AI-based explainable wafer scoring for semiconductor quality management

Abstract

Semiconductor quality links to corporate reliability and competitiveness directly. Therefore, detecting defects in advance and taking precautions before shipping is important for the company’s reliability and competitiveness. The semiconductor process consists of four steps: FAB process, probe test, assembly process, and package test. The probe test is the first stage to generate data for each die during the semiconductor process, so it plays an important role in predicting the final quality. Various studies have been conducted to predict and improve semiconductor yield by modeling based on the probe test data. However, the causes of quality and yield are not analyzed mainly through the prediction model. The traditional cause analysis of yield includes electrical tests and physical or structural analysis methods. In the case of conventional analysis methods, it takes a lot of money and time due to human resources and equipment limitations. Accordingly, if it is possible to explain the relationship between variable and prediction result from the prediction model, efficient cause analysis of yield will be possible. This study analyzes the probe and the package test data. Then, it develops a probe test defect prediction model using tree-based XGBoost. XGBoost is the latest machine learning model. While the previous studies focused solely on prediction performance, this study considers an explainable model. Therefore, it can explain prediction performance and results using the partial dependence function. After model development, wafer quality is defined based on the prediction probability of dies. That proposes a wafer scoring method for improving semiconductor yield. Also, the variables of the prediction model that affect wafer quality would be derived and analyzed. It is expected that it will contribute to interpreting wafer quality by deriving significant probe test variables. Package yield is an important indicator for the final quality of semiconductor finished products. Package yield improves after disposing of low-quality wafers based on the proposed wafer scoring method. The analysis method based on the explainable prediction model can significantly reduce the time required to analyze the cause of package yield using wafer scores. If this methodology is applied to the field, it will reduce the costs incurred in the semiconductor process, such as deriving an optimal probe test process that satisfies the target package quality.