Sub-resolution assist feature generation with reinforcement learning and transfer learning
Journal
IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISBN
9781450392174
Date Issued
2022-10-30
Author(s)
Abstract
As modern photolithography feature sizes continue to shrink, subresolution 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.
Subjects
Design for Manufacturability | Markov Decision Process | Reinforcement Learning | Sub-ResolutionAssist Feature | Transfer Learning
SDGs
Type
conference paper