Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures

TL;DR

This paper introduces a non-intrusive, data-driven model order reduction method for circuits using Hammerstein architectures, effectively modeling nonlinear and linear dynamics from simulation data.

Contribution

It presents a novel sequential system identification approach to develop parsimonious Hammerstein models for key electronic circuits, improving efficiency and accuracy.

Findings

Accurately reproduces circuit behavior over various operating points

Efficiently models nonlinear and linear dynamics from simulation data

Demonstrates effectiveness on CMOS differential and operational amplifiers

Abstract

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.