🤖 AI Summary
Traditional SPICE simulation struggles with strongly nonlinear emerging devices (e.g., ferroelectric memory) due to its reliance on time-discretized numerical solvers, inability to analytically compute derivatives, and lack of native support for inverse problems. To address these limitations, we propose NeuroSPICE—the first systematic framework integrating physics-informed neural networks (PINNs) into circuit simulation. NeuroSPICE directly solves differential-algebraic equations (DAEs) in the time domain via residual minimization and automatic differentiation, enabling end-to-end waveform prediction and generating compact, high-fidelity, differentiable analytical surrogate models. Its core innovations include breaking from conventional SPICE paradigms to natively support joint device-circuit modeling, parameter inversion, and real-time design optimization. Experiments demonstrate that NeuroSPICE significantly improves both simulation efficiency and interpretability for nonlinear circuits, establishing a new paradigm for next-generation EDA tools.
📝 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.