Big data analytics for cycle time related feature selection in the semiconductor wafer fabrication system

Abstract

The dynamic wafer workshop gives rise to strong fluctuations of cycle time (CT) followed by earliness or tardiness penalty. Comprehending these fluctuations is a major challenge as the cycle time intricately interacts with massive parameters. This paper proposes a big data analytics method for feature selection to obtain all explanatory factors of CT that would shed light on the fluctuation of CT. Firstly, the correlative analysis is performed between each two candidate factors with mutual information metric to construct the observed network. Secondly, the network deconvolution is investigated to infer the direct dependence between the candidate factor and the CT by removing the effects of transitive relationships from the network. Additionally, a factor selection algorithm is designed to reduce the dimension of candidate factors to form the CT explanatory network, which contains the key factors interacting with the CT of wafer lots. The experimental results demonstrate the proposed method outperforms the references in term of classification and prediction accuracy. A case study is conducted with real data from a semiconductor wafer fabrication system, the big data captured for feature selection is analyzed, and the identified key factors are proved to be effective in forecasting and fluctuation interpretation of CT.