Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE

• URL: http://arxiv.org/abs/2512.23624v2
• Date: Wed, 31 Dec 2025 16:31:06 GMT
• Title: Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE
• Authors: Chien-Ting Tung, Chenming Hu,
• Abstract summary: NeuroSPICE is a physics-informed neural network (PINN) framework for device and circuit simulation.<n> PINNs do not outperform SPICE in speed or accuracy during training, but offer unique advantages such as surrogate models for design optimization and inverse problems.<n>NeuroSPICE's flexibility enables the simulation of emerging devices, including highly nonlinear systems such as ferroelectric memories.
• Score: 1.1697579672144856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present NeuroSPICE, a physics-informed neural network (PINN) framework for device and circuit simulation. Unlike conventional SPICE, which relies on time-discretized numerical solvers, NeuroSPICE leverages PINNs to solve circuit differential-algebraic equations (DAEs) by minimizing the residual of the equations through backpropagation. It models device and circuit waveforms using analytical equations in time domain with exact temporal derivatives. While PINNs do not outperform SPICE in speed or accuracy during training, they offer unique advantages such as surrogate models for design optimization and inverse problems. NeuroSPICE's flexibility enables the simulation of emerging devices, including highly nonlinear systems such as ferroelectric memories.

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