A Copula network deconvolution-based direct correlation disentangling framework for explainable fault detection in semiconductor wafer fabrication

Wafer fabrication is a highly complex manufacturing system. Using complex network models to portray the correlation between parameters is an effective tool for finding the key influencing factors associated with the cause of wafer defects. However, the complex network in a wafer fabrication system has many nonlinearities, non-normal distributions, coupling, and spurious correlations. Quantitative clarification of direct and indirect correlations in the complex network is the prerequisite for achieving explainable fault detection in smart manufacturing. Therefore, a Copula network deconvolution-based direct correlation disentangling framework is proposed in the context of explainable fault detection in semiconductor wafer fabrication. Firstly, The complex network correlation diagram of the parameters in the wafer fabrication process is constructed with each parameter as a node and the correlation coefficients between parameters as connected edges. Then, a nonlinear correlation metric model based on adaptive Copula function selection is designed to deal with the nonlinear correlation of complex network models. The adaptive selection of the Copula function is realized by the goodness-of-fit test method based on Euclidean distance. After that, considering the coupling and spurious correlation in the complex network model, a network deconvolution-based fault detection method is designed to identify direct correlation. Finally, a case study using the actual data from semiconductor wafer fabrication systems is conducted to compare the existing mainstream fault detection methods and validate the effectiveness of the proposed method.