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Machine Learning Methods for Fast Evaluation of Static IR Drop Effect
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DOI：10.3390/technologies14030169.png)
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Abstract
En 中文
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.
Keywords:
IR drop
ML model
neural network
machine learning
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