Comprehensive Mapping of Continuous/Switching Circuits in CCM and DCM to Machine Learning Domain using Homogeneous Graph Neural Networks

TL;DR

This paper introduces a universal graph-based representation for continuous and switching circuits in CCM and DCM, enabling effective machine learning applications in circuit analysis and design automation.

Contribution

It presents a generalized, mode-independent circuit-to-graph mapping method that facilitates ML-based classification and analysis of complex circuits with arbitrary components.

Findings

Achieved 97.37% accuracy in classifying continuous circuits

Achieved 100% accuracy in classifying switching circuits

Demonstrated applicability to various circuit types and converter modes

Abstract

This paper proposes a method of transferring physical continuous and switching/converter circuits working in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) to graph representation, independent of the connection or the number of circuit components, so that machine learning (ML) algorithms and applications can be easily applied. Such methodology is generalized and is applicable to circuits with any number of switches, components, sources and loads, and can be useful in applications such as artificial intelligence (AI) based circuit design automation, layout optimization, circuit synthesis and performance monitoring and control. The proposed circuit representation and feature extraction methodology is applied to seven types of continuous circuits, ranging from second to fourth order and it is also applied to three of the most common converters (Buck, Boost, and Buck-boost) operating in CCM or DCM. A classifier ML task can easily differentiate between circuit types as well as their mode of operation, showing classification accuracy of 97.37% in continuous circuits and 100% in switching circuits