用物理约束神经网络模拟电路，加速新型器件设计。
Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE
- 用神经网络直接求解电路微分代数方程，避免传统数值方法。
- 可模拟铁电存储器等高度非线性器件，支持快速优化与逆问题求解。
- 适合做芯片设计中的快速仿真与参数反演，尤其适合新兴器件。
我们提出NeuroSPICE，一种基于物理信息神经网络（PINN）的器件与电路仿真框架。与依赖时间离散化数值求解器的传统SPICE不同，NeuroSPICE利用PINN通过反向传播最小化电路微分-代数方程（DAEs）的残差来求解。它在时域中使用解析式建模器件与电路波形，并保证精确的时间导数。尽管训练阶段的精度和速度不及SPICE，但其优势在于可构建用于设计优化与逆问题的代理模型。NeuroSPICE的灵活性使其能够模拟新兴器件，如高度非线性的铁电存储器。
原文摘要 · Abstract (English)
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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