Site-aware Anomaly Detection with Machine Learning for Circuit Probing to Prevent Overkill

This paper introduces an anomaly detection methodology with machine learning for Circuit Probing (CP) using Integrated Passive Device (IPD) as example devices. The IPD can improve the power integrity, performance, and package dimensions of the Integrated Fan-Out Package on Package (InFO-PoP), which is more cost-effective than 3D Integrated Circuits (3DIC) to achieve “More than Moore’s law” for mobile devices. Because a defective IPD can invalidate the entire package, the previous test methods are dedicated to very high-end screening for the underkill/failure-escape of high quality and reliable devices. On the other hand, the overkill issues are not concerned yet, which periodically impact the yield and cost. In this paper, we propose a new flow with machine learning methodologies to detect previously ignored anomalies on site-aware wafer-maps for predictive maintenance. The proposed flow covers the overkill and re-test issues to complete the high-quality and cost-effective test methodology with test defense.