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The growing availability of the data collected from smart manufacturing ischanging the paradigms of production monitoring and control. The increasingcomplexity and content of the wafer manufacturing process in addition to thetime-varying unexpected disturbances and uncertainties, make it infeasible todo the control process with model-based approaches. As a result, data-drivensoft-sensing modeling has become more prevalent in wafer process diagnostics.Recently, deep learning has been utilized in soft sensing system with promisingperformance on highly nonlinear and dynamic time-series data. Despite itssuccesses in soft-sensing systems, however, the underlying logic of the deeplearning framework is hard to understand. In this paper, we propose a deeplearning-based model for defective wafer detection using a highly imbalanceddataset. To understand how the proposed model works, the deep visualizationapproach is applied. Additionally, the model is then fine-tuned guided by thedeep visualization. Extensive experiments are performed to validate theeffectiveness of the proposed system. The results provide an interpretation ofhow the model works and an instructive fine-tuning method based on theinterpretation.

Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned