Variable Selection Under Missing Values and Unlabeled Data in Semiconductor Processes

Manufacturing semiconductor wafers involves many sequential processes, and each process has various equipment-related variables or factors, which results in high-dimensional data. However, measuring the quality of all wafers is time and cost intensive, and only a small proportion of the wafers is labeled. Further, equipment factors are not always measured by sensors due to the complicated process. Variable selection, which is performed to reduce the dimensionality of the input variable space while improving or preserving regression performance by selecting important input factors, plays an important role in regression problems. We propose a variable selection procedure to find the main equipment factors that affect in-process wafer quality in consideration of the following issues: 1) imputation for missing values; 2) semi-supervised regression for unlabeled data; and 3) redundancy among variables. In the proposed procedure, partial least squares and least absolute shrinkage and selection operator regression are utilized as prediction models. Experiments using two semiconductor equipment datasets were conducted to evaluate the performance of the proposed procedure.