Automated Socket Anomaly Detection through Deep Learning

被引：1

作者：

Agrawal, Nidhi ^{[1
]}

Yang, Min-Jian ^{[1
]}

Xanthopoulos, Constantinos ^{[2
]}

Thangamariappan, Vijayakumar ^{[1
]}

Xiao, Joe ^{[3
]}

Ho, Chee-Wah ^{[4
]}

Schaub, Keith ^{[2
]}

Leventhal, Ira ^{[1
]}

机构：

[1] Advantest Amer Inc, San Jose, CA 95134 USA

[2] Advantest Amer Inc, Austin, TX USA

[3] Essai Inc, Advantest Grp, Fremont, CA USA

[4] Essai Inc, Advantest Grp, Phoenix, AZ USA

来源：

2020 IEEE INTERNATIONAL TEST CONFERENCE (ITC) | 2020年

关键词：

D O I：

10.1109/ITC44778.2020.9325269

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The paper will demonstrate the application of Deep Learning (DL) for the detection of defective tester sockets. The proposed methodology relies on images like those used for manual or rule-based inspection, commonly collected using Automated Optical Inspection (AOI) equipment. This work represents a practical example of the use of Machine Learning for achieving improved inspection-quality outcomes at a lower cost. The experimental evaluation of the proposed methodology was performed on production set of collected socket images.

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页数：5

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