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.

翻译：本文提出NeuroSPICE，一种基于物理信息的神经网络（PINN）框架，用于器件与电路仿真。与传统SPICE依赖时间离散化数值求解器不同，NeuroSPICE利用PINN通过反向传播最小化方程残差来求解电路微分代数方程（DAE）。该框架采用时域解析方程并精确计算时间导数，对器件与电路波形进行建模。尽管PINN在训练过程中的速度与精度未超越SPICE，但其具备独特优势，例如可为设计优化与反问题提供代理模型。NeuroSPICE的灵活性使其能够仿真新兴器件，包括铁电存储器等高度非线性系统。