MySTDF · Root Cause Intelligent Engine
From test data to evidence, decision, and action.
MySTDF combines interactive engineering analysis, AI-assisted root-cause intelligence, adaptive learning, and automated reporting in one local-first workflow.
- Site 4 distribution shiftHigh
- Continuity OS tailMedium
- Edge-ring wafer patternMedium
Data to insight
Tester → MySTDF → AI → Alert → Action
Move beyond passive analysis toward a proactive engineering intelligence flow that connects test data, evidence, decisions, and follow-up action.
Tester
STDF, site, bin, parametric, wafer, and metadata inputs.
MySTDF
Scalable conversion, indexing, visualization, and engineering analysis.
AI
Pattern detection, correlation, upstream evidence, and candidate ranking.
Alert
Issue summary, confidence, evidence, and decision-ready notification.
Action
Recommended next checks, engineering follow-up, and report output.
Continuous monitoring ready
A foundation for scheduled conversion, analysis, reporting, and alerting.
Keep data close to engineering
SQLite-based processing avoids loading complete multi-gigabyte datasets into memory.
From result to explanation
Connect detected issues to root-cause candidates, supporting evidence, and next actions.
The engineering problem
Insight is often slower than the data.
Traditional workflows can be fragmented across scripts, spreadsheets, plots, and manual review. MySTDF brings the key evidence into one guided investigation flow.
Manual and reactive
- Repeated data preparation
- Disconnected analysis tools
- Root-cause evidence gathered manually
- Reports assembled after analysis
Integrated and proactive
- Structured local data foundation
- Interactive multi-view verification
- AI-assisted evidence ranking
- Automated decision-ready output
3-layer intelligence architecture
AI + Learning + Auto Report
A practical architecture that converts raw data into engineering intelligence, then preserves knowledge and produces repeatable output.
AI Layer
Root Cause Intelligence
- Correlation and pattern detection
- Upstream evidence ranking
- Wafer, site, bin, and parametric verification
- Root-cause candidate confidence
Learning Layer
Adaptive Knowledge
- User-defined role mapping
- Flexible keyword matching
- Persistent knowledge configuration
- Auto refresh without model retraining
Auto Report Layer
Decision Output
- Automated issue summary
- Root cause, why, and evidence
- Recommended next action
- Presentation-ready reporting
Auto Report
Decision-ready output, not just exported charts.
The report structure follows the way engineers communicate a problem: issue, root cause, evidence, and action.
1. Issue Summary
Yield loss is concentrated on Site 4 with a repeated edge-ring wafer signature.
2. Root Cause + Why
Primary candidate: site-dependent contact or hardware path instability.
3. Evidence
4. Recommended Action
Verify Site 4 contact path, compare continuity distributions, and review affected samples.
Interactive engineering analysis
Click to discover. Instantly understand.
Move from a wafer signature to the supporting tables, distributions, sites, tests, and samples.
Wafer Map Intelligence
Fail maps, parametric maps, spatial signatures, edge patterns, and abnormal clusters.
Test & Site Analysis
TSR, site distributions, fail rate, Cpk, mean shifts, and cross-wafer comparisons.
Correlation & Sanity
Upstream relationships, distribution changes, suspicious specs, and multi-view verification.
Failure Localization
Trace control-table findings to wafers, coordinates, samples, pins, and evidence views.
Scalable Data Foundation
Chunked STDF conversion, per-file SQLite databases, and hot project catalogs.
Automated Reporting
Repeatable summaries for engineering review, collaboration, and escalation.
Adaptive learning engine
User-driven intelligence without constant retraining.
Engineering knowledge can be added through role mapping and flexible keyword rules, then stored as a persistent knowledge configuration.
Define test roles and engineering meaning.
Connect keywords and tests to reusable knowledge.
Apply learned context during root-cause evaluation.
Persist updates and refresh future analyses.
User-defined classification for continuity, supply, scan, analog, IDDQ, and other test families.
Flexible, case-insensitive rules that can evolve without rebuilding the model.
Reusable JSON-based knowledge that remains available across future projects.
Future-ready foundation
Built for AI Agent, MCP, and automation initiatives.
The same structured data, analysis services, and decision outputs can support scheduled monitoring, CLI workflows, web dashboards, agent tools, and autonomous follow-up.
About BestEDA
Building the foundation for AI-driven engineering intelligence.
BestEDA develops focused tools for semiconductor test-data conversion, wafer analytics, root-cause investigation, adaptive engineering knowledge, and automated reporting.
The goal is to help engineers spend less time preparing and assembling data, and more time making traceable, confident decisions.
Start a discussion
Bring your STDF analysis use case.
Product inquiries, technical discussions, collaboration, and MySTDF demonstrations.