Work

Optimizing Analog Circuit Design Through a Machine Learning‐Assisted Evolutionary Algorithm

Year: 2025

Type: article

Abstract: ABSTRACT Evolutionary algorithms (EAs) based on circuit simulation are widely employed for analogue circuit sizing because of their high accuracy and adaptability in various cases. However, most of the existing research is focused on a limited set of analogue integrated circuit design specifications. When addressing a complete specification set, the extensive number of simulations required becomes impractical for large circuits. Recent studies incorporating machine learning (ML) techniques have accelerated the optimization process but still involve high simulation costs. This paper proposes an improved and efficient ML‐assisted evolutionary algorithm for analogue circuit sizing. The proposed approach integrates a machine learning model into the EA optimization process, effectively reducing the number of required simulations and improving the convergence speed. The experimental results demonstrate the efficiency of the proposed methodology in achieving reliable optimization, with a significant reduction in simulation cost and improved convergence.

Source: Electronics Letters

DOI: 10.1049/ell2.70331

Authors: Yu‐Yu Chen, Shao‐Yun Fang

Institution: National Taiwan University of Science and Technology

Language: English

Topic: Evolutionary Algorithms and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,590

Funder: National Science and Technology Council