计算机科学
强化学习
催交
过程(计算)
人工智能
特征(语言学)
学习迁移
GSM演进的增强数据速率
机器学习
语言学
操作系统
工程类
哲学
系统工程
作者
Guanting Liu,Wei-Chen Tai,Yi‐Ting Lin,Iris Hui-Ru Jiang,James P. Shiely,Pu‐Jen Cheng
标识
DOI:10.1145/3508352.3549388
摘要
As modern photolithography feature sizes continue to shrink, sub-resolution assist feature (SRAF) generation has become a key resolution enhancement technique to improve the manufacturing process window. State-of-the-art works resort to machine learning to overcome the deficiencies of model-based and rule-based approaches. Nevertheless, these machine learning-based methods do not consider or implicitly consider the optical interference between SRAFs, and highly rely on post-processing to satisfy SRAF mask manufacturing rules. In this paper, we are the first to generate SRAFs using reinforcement learning to address SRAF interference and produce mask-rule-compliant results directly. In this way, our two-phase learning enables us to emulate the style of model-based SRAFs while further improving the process variation (PV) band. A state alignment and action transformation mechanism is proposed to achieve orientation equivariance while expediting the training process. We also propose a transfer learning framework, allowing SRAF generation under different light sources without retraining the model. Compared with state-of-the-art works, our method improves the solution quality in terms of PV band and edge placement error (EPE) while reducing the overall runtime.
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