+        metric_col = "DEFECT_QTY"
+    else:
+        metric_col = "REJECT_TOTAL_QTY"
+
+    if metric_col not in filtered.columns:
+        return {"items": [], "dimension": dimension, "metric_mode": metric_mode}
+
+    # Group by dimension
+    agg_dict = {}
+    for col in ["MOVEIN_QTY", "REJECT_TOTAL_QTY", "DEFECT_QTY"]:
+        if col in filtered.columns:
+            agg_dict[col] = (col, "sum")
+
+    grouped = filtered.groupby(dim_col, sort=False).agg(**agg_dict).reset_index()
+
+    # Count distinct lots
+    if "CONTAINERID" in filtered.columns:
+        lot_counts = (
+            filtered.groupby(dim_col)["CONTAINERID"]
+            .nunique()
+            .reset_index()
+            .rename(columns={"CONTAINERID": "AFFECTED_LOT_COUNT"})
+        )
+        grouped = grouped.merge(lot_counts, on=dim_col, how="left")
+    else:
+        grouped["AFFECTED_LOT_COUNT"] = 0
+
+    # Compute metric and sort
+    grouped["METRIC_VALUE"] = grouped[metric_col].fillna(0)
+    grouped = grouped[grouped["METRIC_VALUE"] > 0].sort_values(
+        "METRIC_VALUE", ascending=False
+    )
+    if grouped.empty:
+        return {"items": [], "dimension": dimension, "metric_mode": metric_mode}
+
+    total_metric = grouped["METRIC_VALUE"].sum()
+    grouped["PCT"] = (grouped["METRIC_VALUE"] / total_metric * 100).round(4)
+    grouped["CUM_PCT"] = grouped["PCT"].cumsum().round(4)
+
+    all_items = []
+    for _, row in grouped.iterrows():
+        all_items.append({
+            "reason": _normalize_text(row.get(dim_col)) or "(未知)",
+            "metric_value": _as_float(row.get("METRIC_VALUE")),
+            "MOVEIN_QTY": _as_int(row.get("MOVEIN_QTY")),
+            "REJECT_TOTAL_QTY": _as_int(row.get("REJECT_TOTAL_QTY")),
+            "DEFECT_QTY": _as_int(row.get("DEFECT_QTY")),
+            "count": _as_int(row.get("AFFECTED_LOT_COUNT")),
+            "pct": round(_as_float(row.get("PCT")), 4),
+            "cumPct": round(_as_float(row.get("CUM_PCT")), 4),
+        })
+
+    items = list(all_items)
+    if pareto_scope == "top80" and items:
+        top_items = [item for item in items if _as_float(item.get("cumPct")) <= 80.0]
+        if not top_items:
+            top_items = [items[0]]
+        items = top_items
+
+    return {
+        "items": items,
+        "dimension": dimension,
+        "metric_mode": metric_mode,
+    }
+
+
 # ============================================================
 # CSV export from cache
 # ============================================================
@@ -670,6 +793,7 @@ def export_csv_from_cache(
                "Package": _normalize_text(row.get("PRODUCTLINENAME")),
                "FUNCTION": _normalize_text(row.get("PJ_FUNCTION")),
                "TYPE": _normalize_text(row.get("PJ_TYPE")),
+                "WORKFLOW": _normalize_text(row.get("WORKFLOWNAME")),
                "PRODUCT": _normalize_text(row.get("PRODUCTNAME")),
                "原因": _normalize_text(row.get("LOSSREASONNAME")),
                "EQUIPMENT": _normalize_text(row.get("EQUIPMENTNAME")),
--- a/src/mes_dashboard/services/reject_history_service.py
+++ b/src/mes_dashboard/services/reject_history_service.py
@@ -289,6 +289,7 @@ def _prepare_sql(
    metric_column: str = "",
    base_variant: str = "",
    base_where: str = "",
+    dimension_column: str = "",
 ) -> str:
    sql = _load_sql(name)
    sql = sql.replace("{{ BASE_QUERY }}", _base_query_sql(base_variant))
@@ -297,6 +298,7 @@ def _prepare_sql(
    sql = sql.replace("{{ WHERE_CLAUSE }}", where_clause or "")
    sql = sql.replace("{{ BUCKET_EXPR }}", bucket_expr or "TRUNC(b.TXN_DAY)")
    sql = sql.replace("{{ METRIC_COLUMN }}", metric_column or "b.REJECT_TOTAL_QTY")
+    sql = sql.replace("{{ DIMENSION_COLUMN }}", dimension_column or "b.LOSSREASONNAME")
    return sql


@@ -579,6 +581,111 @@ def query_reason_pareto(
    }


+# Allowed dimension → SQL column mapping for dimension_pareto
+_DIMENSION_COLUMN_MAP = {
+    "reason": "b.LOSSREASONNAME",
+    "package": "b.PRODUCTLINENAME",
+    "type": "b.PJ_TYPE",
+    "workflow": "b.WORKFLOWNAME",
+    "workcenter": "b.WORKCENTER_GROUP",
+    "equipment": "b.PRIMARY_EQUIPMENTNAME",
+}
+
+
+def query_dimension_pareto(
+    *,
+    start_date: str,
+    end_date: str,
+    dimension: str = "reason",
+    metric_mode: str = "reject_total",
+    pareto_scope: str = "top80",
+    workcenter_groups: Optional[list[str]] = None,
+    packages: Optional[list[str]] = None,
+    reasons: Optional[list[str]] = None,
+    categories: Optional[list[str]] = None,
+    include_excluded_scrap: bool = False,
+    exclude_material_scrap: bool = True,
+    exclude_pb_diode: bool = True,
+) -> dict[str, Any]:
+    """Pareto chart grouped by an arbitrary dimension (reason, package, type, workcenter, equipment)."""
