Building up End-to-end Mask Optimization Framework with Self-training

Abstract

With the continuous shrinkage of device technology node, the tremendously increasing demands for resolution enhancement technologies (RETs) have created severe concerns over the balance between computational affordability and model accuracy. Having realized the analogies between computational lithography tasks and deep learning-based computer vision applications (e.g., medical image analysis), both industry and academia start gradually migrating various RETs to deep learning-enabled platforms. In this paper, we propose a unified self-training paradigm for building up an end-to-end mask optimization framework from undisclosable layout patterns. Our proposed flow comprises (1) a learning-based pattern generation stage to massively synthesize diverse and realistic layout patterns following the distribution of the undisclosable target layouts, while keeping these confidential layouts blind for any successive training stage, and (2) a complete self-training stage for building up an end-to-end on-neural-network mask optimization framework from scratch, which only requires the aforementioned generated patterns and a compact lithography simulation model as the inputs. Quantitative results demonstrate that our proposed flow achieves comparable state-of-the-art (SOTA) performance in terms of both mask printability and mask correction time while reducing 66% of the turn around time for flow construction.