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.

翻译：我们证明，数据驱动的系统辨识技术可为微电子领域关键构建模块的常见电路提供有效的非侵入式模型降阶基础。该方法受这些电路实际工作方式的启发，采用规范的Hammerstein架构。为验证该方法，我们针对非线性CMOS差分放大器建立了简约的Hammerstein模型。通过结合直流（DC）和瞬态Spice（Xyce）电路仿真数据，采用新颖的顺序辨识策略分别确定模型的静态非线性部分与线性动态部分，对该模型进行训练。仿真结果表明，Hammerstein模型能作为差分放大器电路的有效代理模型，在宽泛的工作点与输入频率范围内精确高效地复现其行为特性。