DiffPattern-Flex: Efficient Layout Pattern Generation via Discrete Diffusion

TL;DR

DiffPattern-Flex introduces a discrete diffusion-based method for efficient, reliable layout pattern generation that ensures legality through an optimization-based assessment, outperforming existing approaches.

Contribution

It presents a novel discrete diffusion model for diverse topology generation combined with a white-box legality check, enhancing efficiency and reliability in layout pattern synthesis.

Findings

Outperforms existing methods on multiple benchmarks.

Generates diverse, legal layout patterns efficiently.

Accelerates layout generation with fast sampling techniques.

Abstract

Recent advancements in layout pattern generation have been dominated by deep generative models. However, relying solely on neural networks for legality guarantees raises concerns in many practical applications. In this paper, we present \tool{DiffPattern}-Flex, a novel approach designed to generate reliable layout patterns efficiently. \tool{DiffPattern}-Flex incorporates a new method for generating diverse topologies using a discrete diffusion model while maintaining a lossless and compute-efficient layout representation. To ensure legal pattern generation, we employ {an} optimization-based, white-box pattern assessment process based on specific design rules. Furthermore, fast sampling and efficient legalization technologies are employed to accelerate the generation process. Experimental results across various benchmarks demonstrate that \tool{DiffPattern}-Flex significantly outperforms existing methods and excels at producing reliable layout patterns.