Feature Selection for Waiting Time Predictions in Semiconductor Wafer Fabs

Based on real operational data from the Robert Bosch GmbH, we investigate influencing features for waiting time estimation of operations in high product-mix / low-volume semiconductor manufacturing fabs. We define waiting time as the elapsed time between completing the previous operation and starting the next one. In addition to well-established features, we introduce novel features to capture the complexity of the manufacturing environment. To the best of our knowledge, we are the first to attempt waiting time estimation in a high product-mix / low-volume semiconductor fab. We present a framework for feature selection which is composed of three steps: First, random forest models are trained for each operation. Second, a permutation feature importance (PFI) for the full set of features for each operation is computed and the performance is statistically evaluated. The optimal subset of features is then chosen by a sequential backward search based on the PFI values. Third, the performance in terms of the coefficient of determination of each optimized model is evaluated by means of the initial performance. We apply the framework to real operational data from the production areas Lithography and Diffusion and conclude that the feature set can be reduced significantly, while the prediction performance remains equal. The novel features are found to be frequently used when estimating waiting times in the investigated use case.