Bayesian Belief Network-based approach for diagnostics and prognostics of semiconductor manufacturing systems

Authors:
Lei Yang;Jay Lee
Affiliations:
Department of Mechanical Engineering, NSF I/UCRC on Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH 45221, USA;Department of Mechanical Engineering, NSF I/UCRC on Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH 45221, USA
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2012

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Abstract

Semiconductor manufacturing is a complex process in that it requires different types of equipments (also referred to as tools in semiconductor industry) with various control variables under monitoring. As the number of sensors grows, a huge amount of data are collected from the production; and yet, the relations among these control variables and their effects on finished wafer are to be fully understood for both equipment monitoring and quality assurance. Meanwhile, as the wafer goes through multiple periods with different recipes, failure that occurs during the process can both cause tremendous loss to manufacturer and compromise product quality. Therefore, occurred failure should be detected as soon as possible, and root cause need to be identified so that corrections can be made in time to avoid further loss. In this paper, we propose to apply Bayesian Belief Network (BBN) to investigate the causal relationship among process variables on the tool and evaluate their influence on wafer quality. By building BBN models at different periods of the process, the causal relation between control parameters, and their influence on wafer can be both qualitatively indicated by the network structure and quantitatively measured by the conditional probabilities in the model. In addition, with the BBN probability propagation, one can diagnose root causes when bad wafer is produced; or predict the wafer quality when abnormal is observed during the process. Our tests on a Chemical Vapor Deposition (CVD) tool show that the BBN model achieves high classification rate for wafer quality, and accurately identifies problematic sensors when bad wafer is found.