As part of the ICCAD Contest 2023 (Problem C) competition, the paper describes a methodology for applying ML models to perform static IR drop analysis. Methods for obtaining a database for training a neural network to solve this problem are given. We consider a technique for training an ML model to analyze the static IR-drop effect. The generation of input data for training a neural network from SPICE netlists is also discussed in this paper. This solution is ranked in the TOP 3 at the ICCAD Contest 2023 competition.

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 $15\times {10}^{\left(-5\right)}$ V while providing up to a 30× speedup compared to NGSPICE.