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GLAU-Net: a fast lithography imaging model for chemically amplified resists
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DOI：10.1364/AO.584083.png)
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Abstract
En 中文
Lithography simulation based on rigorous models is essential but time-consuming for developing and optimizing the chip manufacturing process. Exposure and post-exposure bake (PEB) are crucial steps to generate the PEB latent images, which directly determine the final photoresist profile. This paper proposes a novel, to our knowledge, deep-learning-based lithography imaging model, dubbed GLAU-Net, to accelerate the simulation efficiency of exposure and PEB steps for the extensively used chemically amplified resist. The network incorporates a mask attention gate module that dynamically assigns higher weights to the critical mask regions. A composite loss function combining the mean-squared error with a gradient loss is proposed to improve the prediction accuracy on the latent image boundaries. Experiments show that the GLAU-Net accelerates the simulation efficiency by 235 times and four times compared to the CPU-based and GPU-accelerated rigorous models, respectively. In addition, the proposed method outperforms the other popular deep-learning models in terms of prediction accuracy on the latent image and post-development photoresist profile. (c) 2026 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI)training, and similar technologies, are reserved.
Keywords:
POSTEXPOSURE BAKE
AMPLIFICATION
COMPONENTS
SIMULATION
DIFFUSION
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A
IF:
1.7
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798
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5.1W
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