What question did this study set out to answer?

The aim is to enhance the evaluation of static IR drop in semiconductor designs using machine learning techniques.

March 12, 2026Open Access

Machine Learning Methods for Fast Evaluation of Static IR Drop Effect

Key Points

The aim is to enhance the evaluation of static IR drop in semiconductor designs using machine learning techniques.
Developed a machine learning framework utilizing U-Net architecture.
Integrated MaxViT and EfficientNet encoders for efficient image processing.
Implemented a SPICE-to-image conversion flow with an asymmetric loss function.
Achieved a Mean Absolute Error (MAE) below 15×10−5 V.
Provided a speedup of up to 30× compared to traditional NGSPICE methods.
Ranked Top 3 in the ICCAD 2023 Contest (Problem C).

Abstract

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.

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Cite This Study

Solovyev et al. (Sun,) studied this question.

synapsesocial.com/papers/69b2589696eeacc4fcec86c7 https://doi.org/https://doi.org/10.3390/technologies14030169

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