+    _validate_range(start_date, end_date)
+
+    normalized_dim = _normalize_text(dimension).lower() or "reason"
+    if normalized_dim not in _DIMENSION_COLUMN_MAP:
+        raise ValueError(f"Invalid dimension '{dimension}'. Use: {', '.join(_DIMENSION_COLUMN_MAP)}")
+
+    # For reason dimension, delegate to existing optimized function
+    if normalized_dim == "reason":
+        return query_reason_pareto(
+            start_date=start_date, end_date=end_date,
+            metric_mode=metric_mode, pareto_scope=pareto_scope,
+            workcenter_groups=workcenter_groups, packages=packages,
+            reasons=reasons, categories=categories,
+            include_excluded_scrap=include_excluded_scrap,
+            exclude_material_scrap=exclude_material_scrap,
+            exclude_pb_diode=exclude_pb_diode,
+        )
+
+    normalized_metric = _normalize_text(metric_mode).lower() or "reject_total"
+    if normalized_metric not in VALID_METRIC_MODE:
+        raise ValueError("Invalid metric_mode. Use reject_total or defect")
+
+    normalized_scope = _normalize_text(pareto_scope).lower() or "top80"
+    if normalized_scope not in {"top80", "all"}:
+        raise ValueError("Invalid pareto_scope. Use top80 or all")
+
+    dim_col = _DIMENSION_COLUMN_MAP[normalized_dim]
+
+    where_clause, params, meta = _build_where_clause(
+        workcenter_groups=workcenter_groups,
+        packages=packages,
+        reasons=reasons,
+        categories=categories,
+        include_excluded_scrap=include_excluded_scrap,
+        exclude_material_scrap=exclude_material_scrap,
+        exclude_pb_diode=exclude_pb_diode,
+    )
+    sql = _prepare_sql(
+        "dimension_pareto",
+        where_clause=where_clause,
+        metric_column=_metric_column(normalized_metric),
+        dimension_column=dim_col,
+    )
+    df = read_sql_df(sql, _common_params(start_date, end_date, params))
+    all_items = []
+    if df is not None and not df.empty:
+        for _, row in df.iterrows():
+            all_items.append({
+                "reason": _normalize_text(row.get("DIMENSION_VALUE")) or "(未知)",
+                "metric_value": _as_float(row.get("METRIC_VALUE")),
+                "MOVEIN_QTY": _as_int(row.get("MOVEIN_QTY")),
+                "REJECT_TOTAL_QTY": _as_int(row.get("REJECT_TOTAL_QTY")),
+                "DEFECT_QTY": _as_int(row.get("DEFECT_QTY")),
+                "count": _as_int(row.get("AFFECTED_LOT_COUNT")),
+                "pct": round(_as_float(row.get("PCT")), 4),
+                "cumPct": round(_as_float(row.get("CUM_PCT")), 4),
+            })
+
+    items = list(all_items)
+    if normalized_scope == "top80" and items:
+        top_items = [item for item in items if _as_float(item.get("cumPct")) <= 80.0]
+        if not top_items:
+            top_items = [items[0]]
+        items = top_items
+
+    return {
+        "items": items,
+        "dimension": normalized_dim,
+        "metric_mode": normalized_metric,
+        "pareto_scope": normalized_scope,
+        "meta": {
+            **meta,
+            "total_items_after_filter": len(all_items),
+            "displayed_items": len(items),
+        },
+    }
+
+
 def _apply_metric_filter(where_clause: str, metric_filter: str) -> str:
    """Append metric-type filter (reject / defect) to an existing WHERE clause."""
