A Contactless PCBA Defect Detection Method: Convolutional Neural Networks With Thermographic Images

Abstract

In the mass production of electronic products, in-circuit-test (ICT) and printed circuit board assembly (PCBA) quality tests are performed. ICT measures resistance values and capacitance, but not only does it require the use of a fixture that is expensive and requires frequent replacement but also the fixture’s needles may cause PCBA defects. To overcome these limitations, various studies tried to replace ICT using visual inspection methods; however, visual inspection methods cannot be applied to chip resistors and chip capacitors that do not have externally visible characteristics. In this article, we propose a contactless inspection method that can detect PCBA defects without the use of the fixture and ICT by using the comparison of thermal images and deep learning (DL) analysis. We review the existing contactless inspection methods and compare them with our proposed thermal image analysis method. We analyzed thermal images by applying a structural similarity index map as a rule-based object detection method, and we used convolutional neural networks (CNNs), regions with CNN features, and an autoencoder as DL analysis methods. As a result, we achieved highly accurate defective component detection and location in real time.