Machine Learning Methods for Fast Evaluation of Static IR Drop Effect

Roman Aleksandrovich Solovyev; Dmitry Vladimirovich Telpukhov; Ilya Ilyich Shafeev; Evgeny Demidov; Alexander Stempkovskiy; V. V. Zunin; Alexander Murashov; Alexey Zablotskiy; A. Yu. Romanov

doi:10.3390/technologies14030169

Machine Learning Methods for Fast Evaluation of Static IR Drop Effect

Technologies 2026 Open access

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Authors: Roman Aleksandrovich Solovyev, Dmitry Vladimirovich Telpukhov, Ilya Ilyich Shafeev, Evgeny Demidov, Alexander Stempkovskiy, V. V. Zunin, Alexander Murashov, Alexey Zablotskiy, A. Yu. Romanov

DOI: 10.3390/technologies14030169

Cited by: 0

References: 20

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.

Fields of Study & Subjects

VLSI and FPGA Design Techniques Low-power high-performance VLSI design Analog and Mixed-Signal Circuit Design

Publication details

Journal / source: Technologies
Volume: 14
Issue: 3
Pages: 169–169
Publication date: 8 March 2026
Language: EN

References 20