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.

通过采用扩散模型在电路生成上进行人工数据生成的研究，用于提高在通常训练数据非常有限的情况下，机器学习模型在性能评估、设计和测试等任务中的准确性，本研究利用 HSPICE 设计环境中 22 纳米 CMOS 技术节点的仿真来获取真实代表性的训练数据，并证明了扩散模型生成的合成数据与真实数据的密切相似性，通过验证生成数据的质量，证明数据增强在数字电路的 VLSI 设计的预测分析中确实有效。