Enhancing ML model accuracy for Digital VLSI circuits using diffusion
  models: A study on synthetic data generation
Prasha Srivastava Pawan Kumar Zia Abbas
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.