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%.
Highlights
Q UALITY control is important to assure that products conform to a set of required specifications in semiconductor manufacturing
The chart contains upper and lower control limits determined based on a statistical value of past measurement data such that points that plot outside the control limits indicate the occurrence of shifts of process performance over time [1], [2]
It is possible to select an intuitive quality control limits (QCLs) by sight, but unnecessarily strict process control has a risk of increasing cost, and it is time-consuming and unrealistic to visually identify QCLs for the tens of thousands of process variables measured in a production line
Summary
Q UALITY control is important to assure that products conform to a set of required specifications in semiconductor manufacturing. In a real-world production line, QOSPC has two major challenges: extracting process variables that affect product quality and determining QCLs for each variable. When the credibility is high, we can accept the inferred values as QCLs; that is, we can conclude that the process variable is related to the product quality and should be controlled by the QCLs. when the credibility is low, we do not select the inferred values as QCLs. Our method resolves the two abovementioned challenges simultaneously and makes QOSPC feasible even when there are a large number of process variables to be measured in a production line. We extended our method to 2-dimensional measured variables in Section V, and in Section VI we present the conclusions of this work
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