计算机应用与软件2026，Vol.43Issue(6)：126-132,7.DOI:10.3969/j.issn.1000-386x.2026.06.018
基于图编码模型的元器件连接关系预测
PREDICTION OF ELECTRONIC CONNECTIVITY BASED ON GRAPH ENCODER MODELS
摘要
Abstract
Intelligently assisted analog integrated circuit design requires a large amount of circuit netlist annotation work.In this paper,we propose a netlist annotation method based on graph attention coding models.The method treated component ports as nodes in graph data,and established and trained the graph neural network SGL-WalkPool,which could quickly annotate the connection relationships between electronic components from images containing analog circuit schematics.In addition,we proposed the bypass structure SLG and the S-mish activation function to improve the graph encoder model.Experimental results show that the improved algorithm proposed in this paper achieves better performance than the comparison algorithms on both the custom dataset and the public dataset.关键词
图卷积神经网络/图注意力网络/深度学习/连接预测/模拟集成电路Key words
Graph convolutional neural network/Graph attention network/Deep learning/Link prediction/Analog integrated circuits分类
信息技术与安全科学引用本文复制引用
湛贤科,仝明磊..基于图编码模型的元器件连接关系预测[J].计算机应用与软件,2026,43(6):126-132,7.基金项目
国家自然科学基金项目(621051986). （621051986）
