We demonstrate that 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 parsimonious Hammerstein models for a nonlinear CMOS differential amplifier and an operational amplifier circuit. We train these models on a combination of direct current (DC) and transient Spice circuit simulation data using a novel sequential strategy to identify their static nonlinear and linear dynamical parts. Simulation results show that the Hammerstein model is an effective surrogate for for these types of circuits that accurately and efficiently reproduces their behavior over a wide range of operating points and input frequencies.

翻译：我们证明，系统辨识技术可为微电子学中关键构建模块的常见电路提供有效的非侵入式模型降阶（MOR）基础。该方法受这些电路实际工作方式的启发，并采用规范的Hammerstein架构。为验证该方法，我们针对非线性CMOS差分放大器和运算放大器电路建立了简约的Hammerstein模型。通过结合直流（DC）和瞬态Spice电路仿真数据，采用新颖的顺序辨识策略训练这些模型，以确定其静态非线性部分和线性动态部分。仿真结果表明，Hammerstein模型能作为此类电路的有效代理模型，在宽泛的工作点和输入频率范围内精确高效地复现其行为特征。