We demonstrate that data-driven system identification techniques can provide a basis for effective, non-intrusive model order reduction (MOR) for common circuits that are key building blocks in microelectronics. Our approach is motivated by the practical operation of these circuits and utilizes a canonical Hammerstein architecture. To demonstrate the approach we develop a parsimonious Hammerstein model for a non-linear CMOS differential amplifier. We train this model on a combination of direct current (DC) and transient Spice (Xyce) circuit simulation data using a novel sequential strategy to identify the static nonlinear and linear dynamical parts of the model. Simulation results show that the Hammerstein model is an effective surrogate for the differential amplifier circuit that accurately and efficiently reproduces its behavior over a wide range of operating points and input frequencies.

通过数据驱动的系统识别技术，我们展示了一种有效的、非侵入性的模型阶数缩减方法，可用于微电子中的常用电路。通过使用规范的Hammerstein结构，我们开发了一个简约的非线性CMOS差分放大器模型，并通过一种新颖的顺序策略，在直流（DC）和瞬态Spice（Xyce）电路仿真数据上对该模型进行训练，以识别模型的静态非线性和线性动态部分。仿真结果表明，Hammerstein模型是差分放大器电路的有效替代品，能够准确高效地复现其在广泛的工作点和输入频率范围内的行为。

基于Hammerstein结构的电路非侵入式数据驱动模型降阶