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Anomaly Detection for Semiconductor Test Systems
ML-based anomaly detection in high-throughput chip testing environments.
Client: Semiconductor test equipment company
Challenge
Manual inspection of test data from millions of chip tests was a bottleneck. Subtle anomalies in test patterns went undetected until downstream failures occurred, causing costly recalls and delays.
Our approach
We built an anomaly detection pipeline for semiconductor test data. The solution combines statistical process control with ML-based pattern recognition. A real-time alerting system notifies production line engineers instantly, and historical trend analysis enables root cause correlation across test runs.
Results
01Real-time anomaly detection across millions of test cycles
0290% reduction in undetected test failures
03Automated root cause correlation
04Sub-second alerting latency
Tech stack
Pythonscikit-learnApache KafkaInfluxDBGrafanaReactDocker