Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE
This provides a flexible simulation tool for circuit designers, though it is incremental as it builds on existing PINN methods without outperforming SPICE in speed or accuracy.
The authors tackled circuit simulation by developing NeuroSPICE, a physics-informed neural network framework that solves differential-algebraic equations, offering advantages like surrogate models for design optimization and enabling simulation of emerging devices such as ferroelectric memories.
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.