Classification of Analog Circuits Based on Graph Convolution Network

Zheng, Zhi-Wei; Zhang, Xiongbo; Wang, Yuefan; He, Shan; Huang, Chao; Li, Lin; Guo, Donghui

doi:10.1109/asid56930.2022.9996007

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2024

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“…Previous studies have been conducted in collaboration with semiconductor manufacturers. The only publicly available datasets comprise only 60 circuits [31], which are insufficient for 2) applied to circuit constants…”

Section: Experiments a Datasetmentioning

confidence: 99%

Circuit2Graph: Circuits With Graph Neural Networks

Yamakaji,

Shouno,

Fukushima

2024

IEEE Access

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Circuit design requires trial and error in both prototyping and simulation owing to the high degrees of freedom and mutual interference between the components. In this study, we propose a novel approach to address this challenge by introducing a transformation method that converts circuits into graph networks. This transformation is achieved with no loss of information, and we evaluate its accuracy through the application of graph classification by utilizing graph neural networks. We assume that the information degradation can result from self-loops, multi-edges, and heterogeneous graphs. To mitigate these issues, we propose a method that effectively reduces their impact. The results of this study demonstrate the effectiveness of our proposed method, as it achieves an accuracy of 97.89%. This represents a significant improvement of 5.2% when compared with the conventional method. Notably, our proposed method is applicable to generalpurpose circuits. This makes it a valuable addition to the existing repertoire of circuit solution methods, alongside analytical and simulation approaches.

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Section: Experiments a Datasetmentioning

confidence: 99%