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[2405.20178] Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures
Summary
This paper presents a non-intrusive data-driven model order reduction method for circuits using Hammerstein architectures, demonstrating its effectiveness with CMOS amplifiers.
Why It Matters
The research addresses the challenge of efficiently modeling complex circuits in microelectronics, which is crucial for optimizing design and performance. By leveraging data-driven techniques, this approach can enhance the accuracy and efficiency of circuit simulations, benefiting engineers and researchers in the field.
Key Takeaways
- Introduces a non-intrusive model order reduction technique for circuits.
- Utilizes Hammerstein architectures to accurately model nonlinear behaviors.
- Demonstrates effectiveness through simulations of CMOS differential amplifiers.
- Combines static nonlinear and linear dynamical modeling for comprehensive analysis.
- Offers a potential solution for improving circuit design efficiency in microelectronics.
Electrical Engineering and Systems Science > Systems and Control arXiv:2405.20178 (eess) [Submitted on 30 May 2024 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures Authors:Joshua Hanson, Paul Kuberry, Biliana Paskaleva, Pavel Bochev View a PDF of the paper titled Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures, by Joshua Hanson and 3 other authors View PDF 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. Comments: Subjects: Systems and Control (eess.SY); Machine Learning (cs...
