Abstract
With the coming of the 4th industrial revolution era, manufacturers produce high-tech products. As the production process is refined, inspection technologies become more important. Specifically, the inspection of a printed circuit board (PCB), which is an indispensable part of electronic products, is an essential step to improve the quality of the process and yield. Image processing techniques are utilized for inspection, but there are limitations because the backgrounds of images are different and the kinds of defects increase. In order to overcome these limitations, methods based on machine learning have been used recently. These methods can inspect without a normal image by learning fault patterns. Therefore, this paper proposes a method can detect various types of defects using machine learning. The proposed method first extracts features through speeded-up robust features (SURF), then learns the fault pattern and calculates probabilities. After that, we generate a weighted kernel density estimation (WKDE) map weighted by the probabilities to consider the density of the features. Because the probability of the WKDE map can detect an area where the defects are concentrated, it improves the performance of the inspection. To verify the proposed method, we apply the method to PCB images and confirm the performance of the method.
Highlights
Because the era of the Internet of Things (IoT) has been accompanied by the rapid development of the semiconductor industry and communication technologies, the use of high-tech products, such as mobile phones and wearable devices, has been spreading widely in our daily lives
Based on the concept of a learning property, we propose a new non-referential method for fault detection by extracting features based on an image-processing method and learning the fault information using random forests
Random forests is a classification method proposed by Breiman and is a type of ensemble learning method that constructs a multitude of decision trees and combines the predictions from method that constructs a multitude of decision trees and combines the predictions from them [20]
Summary
Because the era of the Internet of Things (IoT) has been accompanied by the rapid development of the semiconductor industry and communication technologies, the use of high-tech products, such as mobile phones and wearable devices, has been spreading widely in our daily lives. When extracting the features of a PCB image, this method uses the maximum value of the correlation coefficients between the features of the reference image and the inspected image This approach of applying machine-learning algorithms is called the learning-based model. Morphological techniques do not require a predefined model of a perfect pattern because we can construct specific shapes in an image by choosing an appropriate neighborhood shape [10] This method has a disadvantage in that we should apply different pre-processing algorithms to check for faults in a PCB. The proposed method first extracts robust features using an image-processing technique and learns the fault pattern using an efficient classification technique utilizing high-dimensional data.
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