Use Cases & Benefits

Benefits and Use Cases

Support root cause analysis for yield degradation, equipment anomaly detection, and new product ramp-up, leading to shorter analysis time, reduced engineering workload, and shorter TAT.

Implementation image from test data collection to analysis and visualization
Visualize category and value distributions on a wafer to support root cause analysis

Root Cause Analysis for Yield Degradation

  1. Check yield at the lot level
  2. Visualize spatial distribution with wafer maps
  3. Identify local anomalies through region splitting
  4. Extract influential parameters using correlation analysis
  5. Feed insights back to processes and equipment

Equipment Anomaly Detection

Monitor trends using control charts such as Xbar-R, EWMA, and CUSUM, and detect equipment anomalies early through drift detection.

New Product Ramp-Up

Useful for early evaluation of parameter distributions, extraction of important parameters through correlation analysis, and early removal of abnormal data.

Benefits

Implementation Benefits

Support yield improvement, early defect detection, process and equipment anomaly detection, analysis automation, reduced engineering workload, lower manufacturing costs, and shorter production lead time.

Hours → Minutes Shorter anomaly detection lead time
50〜90% Reduction in analysis time
30〜70% Reduction in engineering workload
Shorter TAT Faster process feedback

* Effects depend on the environment, product, and implementation scope.

Differentiation from Other Tools

  • Designed specifically for semiconductor test data
  • Supports Lot / Wafer / Site structures
  • Integrated wafer maps and statistical analysis
  • Cross-analysis of WT/FT data
  • Architecture designed for big data

Prerequisites and Limitations

  • Data is assumed to be managed by lot
  • Missing values depend on preprocessing or internal processing
  • Correlation accuracy may decrease with sparse data

Future Extensions

  • Anomaly detection using machine learning
  • Defect classification using clustering
  • Predictive analysis for yield prediction

Contact

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