An efficient deep learning framework for mixed-type wafer map defect pattern recognition
Hao Sheng,
Kun Cheng,
Xiaokang Jin
et al.
Abstract:Defect detection on wafers holds immense significance in producing micro- and nano-semiconductors. As manufacturing processes grow in complexity, wafer maps may display a mixture of defect types, necessitating the utilization of more intricate deep learning models for effective feature learning. However, sophisticated models come with a demand for substantial computational resources. In this paper, we propose an efficient deep learning framework designed explicitly for mix-type wafer map defect pattern recogni… Show more
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