Abstract
Automatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of preprocessing steps or a complex illumination system to improve the accuracy. This paper studies the effectiveness of using CNN to detect soldering bridge faults in a PCB assembly. The paper presents a method for designing an optimized CNN architecture to detect soldering faults in a PCBA. The proposed CNN architecture is compared with the state-of-the-art object detection architecture, namely YOLO, with respect to detection accuracy, processing time, and memory requirement. The results of our experiments show that the proposed CNN architecture has a 3.0% better average precision, has 50% less number of parameters and infers in half the time as YOLO. The experimental results prove the effectiveness of using CNN in AOI by using images of a PCB assembly without any reference image, any complex preprocessing stage, or a complex illumination system. Doi: 10.28991/HIJ-2022-03-01-01 Full Text: PDF
Highlights
A Printed Circuit Board (PCB) is a mechanical structure that holds and connects electronic components
This section compares the performance of the 6 Convolutional Neural Networks (CNN) architectures described in Appendix I with YOLO architecture based on the three important metrics, namely the accuracy of the model measured through Average Precision (AP) for soldering bridge fault, inference time and the number of learnable parameters in the model
As a rule of thumb, increasing the number of CONV layers increases the number of features learned, which in turn improves the accuracy of the CNN architecture, but only up to a certain number of layers [52]
Summary
A Printed Circuit Board (PCB) is a mechanical structure that holds and connects electronic components. Soldering is used to fix the electronic components in place on the PCB permanently by applying hot copper liquid onto a joint. After placing the electronic components onto the bare PCB it becomes a printed circuit board assembly (PCBA). With the development of technology, demand for electronic products to contain more features and be smaller in size has emerged. This demand has in turn caused the PCBA area to be smaller, more complex and denser. Manual Visual Inspection (MVI) has acted as the de facto test process for PCBA. Manual modes of inspection had a low reliability rate and were often affected by visual fatigue [3, 4]
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