中图分类号：TN707文献标识码：ADOI:10.19339/j.issn.1674-2583.2026.07.005
中文引用格式：李雪洁. 基于深度学习的电子电路故障诊断技术分析［J］.集成电路应用，2026，43（7）：24-28.
英文引用格式：Li Xuejie. Analysis of electronic circuit fault diagnosis technology based on deep learning［J］.Application of IC，2026，43（7）：24-28.

Analysis of electronic circuit fault diagnosis technology based on deep learning

Abstract： Electronic circuit fault diagnosis faces core challenges such as concealed and ambiguous fault manifestations and complex system coupling, making traditional methods ineffective for efficient localization. Deep learning technology constructs deep neural network models to autonomously learn feature patterns from multimodal fault information, including voltage signals, current data, and circuit images, thereby overcoming reliance on manual experience. By leveraging key technologies such as multimodal data acquisition and preprocessing, the construction of automatic deep feature extraction networks, and fault type identification and localization, this approach achieves precise capture and real-time analysis of fault characteristics, significantly enhancing diagnostic efficiency and accuracy, and providing reliable technical support for troubleshooting complex electronic circuits.

Key words : deep learning; electronic circuit; fault diagnosis technology