Predictive Silicon Health Monitor

A machine learning system that shifts post-silicon validation from reactive troubleshooting to predictive maintenance. Trained on historical test data spanning millions of devices, the model identifies subtle parametric drifts in silicon performance before they escalate into batch failures. Uses gradient-boosted trees and anomaly detection on multi-dimensional test vectors to flag at-risk wafer lots. Integrated with automated alerting so the validation team can intervene early, preventing yield loss and reducing scrap rates. Moved the team from a "wait and fail" model to a "predict and prevent" paradigm.