Defect Detection and Classification of Electronic Circuit Boards Using Keypoint Extraction and CNN Features

This paper proposes a method for defect detection and classification of electronic circuit board by extracting keypoints without reference images. The final purpose is to distinguish a problematic defect, such as disconnection from a non-defect, dust in the manufacturing process et al. Keypoints are extracted from the electronic circuit board image, then a patch image is cropped using obtained keypoint information, such as the position. The cropped images are used as input to CNN (Convolutional Neural Network) and 4096-dimensional features are obtained in the final layer of the full connected layers. SVM (Support Vector Machine) is introduced for learning and classification using CNN features. The effectiveness of the proposed method is confirmed through a detection experiment using actual electronic circuit board images containing defects and by comparing the results with the previous method. Keywords–Defect Detection; Defect Classification; CNN; SVM; SURF. I. I NTRODUCTION Electronic circuit boards are used as components of various precision instruments, such as computers and liquid crystal displays. Each layer is inspected after drawing and baking the mask pattern in the manufacturing process of the electronic circuit boards. There is Automated Optical Inspection (AOI) as a computer assisted automated visual inspection for circuit boards. The defect is judged from the loss rate of the lead wire portion in AOI, but the final goal is to determine if that defect is a true or a pseudo defect of the product. The inspections need to be done with high accuracy. The current AOI needs a subsequent final verification by the human eye to judge the existence of a defect. The human cost and variability of the inspection accuracy originating from individual checking ability are problems in the verification process. It is hoped to reduce this cost and to keep the accuracy for inspection with computer-aided defect inspection. Defect types during the inspection consist of true defect and pseudo defect. True defects include chipping, breaking, protrusions, shorts, etc. True defects cannot be shipped as the products when these defects are found. On the other hand, pseudo defects have foreign matter adherence and stains and these can be removed after inspection. So, pseudo defets can be shipped as the product. If a true defect is erroneously classified into a pseudo defect, it becomes a problem. If a pseudo defect is erroneously classified as a true defect, the product will be discarded. Normal products are disposed of when a pseudo defect is erroneously classified as a true defect, and it causes reduction of production yield rate. Papers [1] and [2] have been proposed to solve these problems using image processing. Paper [1] proposes a global defect inspection of defects by learning using Mahalanobis distance. Paper [2] supplies a current to the electronic circuit boards, and the defect is detected from the radiation position from the radiation infrared image by taking advantage of the characteristic that the short portion generates heat due to the leak current. The works [3] [4] have proposed the defect classification. Paper [3] classifies defect type using its shape informationunder the assumption that the reference image is used for the classification. Paper [5] detects a candidate region of defect by taking the difference between the reference image and the test image. Feature quantities are obtained from the candidate region and two classes classification of true defect and pseudo defect is proposed using SVM. Mutiple subsets are constructed by random sampling of the dataset,thenn multiple classifiers are constructed based on each subsets feature. The data classification is performed by taking a majorityvote, and the stable accuracy is obtained if the number of learningdata is sufficient. However, it is necessary to prepare the reference images under inspection. The creation of the reference image requires positioning in units of pixels, and it costs much to create a reference image for each inspection image. Paper [4] proposes a defect classification method using Bag-of-Features as a method without using a reference image, while this paper deals with AVI (Automatic Visual Inspection) which is available to the simpler patterns of electronic circuit boards. The method cannot be directly applied to AOI. This paper tries to improve the accuracy of detection and classification using features obtained by Convolutional Neural Network (CNN). The candidate defect region is extracted without reference image by keypoint extraction in defect classification, and features are extracted by inputting the cropped region into the CNN.