    if metric_filter == "reject":
@@ -648,6 +755,7 @@ def query_list(
                    "WORKCENTER_GROUP": _normalize_text(row.get("WORKCENTER_GROUP")),
                    "WORKCENTERNAME": _normalize_text(row.get("WORKCENTERNAME")),
                    "SPECNAME": _normalize_text(row.get("SPECNAME")),
+                    "WORKFLOWNAME": _normalize_text(row.get("WORKFLOWNAME")),
                    "EQUIPMENTNAME": _normalize_text(row.get("EQUIPMENTNAME")),
                    "PRODUCTLINENAME": _normalize_text(row.get("PRODUCTLINENAME")),
                    "PJ_TYPE": _normalize_text(row.get("PJ_TYPE")),
@@ -724,6 +832,7 @@ def export_csv(
                    "Package": _normalize_text(row.get("PRODUCTLINENAME")),
                    "FUNCTION": _normalize_text(row.get("PJ_FUNCTION")),
                    "TYPE": _normalize_text(row.get("PJ_TYPE")),
+                    "WORKFLOW": _normalize_text(row.get("WORKFLOWNAME")),
                    "PRODUCT": _normalize_text(row.get("PRODUCTNAME")),
                    "原因": _normalize_text(row.get("LOSSREASONNAME")),
                    "EQUIPMENT": _normalize_text(row.get("EQUIPMENTNAME")),
--- a/src/mes_dashboard/sql/query_tool/equipment_recent_jobs.sql
+++ b/src/mes_dashboard/sql/query_tool/equipment_recent_jobs.sql
@@ -0,0 +1,19 @@
+-- Recent JOB records for a specific equipment (last 30 days)
+--
+-- Parameters:
+--   :equipment_id - Equipment ID (RESOURCEID)
+
+SELECT
+    j.JOBID,
+    j.JOBSTATUS,
+    j.JOBMODELNAME,
+    j.CREATEDATE,
+    j.COMPLETEDATE,
+    j.CAUSECODENAME,
+    j.REPAIRCODENAME,
+    j.RESOURCENAME
+FROM DWH.DW_MES_JOB j
+WHERE j.RESOURCEID = :equipment_id
+  AND j.CREATEDATE >= SYSDATE - 30
+ORDER BY j.CREATEDATE DESC
+FETCH FIRST 5 ROWS ONLY
--- a/src/mes_dashboard/sql/reject_history/dimension_pareto.sql
+++ b/src/mes_dashboard/sql/reject_history/dimension_pareto.sql
@@ -0,0 +1,43 @@
+-- Reject History Dimension Pareto (generic dimension grouping)
+-- Template slots:
+--   BASE_WITH_CTE (base reject-history daily dataset SQL wrapped as CTE)
+--   METRIC_COLUMN (metric expression: b.REJECT_TOTAL_QTY or b.DEFECT_QTY)
+--   WHERE_CLAUSE (QueryBuilder-generated WHERE clause against alias b)
+--   DIMENSION_COLUMN (column to group by, e.g. b.PJ_TYPE, b.PRODUCTLINENAME)
+--   DIMENSION_ALIAS (output alias, e.g. DIMENSION_VALUE)
+
+{{ BASE_WITH_CTE }},
+dim_agg AS (
+    SELECT
+        {{ DIMENSION_COLUMN }} AS DIMENSION_VALUE,
+        SUM({{ METRIC_COLUMN }}) AS METRIC_VALUE,
+        SUM(b.MOVEIN_QTY) AS MOVEIN_QTY,
+        SUM(b.REJECT_TOTAL_QTY) AS REJECT_TOTAL_QTY,
+        SUM(b.DEFECT_QTY) AS DEFECT_QTY,
+        SUM(b.AFFECTED_LOT_COUNT) AS AFFECTED_LOT_COUNT
+    FROM base b
+    {{ WHERE_CLAUSE }}
+    GROUP BY
+        {{ DIMENSION_COLUMN }}
+    HAVING SUM({{ METRIC_COLUMN }}) > 0
+)
+SELECT
+    DIMENSION_VALUE,
+    METRIC_VALUE,
