A Gaussian Mixture Model Clustering Ensemble Regressor for Semiconductor Manufacturing Final Test Yield Prediction

Abstract

In the semiconductor industry, many studies have been carried out for front-end related process improvement and yield prediction using machine learning techniques. However, very few research investigations have dealt with the backend Final Test (FT) yield prediction using the front-end wafer acceptance test (WAT) parameters. The manufacturing cycle time between wafer fabrication (WF) and FT can range anywhere between a few weeks to several months. It is therefore important for semiconductor manufacturers to detect wafer material related low yield problems at an earlier stage for effective cost and quality control. This is a challenging goal as the input data used for prediction is at a very early manufacturing stage and the output FT yield for packaged chips is the last stage of the fabrication chain. There are many unknown production variations caused by different manufacturing processes, equipment configurations and human interferences in this multi-stage sequential fabrication chain. In this paper, we proposed a novel procedure to predict the backend FT yield at the WF stage itself using a Gaussian Mixture Models (GMM) clustering approach that is applied to build a weighted ensemble regressor. Real production data for new chip product lines are verified with this method and show significant improvement in the prediction performance.

Keywords

• Semiconductor manufacturing
• yield prediction
• final test
• Gaussian mixture models
• clustering
• regression