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"The growing availability of the data collected from smart manufacturing is\nchanging the paradigms of production monitoring and control. The increasing\ncomplexity and content of the wafer manufacturing process in addition to the\ntime-varying unexpected disturbances and uncertainties, make it infeasible to\ndo the control process with model-based approaches. As a result, data-driven\nsoft-sensing modeling has become more prevalent in wafer process diagnostics.\nRecently, deep learning has been utilized in soft sensing system with promising\nperformance on highly nonlinear and dynamic time-series data. Despite its\nsuccesses in soft-sensing systems, however, the underlying logic of the deep\nlearning framework is hard to understand. In this paper, we propose a deep\nlearning-based model for defective wafer detection using a highly imbalanced\ndataset. To understand how the proposed model works, the deep visualization\napproach is applied. Additionally, the model is then fine-tuned guided by the\ndeep visualization. Extensive experiments are performed to validate the\neffectiveness of the proposed system. The results provide an interpretation of\nhow the model works and an instructive fine-tuning method based on the\ninterpretation."
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