Spatial Modeling with Automated Machine Learning and Gaussian Process Regression Techniques for Imputing Wafer Acceptance Test Data

The Wafer Acceptance Test (WAT) is a significant quality control measurement in the semiconductor industry. However, because the WAT process can be time-consuming and expensive, sampling test is commonly employed during production. This makes root cause tracing impossible when abnormal products have not been tested. Therefore, in our study, we focus on establishing a reliable method to estimate WAT results for non tested shots, including both intra and inter-wafer prediction. Notably, we are the first to combine the use of Chip Probing data with WAT to improve the predictions. Our proposed method first extracts valuable features from Chip Probing test results by using the Automated Machine Learning technique. We then employ Gaussian Process Regression to capture the spatiotemporal correlation. Finally, we adopted the linear regression model to ensemble two components and proposed a SMART-WAT model to effectively estimate the wafer acceptance test data. Our method has been tested on a real-world dataset from the semiconductor manufacturing industry. The prediction results of four key WAT parameters indicate that our proposed model outperforms the state-of-the-art methods in both intra and inter-wafer prediction.