+    MOVEIN_QTY,
+    REJECT_TOTAL_QTY,
+    DEFECT_QTY,
+    AFFECTED_LOT_COUNT,
+    ROUND(
+        METRIC_VALUE * 100 / NULLIF(SUM(METRIC_VALUE) OVER (), 0),
+        4
+    ) AS PCT,
+    ROUND(
+        SUM(METRIC_VALUE) OVER (
+            ORDER BY METRIC_VALUE DESC, DIMENSION_VALUE
+            ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+        ) * 100 / NULLIF(SUM(METRIC_VALUE) OVER (), 0),
+        4
+    ) AS CUM_PCT
+FROM dim_agg
+ORDER BY METRIC_VALUE DESC, DIMENSION_VALUE
--- a/src/mes_dashboard/sql/reject_history/export.sql
+++ b/src/mes_dashboard/sql/reject_history/export.sql
@@ -11,6 +11,7 @@ SELECT
    b.WORKCENTER_GROUP,
    b.WORKCENTERNAME,
    b.SPECNAME,
+    b.WORKFLOWNAME,
    b.EQUIPMENTNAME,
    b.PRODUCTLINENAME,
    b.PJ_FUNCTION,
--- a/src/mes_dashboard/sql/reject_history/list.sql
+++ b/src/mes_dashboard/sql/reject_history/list.sql
@@ -30,6 +30,7 @@ SELECT
    p.WORKCENTERSEQUENCE_GROUP,
    p.WORKCENTERNAME,
    p.SPECNAME,
+    p.WORKFLOWNAME,
    p.EQUIPMENTNAME,
    p.PRIMARY_EQUIPMENTNAME,
    p.PRODUCTLINENAME,
--- a/src/mes_dashboard/sql/reject_history/performance_daily.sql
+++ b/src/mes_dashboard/sql/reject_history/performance_daily.sql
@@ -30,6 +30,17 @@ WITH spec_map AS (
    WHERE SPEC IS NOT NULL
    GROUP BY SPEC
 ),
+workflow_lookup AS (
+    SELECT /*+ MATERIALIZE */ DISTINCT w.CONTAINERID, w.WORKFLOWNAME
+    FROM DWH.DW_MES_WIP w
+    WHERE w.PRODUCTLINENAME <> '點測'
+      AND w.CONTAINERID IN (
+          SELECT DISTINCT r0.CONTAINERID
+          FROM DWH.DW_MES_LOTREJECTHISTORY r0
+          WHERE r0.TXNDATE >= TO_DATE(:start_date, 'YYYY-MM-DD')
+            AND r0.TXNDATE < TO_DATE(:end_date, 'YYYY-MM-DD') + 1
+      )
+),
 reject_raw AS (
    SELECT
        TRUNC(r.TXNDATE) AS TXN_DAY,
@@ -48,6 +59,7 @@ reject_raw AS (
            TRIM(REGEXP_SUBSTR(r.EQUIPMENTNAME, '[^,]+', 1, 1)),
            NVL(TRIM(r.EQUIPMENTNAME), '(NA)')
        ) AS PRIMARY_EQUIPMENTNAME,
+        NVL(TRIM(wf.WORKFLOWNAME), NVL(TRIM(r.SPECNAME), '(NA)')) AS WORKFLOWNAME,
        NVL(TRIM(r.LOSSREASONNAME), '(未填寫)') AS LOSSREASONNAME,
        NVL(
            TRIM(REGEXP_SUBSTR(NVL(TRIM(r.LOSSREASONNAME), '(未填寫)'), '^[^_[:space:]-]+')),
@@ -78,6 +90,8 @@ reject_raw AS (
      ON c.CONTAINERID = r.CONTAINERID
    LEFT JOIN spec_map sm
      ON sm.SPEC = TRIM(r.SPECNAME)
+    LEFT JOIN workflow_lookup wf
+      ON wf.CONTAINERID = r.CONTAINERID
    WHERE {{ BASE_WHERE }}
 ),
 daily_agg AS (
@@ -88,6 +102,7 @@ daily_agg AS (
        WORKCENTERSEQUENCE_GROUP,
        WORKCENTERNAME,
        SPECNAME,
+        WORKFLOWNAME,
        EQUIPMENTNAME,
        PRIMARY_EQUIPMENTNAME,
        PRODUCTLINENAME,
@@ -115,6 +130,7 @@ daily_agg AS (
        WORKCENTERSEQUENCE_GROUP,
        WORKCENTERNAME,
        SPECNAME,
+        WORKFLOWNAME,
        EQUIPMENTNAME,
        PRIMARY_EQUIPMENTNAME,
        PRODUCTLINENAME,
