To address the persistent challenges of low F1-score and high false alarm rate (FAR) in lithography hotspot detection, this study proposes a new detection framework based on a Dual Attention Swin transformer (DA-Swin). The proposed architecture leverages the multiscale feature fusion capability of the Feature Pyramid Network (FPN) to enhance the model's sensitivity to scale-invariant features. Meanwhile, the Bottleneck Attention Module (BAM) establishes a channel-spatial cooperative attention mechanism, achieving a 22.2% reduction in FAR. Compared to CrossEntropyLoss, the adoption of BCEWithLogitsLoss significantly reduces output layer parameters by approximately 50%. Benchmark tests on ICCAD demonstrate that our method achieves an accuracy of 98.67% and an F1-score of 0.857, surpassing prior works (TCAD'18, ICCAD'20, and JM3'24). Ablation studies further confirm the synergistic optimization of each module, delivering a highprecision (>98%) and costeffective (14.85% faster inference speed) solution for advancednode IC layout verification.

Lu Chunyan. FPN and BAM Module-Enhanced Swin Transformer for Lithography Hotspot Detection. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 2: 30-38. https://doi.org/10.25236/AJCIS.2026.090205.

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