Autoencoder-Augmented Graph Neural Networks for Accurate and Scalable Structure Recognition in Analog/Mixed-Signal Schematics

The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, where data scarcity and confidentiality constraints limit model training. In this work, a novel framework has be...

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Bibliografiske detaljer

Principais autores:	Mohamed Salem, Witesyavwirwa Vianney Kambale, Ali Deeb, Sergii Tkachov, Anjeza Karaj, Joachim Pichler, Manuel Ludwig Lexer, Kyandoghere Kyamakya
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2025-01-01
Serier:	IEEE Access
Fag:	Analog/mixed-signal schematics graph neural networks graph attention networks autoencoders data augmentation circuit recognition
Online adgang:	https://ieeexplore.ieee.org/document/11088082/
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