Annotation of Analog Circuit Structures via Graph Attention Networks

doi:10.3969/j.issn.1006-2475.2025.06.016

Abstract

Abstract:
Abstract：Automated annotation of circuit structures can generate hierarchical representations of analog circuit networks， thereby advancing the development of automated analog circuit design tasks. This paper introduces a graph attention network-based model that transforms circuit netlists into graph structures， proposes a feature extraction strategy to learn and predict the circuit structures composed of nodes in the netlists， and presents a method for quickly generating a large number of SPICE circuit netlists to provide ample data for training the graph model. Experiments compared the recognition effects of graph convolutional networks， graph isomorphism networks， and GraphSAGE on the same dataset. The results show that the model outperforms the other models in accuracy， precision， and mean average precision， achieving 90.9%， 91.6%， and 91.9%， respectively. These results demonstrate the superiority of the model in capturing circuit connections， especially in terms of its effectiveness in processing complex circuit diagrams.

Key words: Key words: automated annotation, graph attention networks, circuit structure, feature extraction

CLC Number:

TP391

LI Xinpeng, TONG Minglei. Annotation of Analog Circuit Structures via Graph Attention Networks[J]. Computer and Modernization, 2025, 0(06): 101-105.

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