@@ -131,6 +147,7 @@ SELECT
    WORKCENTERSEQUENCE_GROUP,
    WORKCENTERNAME,
    SPECNAME,
+    WORKFLOWNAME,
    EQUIPMENTNAME,
    PRIMARY_EQUIPMENTNAME,
    PRODUCTLINENAME,
--- a/src/mes_dashboard/sql/reject_history/performance_daily_lot.sql
+++ b/src/mes_dashboard/sql/reject_history/performance_daily_lot.sql
@@ -19,6 +19,17 @@ WITH spec_map AS (
    WHERE SPEC IS NOT NULL
    GROUP BY SPEC
 ),
+workflow_lookup AS (
+    SELECT /*+ MATERIALIZE */ DISTINCT w.CONTAINERID, w.WORKFLOWNAME
+    FROM DWH.DW_MES_WIP w
+    WHERE w.PRODUCTLINENAME <> '點測'
+      AND w.CONTAINERID IN (
+          SELECT DISTINCT r0.CONTAINERID
+          FROM DWH.DW_MES_LOTREJECTHISTORY r0
+          WHERE r0.TXNDATE >= TO_DATE(:start_date, 'YYYY-MM-DD')
+            AND r0.TXNDATE < TO_DATE(:end_date, 'YYYY-MM-DD') + 1
+      )
+),
 reject_raw AS (
    SELECT
        r.TXNDATE,
@@ -41,6 +52,7 @@ reject_raw AS (
            TRIM(REGEXP_SUBSTR(r.EQUIPMENTNAME, '[^,]+', 1, 1)),
            NVL(TRIM(r.EQUIPMENTNAME), '(NA)')
        ) AS PRIMARY_EQUIPMENTNAME,
+        NVL(TRIM(wf.WORKFLOWNAME), NVL(TRIM(r.SPECNAME), '(NA)')) AS WORKFLOWNAME,
        NVL(TRIM(r.LOSSREASONNAME), '(未填寫)') AS LOSSREASONNAME,
        NVL(
            TRIM(REGEXP_SUBSTR(NVL(TRIM(r.LOSSREASONNAME), '(未填寫)'), '^[^_[:space:]-]+')),
@@ -72,6 +84,8 @@ reject_raw AS (
      ON c.CONTAINERID = r.CONTAINERID
    LEFT JOIN spec_map sm
      ON sm.SPEC = TRIM(r.SPECNAME)
+    LEFT JOIN workflow_lookup wf
+      ON wf.CONTAINERID = r.CONTAINERID
    WHERE {{ BASE_WHERE }}
 ),
 daily_agg AS (
@@ -86,6 +100,7 @@ daily_agg AS (
        WORKCENTERSEQUENCE_GROUP,
        WORKCENTERNAME,
        SPECNAME,
+        WORKFLOWNAME,
        EQUIPMENTNAME,
        PRIMARY_EQUIPMENTNAME,
        PRODUCTLINENAME,
@@ -118,6 +133,7 @@ daily_agg AS (
        WORKCENTERSEQUENCE_GROUP,
        WORKCENTERNAME,
        SPECNAME,
+        WORKFLOWNAME,
        EQUIPMENTNAME,
        PRIMARY_EQUIPMENTNAME,
        PRODUCTLINENAME,
@@ -139,6 +155,7 @@ SELECT
    WORKCENTERSEQUENCE_GROUP,
    WORKCENTERNAME,
    SPECNAME,
+    WORKFLOWNAME,
    EQUIPMENTNAME,
    PRIMARY_EQUIPMENTNAME,
    PRODUCTLINENAME,
--- a/tests/test_mid_section_defect_service.py
+++ b/tests/test_mid_section_defect_service.py
@@ -8,6 +8,8 @@ from unittest.mock import patch
 import pandas as pd

 from mes_dashboard.services.mid_section_defect_service import (
+    _attribute_materials,
+    _attribute_wafer_roots,
    build_trace_aggregation_from_events,
    query_analysis,
    query_analysis_detail,
@@ -251,3 +253,112 @@ def test_query_station_options_returns_ordered_list():
    assert result[0]['order'] == 0
    assert result[-1]['name'] == '測試'
    assert result[-1]['order'] == 11
+
+
+# --- _attribute_materials tests ---
+
+def _make_detection_data(entries):
+    """Helper: build detection_data dict from simplified entries."""
