DiffPattern-Flex: Efficient Layout Pattern Generation via Discrete Diffusion

--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 DiffPattern-Flex, a novel approach designed to generate reliable layout patterns efficiently. DiffPattern-Flex incorporates a new method for generating diverse topologies using a discrete diffusion model while maintaining a lossless and compute-efficient layout representation. T o 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 DiffPattern-Flex significantly outperforms existing methods and excels at producing reliable layout patterns. ELIABLE very-large-scale integration (VLSI) layout pattern libraries form the backbone of various Design for Manufacturability (DFM) research, such as refining design rules [1]-[3], optimizing Optical Proximity Correction (OPC) techniques [4]-[6], performing lithography simulations [7]-[9], and detecting layout hotspots [10]-[12]. With the increasing demand for layout patterns in machine-learning-based lithography design, building a comprehensive and practical large-scale pattern library has become highly resource-intensive due to the extended logic-to-chip design cycle. To address this challenge, a variety of rule-based and learning-based layout pattern generation methods have been introduced. These units were then randomly selected and combined. However, this approach results in limited diversity and quantity of generated patterns. More recently, learning-based generative methods [15]-[19] have demonstrated the ability to produce diverse layout patterns at a larger scale. This work is supported by The Research Grants Council of Hong Kong SAR (No. CUHK14208021) and the MIND project (MINDXZ202404). Y unheng Shen is with Tsinghua University, Beijing, China.