Real-Time Automated Socket Inspection using Advanced Computer Vision and Machine Learning : DI: Defect Inspection and Reduction

Abstract

Our test tools pick and place units into sockets for electrical testing. Defects or loose debris accumulated inside the test sockets will likely damage each subsequent unit being tested until the issue is detected and the defective socket is repaired or replaced. To resolve this critical issue, we equipped each pick-and-place arm with a new machine vision system designed to fit inside the existing tool. The limited footprint constraints required a highly compact imaging system which resulted in a variety of image artifacts, creating several unique challenges for the inspection system. We developed an inspection algorithm that utilizes a variety of advanced computer vision and machine learning techniques to normalize and match the images, remove artifacts, and detect defects. The flagged socket images can be manually dispositioned by the user and the socket can be sent for repair or cleaning as needed.