A Deep Learning-based Generic Solder Defect Detection System

Shi Qi Ye, Chen Sheng Xue, Cheng Yuan Jian, Yi Zhen Chen, Jia Jiun Gung, Chia Yu Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Automated optical inspection (AOI) is essential in the electronic manufacturing production line. Strict screening rules lead to a high false alarm rate of AOI. Many industries use AI models to classify defects. The lack of flawed data and the uneven distribution of categories is a big challenge for model training. Furthermore, the AI model must be retrained when adding new production line data, and the time cost is high. In order to reduce the false alarm rate and improve the generalization of the AI model, we build a deep learning- based generic solder defect detection system (GSDD) to classify defects into seven types. In GSDD, the color gradation adjustment module solves the problem of color difference, and the data augmentation module solves the problem of variable data. In the experiment, we use the data set provided by the enterprise to evaluate the accuracy of the model to 96%, and the model can be applied to different machines. Thus, GSDD is a general model and can efficiently detect defects.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages99-100
Number of pages2
ISBN (Electronic)9781665470506
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan
Duration: 6 Jul 20228 Jul 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Country/TerritoryTaiwan
CityTaipei
Period6/07/228/07/22

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