AI RESEARCH
KCLNet: Electrically Equivalence-Oriented Graph Representation Learning for Analog Circuits
arXiv CS.LG
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ArXi:2603.24101v1 Announce Type: new Digital circuits representation learning has made remarkable progress in the electronic design automation domain, effectively ing critical tasks such as testability analysis and logic reasoning. However, representation learning for analog circuits remains challenging due to their continuous electrical characteristics compared to the discrete states of digital circuits. This paper presents a direct current (DC) electrically equivalent-oriented analog representation learning framework, named \textbf{KCLNet