Quality-Oriented Statistical Process Control Utilizing Bayesian Modeling

Abstract

Quality control is an important issue in semiconductor manufacturing. Statistical process control (SPC) is known as a powerful method for accomplishing process stability and reducing variability. In this paper, we adopt the quality-oriented statistical process control (QOSPC) method. In QOSPC, product quality test data, such as electrical performance and product reliability, are incorporated into the process control procedure. QOSPC has two major challenges: extracting process variables that affect product quality, and determining quality control limits (QCLs) for each variable. In this work, we fully exploit a Bayesian approach to resolve both of these challenges simultaneously. We introduced a linear bathtub model that contains parameters corresponding to QCLs as obvious change points and fit the model to the observed data by Bayesian inference (BI). In our experiments with artificial datasets, we demonstrated that the values of QCLs and their confidence, by which we can judge whether the measured process variable is related to product quality, are estimated successfully by BI. We verified the robustness of our method by testing it repeatedly. The proposed method reduced the human labor cost for extracting quality-related process variables and determining QCLs by 93%.