Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation. This study investigates the use of diffusion models in generating artificial data generation for electronic circuits for enhancing the accuracy of subsequent machine learning models in tasks such as performance assessment, design, and testing when training data is usually known to be very limited. We utilize simulations in the HSPICE design environment with 22nm CMOS technology nodes to obtain representative real training data for our proposed diffusion model. Our results demonstrate the close resemblance of synthetic data using diffusion model to real data. We validate the quality of generated data, and demonstrate that data augmentation certainly effective in predictive analysis of VLSI design for digital circuits.

翻译：生成式人工智能在过去几年中取得了显著进展，扩散模型已成为图像生成领域的先进技术。本研究探讨了在训练数据通常极为有限的条件下，如何利用扩散模型为电子电路生成人工数据，以提升后续机器学习模型在性能评估、设计与测试等任务中的准确性。我们采用22nm CMOS工艺节点的HSPICE设计环境进行仿真，为所提出的扩散模型获取具有代表性的真实训练数据。实验结果表明，基于扩散模型生成的合成数据与真实数据高度相似。我们对生成数据的质量进行了验证，并证明数据增强对数字电路VLSI设计的预测分析确实有效。