An Efficient Deep Learning Framework for Mixed-Type Wafer Map Defect Pattern Recognition

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 recognition. Our proposed model incorporates several crucial design elements, including lightweight convolutions, bottleneck residual connections, efficient channel attention mechanisms, and optimized activation functions, enabling it to learn spatial and channel features efficiently. We conduct evaluations on a real-world dataset containing 38 mixed-type defect patterns. The experimental results demonstrate that our framework maintains a high level of accuracy while possessing a compact parameter size and remarkably fast inference speed.