+    data = {}
+    for e in entries:
+        data[e['cid']] = {
+            'containername': e.get('name', e['cid']),
+            'trackinqty': e['trackinqty'],
+            'rejectqty_by_reason': e.get('reasons', {}),
+        }
+    return data
+
+
+def test_attribute_materials_basic_rate_calculation():
+    detection_data = _make_detection_data([
+        {'cid': 'C1', 'trackinqty': 100, 'reasons': {'R1': 5}},
+        {'cid': 'C2', 'trackinqty': 200, 'reasons': {'R1': 10}},
+    ])
+    ancestors = {'C1': {'A1'}, 'C2': {'A1'}}
+    materials_by_cid = {
+        'A1': [{'MATERIALPARTNAME': 'PART-A', 'MATERIALLOTNAME': 'LOT-X'}],
+    }
+
+    result = _attribute_materials(detection_data, ancestors, materials_by_cid)
+
+    assert len(result) == 1
+    assert result[0]['MATERIAL_KEY'] == 'PART-A (LOT-X)'
+    assert result[0]['INPUT_QTY'] == 300
+    assert result[0]['DEFECT_QTY'] == 15
+    assert abs(result[0]['DEFECT_RATE'] - 5.0) < 0.01
+    assert result[0]['DETECTION_LOT_COUNT'] == 2
+
+
+def test_attribute_materials_null_lot_name():
+    detection_data = _make_detection_data([
+        {'cid': 'C1', 'trackinqty': 100, 'reasons': {'R1': 3}},
+    ])
+    ancestors = {'C1': {'A1'}}
+    materials_by_cid = {
+        'A1': [{'MATERIALPARTNAME': 'PART-B', 'MATERIALLOTNAME': None}],
+    }
+
+    result = _attribute_materials(detection_data, ancestors, materials_by_cid)
+
+    assert len(result) == 1
+    assert result[0]['MATERIAL_KEY'] == 'PART-B'
+    assert result[0]['MATERIAL_LOT_NAME'] == ''
+
+
+def test_attribute_materials_with_loss_reason_filter():
+    detection_data = _make_detection_data([
+        {'cid': 'C1', 'trackinqty': 100, 'reasons': {'R1': 5, 'R2': 3}},
+    ])
+    ancestors = {'C1': {'A1'}}
+    materials_by_cid = {
+        'A1': [{'MATERIALPARTNAME': 'P', 'MATERIALLOTNAME': 'L'}],
+    }
+
+    result = _attribute_materials(detection_data, ancestors, materials_by_cid, loss_reasons=['R1'])
+
+    assert result[0]['DEFECT_QTY'] == 5
+
+
+# --- _attribute_wafer_roots tests ---
+
+def test_attribute_wafer_roots_basic():
+    detection_data = _make_detection_data([
+        {'cid': 'C1', 'name': 'LOT-1', 'trackinqty': 100, 'reasons': {'R1': 5}},
+        {'cid': 'C2', 'name': 'LOT-2', 'trackinqty': 200, 'reasons': {'R1': 10}},
+    ])
+    roots = {'C1': 'ROOT-A', 'C2': 'ROOT-A'}
+
+    result = _attribute_wafer_roots(detection_data, roots)
+
+    assert len(result) == 1
+    assert result[0]['ROOT_CONTAINER_NAME'] == 'ROOT-A'
+    assert result[0]['INPUT_QTY'] == 300
+    assert result[0]['DEFECT_QTY'] == 15
+
+
+def test_attribute_wafer_roots_self_root():
+    """LOTs with no root mapping should use their own container name."""
+    detection_data = _make_detection_data([
+        {'cid': 'C1', 'name': 'LOT-SELF', 'trackinqty': 100, 'reasons': {'R1': 2}},
+    ])
+    roots = {}  # No root for C1
+
+    result = _attribute_wafer_roots(detection_data, roots)
+
+    assert len(result) == 1
+    assert result[0]['ROOT_CONTAINER_NAME'] == 'LOT-SELF'
+
+
+def test_attribute_wafer_roots_multiple_roots():
+    detection_data = _make_detection_data([
+        {'cid': 'C1', 'name': 'L1', 'trackinqty': 100, 'reasons': {'R1': 5}},
+        {'cid': 'C2', 'name': 'L2', 'trackinqty': 200, 'reasons': {'R1': 20}},
+    ])
+    roots = {'C1': 'ROOT-A', 'C2': 'ROOT-B'}
+
+    result = _attribute_wafer_roots(detection_data, roots)
+
+    assert len(result) == 2
+    # Sorted by DEFECT_RATE desc
+    assert result[0]['ROOT_CONTAINER_NAME'] == 'ROOT-B'
+    assert result[1]['ROOT_CONTAINER_NAME'] == 'ROOT-A'
--- a/tests/test_query_tool_routes.py
+++ b/tests/test_query_tool_routes.py
@@ -1152,6 +1152,62 @@ class TestWorkcenterGroupsEndpoint:
        assert 'error' in data


+class TestEquipmentRecentJobsEndpoint:
+    """Tests for /api/query-tool/equipment-recent-jobs/<equipment_id> endpoint."""
+
+    @patch('mes_dashboard.core.database.read_sql_df')
+    @patch('mes_dashboard.sql.SQLLoader.load', return_value='SELECT 1')
+    def test_returns_recent_jobs(self, _mock_sql, mock_read_sql, client):
+        """Should return recent JOB records for given equipment."""
