JOURNAL ARTICLE

Printed Circuit Board Defect Detection Using Generative Deep Learning Model

Jae‐Han ParkSoo Young Shin

Year: 2022 Journal:   2022 13th International Conference on Information and Communication Technology Convergence (ICTC) Pages: 463-466

Abstract

Recently, Printed Circuit Board(PCB) design of complex structures is essential to design small electronic devices. Defect detection is one of the most important PCB processes because PCB defects have a fatal effect on product quality. For previous defect detection, methods such as Automated Optical Inspection(AOI) or In-Circuit Test(ICT) were used. But these methods have their disadvantage. They need high-cost inspection equipment, and new setting values are required each time as the surrounding environment changes. The proposed system is robust to environmental changes using generative deep learning models. In addition, it is more convenient to use than existing deep learning models using semi-supervised learning.

Keywords:
Printed circuit board Deep learning Computer science Artificial intelligence Generative grammar Automated optical inspection Generative model Electronic product Engineering

Metrics

3
Cited By
1.19
FWCI (Field Weighted Citation Impact)
10
Refs
0.72
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Industrial Vision Systems and Defect Detection
Physical Sciences →  Engineering →  Industrial and Manufacturing Engineering
Currency Recognition and Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Neural Network Applications
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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