Weak Die Screening by Feature Prioritized Random Forest for Improving Semiconductor Quality and Reliability

With the increasing demand for safety-critical products, the quality and reliability of semiconductor components are among the top priorities. In recent years, test data analytics by machine learning (ML) algorithms are widely considered to have great potential for improving the quality and reliability of semiconductor chips. In this work, we inspect a typical test flow of advanced semiconductor products, and propose an ML-based weak die screening method for improving the quality and reliability of shipped products. We propose the feature prioritized random forest (FPRF) model, which can fit smoothly into the existing test flow. We perform experiments on an advanced SRAM product using the FPRF model. We perform feature analysis based on the test data obtained from the final test (FT). After the FPRF screening, we are able to screen out more bad dies from those that have passed the FT. For an overkill rate of 12.93 %, the bad die hit rate can be as high as 96.55%. One can explore the FPRF model for other products as well.