Generation of diverse VLSI layout patterns is crucial for various downstream tasks in design for manufacturing (DFM) studies. However, the lengthy design cycles often hinder the creation of a comprehensive layout pattern library, and new detrimental patterns may be discovered late in the product development process. Existing training-based ML pattern generation approaches struggle to produce legal layout patterns in the early stages of technology node development due to the limited availability of training samples.To address this challenge, we propose PatternPaint, a training-free framework capable of generating legal patterns with limited DRC Clean training samples. PatternPaint simplifies complex layout pattern generation into a series of inpainting processes with a template-based denoising scheme. Our framework enables even a general pre-trained image foundation model (stable-diffusion), to generate valuable pattern variations, thereby enhancing the library. Notably, PatternPaint can operate with any input size. Furthermore, we explore fine-tuning a pre-trained model with VLSI layout images, resulting in a 2x generation efficiency compared to the base model. Our results show that the proposed model can generate legal patterns in complex 2D metal interconnect design rule settings and achieves a high diversity score. The designed system, with its flexible settings, supports pattern generation with localized changes and design rule violation correction. Validated on a sub-3nm technology node (Intel 18A), PatternPaint is the first framework to generate a complex 2D layout pattern library using only 20 design rule clean layout patterns as input.

翻译：生成多样化的超大规模集成电路（VLSI）版图模式对于可制造性设计（DFM）研究中的各项下游任务至关重要。然而，冗长的设计周期往往阻碍了全面版图模式库的构建，且新的有害模式可能在产品开发后期才被发现。现有的基于机器学习的模式生成方法在技术节点开发早期，由于训练样本有限，难以生成符合设计规则的合法版图模式。为应对这一挑战，我们提出了PatternPaint，一种无需训练的框架，能够在仅有少量符合设计规则检查（DRC Clean）的训练样本情况下生成合法模式。PatternPaint通过基于模板的去噪方案，将复杂的版图模式生成简化为一系列图像修复过程。该框架使得通用的预训练图像基础模型（如stable-diffusion）也能生成有价值的模式变体，从而丰富模式库。值得注意的是，PatternPaint可处理任意输入尺寸。此外，我们探索了使用VLSI版图图像对预训练模型进行微调，其生成效率达到基础模型的两倍。实验结果表明，所提模型能够在复杂的二维金属互连设计规则设置下生成合法模式，并获得较高的多样性评分。该设计系统凭借其灵活的设置，支持生成具有局部修改的模式并修正设计规则违规。在亚3纳米技术节点（Intel 18A）上的验证表明，PatternPaint是首个仅需20个符合设计规则的版图模式作为输入，即可生成复杂二维版图模式库的框架。