Automated Socket Anomaly Detection through Deep Learning

Abstract

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.