Data-driven topology design for conductor layout problem of electromagnetic interference filter

TL;DR

This paper introduces a data-driven topology design method using deep generative models to optimize conductor layouts in EMI filters, aiming to improve electromagnetic noise reduction performance.

Contribution

It proposes a novel data-driven topology design approach for EMI filters that overcomes traditional topology optimization challenges, with a focus on maintaining circuit topology during optimization.

Findings

Numerical examples demonstrate the effectiveness of the proposed method.

The approach successfully maintains circuit topology during design optimization.

The method shows potential for significant performance improvements in EMI filters.

Abstract

Electromagnetic interference (EMI) filters are used to reduce electromagnetic noise. It is well known that the performance of an EMI filter in reducing electromagnetic noise largely depends on its conductor layout. Therefore, if a conductor layout optimization method with a high degree of freedom is realized, a drastic performance improvement is expected. Although there are a few design methods based on topology optimization for this purpose, these methods have some difficulties originating from topology optimization. In this paper, we therefore propose a conductor layout design method for EMI filters on the basis of data-driven topology design (DDTD), which is a high degree of freedom structural design methodology incorporating a deep generative model and data-driven approach. DDTD was proposed to overcome the intrinsic difficulties of topology optimization, and we consider it suitable for the conductor layout design problem of EMI filters. One significant challenge in applying DDTD to the conductor layout design problem is maintaining the topology of the circuit diagram during the solution search. For this purpose, we propose a simple yet efficient constraint. We further provide numerical examples to confirm the usefulness of the proposed method.