A Deep Learning-based Microsection Measurement Framework for Print Circuit Boards

Abstract

Microsectioning is a destructive testing procedure used in the printed circuit board (PCB) fabrication industry to evaluate the quality of PCBs. During cross-section analysis, operators measure PCB component widths manually, which can lead to inconsistencies and make it challenging to establish standardized procedures. We propose a Deep Learning-based Microsection Measurement (DL-MM) Framework for PCB microsection samples to address this issue. The framework comprises four modules: the target detection module, the image preprocessing module, the labeling model, and the coordinate adaptation module. The target detection module is responsible for extracting the area of interest to be measured, which reduces the influence of surrounding noise and improves measurement accuracy. In the image preprocessing module, the target area image is normalized, labeled with coordinates, and resized to different sizes based on the class. The labeling model utilizes a convolutional neural network (CNN) model trained separately for each class to predict its punctuation, as the number of coordinates varies for each class. The final module is the coordinate adaptation module, which utilizes the predicted coordinates to draw a straight line on the expected image for improved readability. In addition, we evaluate the proposed framework on two types of microsections, and the experimental results show that the measurements’ root-mean-square error (RMSE) is only 2.1 pixels. Our proposed framework offers a more efficient, faster, and cost-effective alternative to the traditional manual measurement method.