Visual and electrical approaches for automated verification of electronic components

Abstract

Reliable inspection and sorting of electronic components have become pivotal along the path of electronic manufacturing toward higher density and automation. Automated optical inspection systems at present depend on visual assessment methods because they do not include electrical testing capabilities, which results in a component verification reliability gap. In spite of recent advances, largely due to the fact that most automated optical inspection systems still abide by visual evaluation, verification of the actual electrical behavior of components became quite impossible. This paper is focused on bridging this gap through the introduction of a unified inspection framework whereby visual analysis is executed along with programmable electrical validation under a single automated process in conformity with Industry 4.0 practices. The system's synchronized workflow includes vision-based detection, optical character recognition, resistor color-band parsing, surface defect analysis, and electrical testing in real time. The component localization task uses YOLOv5, while EasyOCR with a convolutional neural network-long short-term memory (CNN-LSTM) structure and HSV-based segmentation delivers exact value extraction results. The testing system achieved 98.4% classification accuracy, 98.9% value recognition accuracy, and 94.9% overall sorting accuracy when tested on 3,000 photos and 400 physically inspected components at a throughput rate of seven components per minute.