+        import pandas as pd
+        mock_read_sql.return_value = pd.DataFrame([
+            {
+                'JOBID': 'JOB-001',
+                'JOBSTATUS': 'Complete',
+                'JOBMODELNAME': 'MODEL-A',
+                'CREATEDATE': '2026-02-01 10:00:00',
+                'COMPLETEDATE': '2026-02-01 12:00:00',
+                'CAUSECODENAME': 'CAUSE-1',
+                'REPAIRCODENAME': 'REPAIR-1',
+                'RESOURCENAME': 'EQ-001',
+            },
+        ])
+
+        response = client.get('/api/query-tool/equipment-recent-jobs/EQ001')
+
+        assert response.status_code == 200
+        data = json.loads(response.data)
+        assert len(data['data']) == 1
+        assert data['data'][0]['JOBID'] == 'JOB-001'
+        assert data['total'] == 1
+
+    @patch('mes_dashboard.core.database.read_sql_df')
+    @patch('mes_dashboard.sql.SQLLoader.load', return_value='SELECT 1')
+    def test_returns_empty_when_no_jobs(self, _mock_sql, mock_read_sql, client):
+        """Should return empty list when no jobs found."""
+        import pandas as pd
+        mock_read_sql.return_value = pd.DataFrame()
+
+        response = client.get('/api/query-tool/equipment-recent-jobs/EQ002')
+
+        assert response.status_code == 200
+        data = json.loads(response.data)
+        assert data['data'] == []
+        assert data['total'] == 0
+
+    @patch('mes_dashboard.core.database.read_sql_df')
+    @patch('mes_dashboard.sql.SQLLoader.load', return_value='SELECT 1')
+    def test_handles_db_error(self, _mock_sql, mock_read_sql, client):
+        """Should return 500 on database error."""
+        mock_read_sql.side_effect = Exception('DB connection failed')
+
+        response = client.get('/api/query-tool/equipment-recent-jobs/EQ003')
+
+        assert response.status_code == 500
+        data = json.loads(response.data)
+        assert 'error' in data
+
+
 class TestLotHistoryWithWorkcenterFilter:
    """Tests for /api/query-tool/lot-history with workcenter filter."""

--- a/tests/test_reject_history_routes.py
+++ b/tests/test_reject_history_routes.py
@@ -166,7 +166,7 @@ class TestRejectHistoryApiRoutes(TestRejectHistoryRoutesBase):
        self.assertEqual(response.status_code, 400)
        self.assertFalse(payload['success'])

-    @patch('mes_dashboard.routes.reject_history_routes.query_reason_pareto')
+    @patch('mes_dashboard.routes.reject_history_routes.query_dimension_pareto')
    def test_reason_pareto_defaults_top80(self, mock_pareto):
        mock_pareto.return_value = {'items': [], 'metric_mode': 'reject_total', 'pareto_scope': 'top80', 'meta': {}}

@@ -176,6 +176,47 @@ class TestRejectHistoryApiRoutes(TestRejectHistoryRoutesBase):
        _, kwargs = mock_pareto.call_args
        self.assertEqual(kwargs['pareto_scope'], 'top80')
        self.assertEqual(kwargs['metric_mode'], 'reject_total')
+        self.assertEqual(kwargs['dimension'], 'reason')
+
+    @patch('mes_dashboard.routes.reject_history_routes.query_dimension_pareto')
+    def test_dimension_pareto_accepts_package(self, mock_pareto):
+        mock_pareto.return_value = {
+            'items': [{'reason': 'PKG-A', 'metric_value': 100, 'pct': 50, 'cumPct': 50}],
+            'dimension': 'package',
+            'metric_mode': 'reject_total',
+            'pareto_scope': 'all',
+            'meta': {},
+        }
+
+        response = self.client.get(
+            '/api/reject-history/reason-pareto?start_date=2026-02-01&end_date=2026-02-07&dimension=package&pareto_scope=all'
+        )
+        payload = json.loads(response.data)
+
+        self.assertEqual(response.status_code, 200)
+        self.assertTrue(payload['success'])
+        _, kwargs = mock_pareto.call_args
+        self.assertEqual(kwargs['dimension'], 'package')
+
+    @patch('mes_dashboard.routes.reject_history_routes.query_dimension_pareto')
+    def test_dimension_pareto_accepts_equipment(self, mock_pareto):
+        mock_pareto.return_value = {
+            'items': [{'reason': 'EQ-01', 'metric_value': 50, 'pct': 100, 'cumPct': 100}],
+            'dimension': 'equipment',
+            'metric_mode': 'reject_total',
+            'pareto_scope': 'top80',
+            'meta': {},
+        }
+
+        response = self.client.get(
+            '/api/reject-history/reason-pareto?start_date=2026-02-01&end_date=2026-02-07&dimension=equipment'
+        )
+        payload = json.loads(response.data)
+
+        self.assertEqual(response.status_code, 200)
+        self.assertTrue(payload['success'])
+        _, kwargs = mock_pareto.call_args
+        self.assertEqual(kwargs['dimension'], 'equipment')

    @patch('mes_dashboard.routes.reject_history_routes.query_list')
    @patch('mes_dashboard.core.rate_limit.check_and_record', return_value=(True, 6))
--- a/tests/test_trace_routes.py
+++ b/tests/test_trace_routes.py
@@ -9,7 +9,7 @@ import mes_dashboard.core.database as db
 from mes_dashboard.app import create_app
 from mes_dashboard.core.cache import NoOpCache
 from mes_dashboard.core.rate_limit import reset_rate_limits_for_tests
-from mes_dashboard.routes.trace_routes import _lineage_cache_key
+from mes_dashboard.routes.trace_routes import _lineage_cache_key, _seed_cache_key


 def _client():
@@ -380,3 +380,105 @@ def test_events_rate_limited_returns_429(_mock_rate_limit):
    assert response.headers.get('Retry-After') == '5'
    payload = response.get_json()
    assert payload['error']['code'] == 'TOO_MANY_REQUESTS'
+
+
+# ---- MSD cache isolation tests ----
+
+
+def test_msd_seed_cache_key_ignores_loss_reasons():
+    """Changing loss_reasons should not change the seed cache key for MSD."""
