BestEDA Semiconductor Test Intelligence

MySTDF 繚 STDF Conversion and Engineering Intelligence

Convert STDF at Scale. Analyze with Confidence.

MySTDF converts large STDF and compressed archive files into structured, analysis-ready outputs using efficient local processing. The converted data connects directly to wafer analysis, yield investigation, site comparison, AI-assisted root-cause analysis, and automated reporting.

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STDF / Archive Transfer Tool SQLite / Excel / PPT Analytics AI Root Cause Auto Report
MySTDF 繚 Engineering Workspace LOCAL
Wafer Yield97.84%Stable
Risk Tests123 high
Site Delta1.9%Site 4
Wafer PatternZ-score evidence
Yield TrendLot / wafer
Root-cause candidatesRanked evidence
  1. Site distribution shiftHigh
  2. Continuity OS tailMedium
  3. Edge-ring wafer patternMedium
MySTDF Transfer Tool converts tester output into reusable engineering data and report outputs.

STDF Conversion

Convert STDF Files into Analysis-Ready Outputs

MySTDF starts by converting raw STDF or archive files into structured outputs for downstream engineering analysis. The transfer engine is designed for large production files and uses efficient local processing without loading the complete dataset into memory.

  • STDF and compressed archive input
  • Chunked processing for large files
  • Per-file SQLite database output
  • Optional Excel output
  • Optional automated PPT reporting
  • Local processing without cloud upload
  • Transfer progress and detailed status log
  • Portable Windows deployment
  • No Python installation required for the packaged release

Conversion Pipeline

Convert Once. Reuse the Data Across Engineering Workflows.

The structured database becomes the reusable data foundation for TSR analysis, wafer maps, site comparison, bin analysis, correlation, project trends, and automated reporting.

01STDF / STDF.GZ
02Transfer Engine
03SQLite Database
04Engineering Analytics
05AI Root Cause
06Auto Report

Engineering Analytics

One Test. Multiple Engineering Views.

Select a test once and verify its wafer pattern, parametric behavior, zone trend, cumulative distribution, and site variation in one synchronized engineering workspace.

Synchronized visual analysis helps engineers connect spatial patterns, distributions, limits, and site behavior.
Fail Wafer Map Parametric Wafer Map Zone and Edge Trend Cumulative Distribution By-Site Comparison

Project Yield Management

Project-Level Yield and Risk Visibility

Review lots, wafers, yield, hardware-bin loss, first-fail behavior, TSR results, and project trends before opening detailed per-file analysis.

The project catalog provides fast navigation and summary intelligence while detailed results remain available in per-file databases.
Multi-lot and multi-wafer overview
Yield trend monitoring
Hardware-bin summary
First-fail analysis
Top-risk test identification
Project catalog status
Fast drill-down to detailed TSR analysis

Engineering Intelligence

From Engineering Evidence to Root-Cause Candidates

AI assists the investigation by organizing evidence and ranking candidates. Engineers retain visibility into the data, logic, and final decision.

01

Evidence

Correlation and Pattern Detection

  • Wafer, site, bin, and parametric verification
  • Distribution shift and outlier checks
  • Spatial pattern and edge behavior review
02

Ranking

Root-Cause Candidate Confidence

  • Upstream evidence ranking
  • Candidate confidence scoring
  • Traceable supporting checks
03

Action

Recommended Next Checks

  • Engineer-controlled decision making
  • Focused follow-up experiments
  • Clear paths into automated 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.

IssueRoot CauseEvidenceAction
MySTDF AUTO REPORTWafer Issue Summary
Decision Ready
Yield92.8%
Top FailHB 103
AI RiskHigh
Confidence0.86

Issue Summary

Yield loss is concentrated on one site with a repeated edge-ring wafer signature.

Root Cause and Why

Primary candidate: site-dependent contact or hardware path instability.

Supporting Evidence

Recommended Action

Verify the affected contact path, compare continuity distributions, and review impacted samples.

Adaptive Learning

User-Driven Intelligence Without Constant Retraining

Engineering knowledge can be added through role mapping, flexible keyword matching, persistent knowledge configuration, reusable engineering rules, and automatic refresh of future analysis.

Role Mapping

User-defined classification for continuity, supply, scan, analog, IDDQ, and other test families.

Flexible Keywords

Case-insensitive rules can evolve as new products, tests, and naming styles appear.

Reusable Rules

Persistent configuration keeps engineering context available across future projects and reports.

Download

Download the Latest MySTDF Release

Convert STDF files locally, generate structured analysis outputs, and explore the MySTDF engineering workflow without building custom scripts.

Portable Windows package 繚 No Python installation required

Download Latest Release View Release Notes

Start a Discussion

Bring your STDF analysis use case.

Product inquiries, technical discussions, collaboration, and MySTDF demonstration requests.

Email BestEDA yrwuang@gmail.com