SemiKong, a semiconductor-domain fine-tune of Llama 3, is reported to outscore larger general models on expert-style etching questions, but every score comes from a GPT-4o judge with no reported agreement against human experts.
CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning
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abstract
Optical proximity correction (OPC) is a vital step to ensure printability in modern VLSI manufacturing. Various OPC approaches based on machine learning have been proposed to pursue performance and efficiency, which are typically data-driven and hardly involve any particular considerations of the OPC problem, leading to potential performance or efficiency bottlenecks. In this paper, we propose CAMO, a reinforcement learning-based OPC system that specifically integrates important principles of the OPC problem. CAMO explicitly involves the spatial correlation among the movements of neighboring segments and an OPC-inspired modulation for movement action selection. Experiments are conducted on both via layer patterns and metal layer patterns. The results demonstrate that CAMO outperforms state-of-the-art OPC engines from both academia and industry.
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SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Model
SemiKong, a semiconductor-domain fine-tune of Llama 3, is reported to outscore larger general models on expert-style etching questions, but every score comes from a GPT-4o judge with no reported agreement against human experts.