+    base_params = {
+        'start_date': '2025-01-01',
+        'end_date': '2025-01-31',
+        'station': '測試',
+        'direction': 'backward',
+    }
+    key_all = _seed_cache_key('mid_section_defect', {**base_params, 'loss_reasons': ['A', 'B', 'C']})
+    key_two = _seed_cache_key('mid_section_defect', {**base_params, 'loss_reasons': ['A']})
+    key_none = _seed_cache_key('mid_section_defect', base_params)
+
+    assert key_all == key_two == key_none
+
+
+def test_non_msd_seed_cache_key_includes_all_params():
+    """For non-MSD profiles the seed cache key should still hash all params."""
+    params_a = {'resolve_type': 'lot_id', 'values': ['LOT-001'], 'extra': 'x'}
+    params_b = {'resolve_type': 'lot_id', 'values': ['LOT-001'], 'extra': 'y'}
+
+    key_a = _seed_cache_key('query_tool', params_a)
+    key_b = _seed_cache_key('query_tool', params_b)
+    assert key_a != key_b
+
+
+@patch('mes_dashboard.routes.trace_routes.build_trace_aggregation_from_events')
+@patch('mes_dashboard.routes.trace_routes.EventFetcher.fetch_events')
+def test_msd_events_recomputes_aggregation_on_each_call(
+    mock_fetch_events,
+    mock_build_aggregation,
+):
+    """MSD events should NOT use events-level cache, so aggregation is always fresh."""
+    mock_fetch_events.return_value = {
+        'CID-001': [{'CONTAINERID': 'CID-001', 'WORKCENTER_GROUP': '測試'}]
+    }
+    mock_build_aggregation.return_value = {
+        'kpi': {'total_input': 100},
+        'charts': {},
+        'daily_trend': [],
+        'available_loss_reasons': [],
+        'genealogy_status': 'ready',
+        'detail_total_count': 0,
+    }
+
+    client = _client()
+    body = {
+        'profile': 'mid_section_defect',
+        'container_ids': ['CID-001'],
+        'domains': ['upstream_history'],
+        'params': {
+            'start_date': '2025-01-01',
+            'end_date': '2025-01-31',
+            'loss_reasons': ['Reason-A'],
+        },
+        'lineage': {'ancestors': {'CID-001': ['CID-A']}},
+        'seed_container_ids': ['CID-001'],
+    }
+
+    # First call
+    resp1 = client.post('/api/trace/events', json=body)
+    assert resp1.status_code == 200
+
+    # Second call with different loss_reasons — aggregation must be re-invoked
+    body['params']['loss_reasons'] = ['Reason-B']
+    resp2 = client.post('/api/trace/events', json=body)
+    assert resp2.status_code == 200
+
+    assert mock_build_aggregation.call_count == 2
+
+
+@patch('mes_dashboard.routes.trace_routes.EventFetcher.fetch_events')
+@patch('mes_dashboard.routes.trace_routes.cache_get')
+@patch('mes_dashboard.routes.trace_routes.cache_set')
+def test_non_msd_events_cache_unchanged(mock_cache_set, mock_cache_get, mock_fetch_events):
+    """Non-MSD profiles should still use events-level cache as before."""
+    cached_response = {
+        'stage': 'events',
+        'results': {'history': {'data': [], 'count': 0}},
+        'aggregation': None,
+    }
+    mock_cache_get.return_value = cached_response
+
+    client = _client()
+    response = client.post(
+        '/api/trace/events',
+        json={
+            'profile': 'query_tool',
+            'container_ids': ['CID-001'],
+            'domains': ['history'],
+        },
+    )
+
+    assert response.status_code == 200
+    payload = response.get_json()
+    assert payload['stage'] == 'events'
+    # EventFetcher should NOT have been called — served from cache
+    mock_fetch_events.assert_not_called()