LayouTransformer

Abstract

Generating legal and diverse layout patterns to establish large pattern libraries is fundamental for many lithography design applications. Existing pattern generation models typically regard the pattern generation problem as image generation of layout maps and learn to model the patterns via capturing pixel-level coherence, which is insufficient to achieve polygon-level modeling, e.g., shape and layout of patterns, thus leading to poor generation quality. In this paper, we regard the pattern generation problem as an unsupervised sequence generation problem, in order to learn the pattern design rules by explicitly modeling the shapes of polygons and the layouts among polygons. Specifically, we first propose a sequential pattern representation scheme that fully describes the geometric information of polygons by encoding the 2D layout patterns as sequences of tokens, i.e., vertexes and edges. Then we train a sequential generative model to capture the long-term dependency among tokens and thus learn the design rules from training examples. To generate a new pattern in sequence, each token is generated conditioned on the previously generated tokens that are from the same polygon or different polygons in the same layout map. Our framework, termed LayouTransformer, is based on the Transformer architecture due to its remarkable ability in sequence modeling. Comprehensive experiments show that our LayouTransformer not only generates a large amount of legal patterns but also maintains high generation diversity, demonstrating its superiority over existing pattern generative models.