Machine Learning Methods for Fast Evaluation of Static IR Drop Effect

With the continuous scaling of semiconductor design technologies, evaluating static IR drop has become a critical bottleneck in the physical synthesis flow. This paper presents a machine learning-based framework that transforms the power delivery network (PDN) analysis problem into an image-to-image translation task using a U-Net architecture with MaxViT and EfficientNet encoders. By implementing a novel SPICE-to-image conversion flow and an asymmetric loss function, our method achieved a Top 3 ranking in the ICCAD 2023 Contest (Problem C). The experimental results demonstrate that the proposed model achieves a Mean Absolute Error (MAE) below 15×10−5 V while providing up to a 30× speedup compared to NGSPICE.