MF-VIT: Lithography Hotspot Detection Based on Multi-scale Feature and Vision Transformer

As the size of chips continues to shrink, the optical proximity effect becomes increasingly pronounced. Lithography hotspots, such as open circuits or short circuits, may manifest during the manufacturing process, adversely impacting chip yield and reliability. Although deep learning has been widely applied to hotspot detection, challenges remain in achieving high recall and precision. In this paper, we propose a hotspot classification model MF-VIT based on an improved Vision Transformer, addressing these challenges using a multi-scale feature fusion strategy. In addition, the Focal Loss function is introduced to effectively mitigate the issue of imbalanced lithography hotspot data. Comparative experiments on the ICCAD-2012 Contest benchmarks demonstrate that the proposed method achieves superior classification performance, yielding an overall average recall rate of 98.5%.