Sustainability in Semiconductor Production via Interpretable and Reliable Predictions

Abstract

Sustainability in production stands out as one of the foremost achievements facilitated by Industry 4.0. With the continuous monitoring of production lines, it is now possible to detect quality deviations at their earliest occurrence and reduce the scrap production rate. This paper studies the sustainability in context of a partially monitored multistage semiconductor production line with multiple test units. Our goal is to provide a foundation for interpretable and reliable Product Quality Prediction (PQP) in industrial use cases with noisy quality check results and missing process information. To that end, we examine how the accumulated process information from different steps of the production line impacts the accuracy of the PQP models used later as base learners. Furthermore, we highlight the drawbacks of conventional model stacking on the accuracy due to base learner’s prediction quantization. We propose instead an interpretable stacked model (SM) from base learners’ predicted probability values, and demonstrate how it increases the accuracy and reliability of the predictions. To improve the results even further, we analyze different calibration methods both for base learners and the SM. Our final model leads to a 19.49% reduction in the binary estimated calibration error compared to the conventional models, and thus allows for increased PQP reliability.