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Detecting Defects in PCB using Deep Learning via Convolution Neural Networks

In this paper we have deployed the concept of deep learning known as convolutional neural networks (CNN) as we can realize nowadays deep learning is growing in each and every field. Deep learning is executed in each and every platform and its outcome is impressive. On the other hand, the capability and accuracy of deep learning is somehow compared with human beings. We trained CNN to classify either defective or good printed circuit board (PCB). In this experiment we have used 41,387 images, which is divided into 3 different data sets i.e. training, validation and testing. The CNN, which has 60 million parameters and 500,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and two globally connected layers with a final 1000-way softmax. Hence, deep learning via convolution neural networks has been introduced in this paper, which will eventually increase the accuracy and reduce a lot of time and consumption of skilled manpower. According to this preliminary study, we can overall achieve accuracy of above 85% and minimize the count of defective PCB classifying as good. In the near future, we hope that over 95% accuracy can be achieved by using different CNN models like VGGNET, RESNET and GOOGLENET and collecting more PCB image data in order to reduce the consumption of time, manpower and increase the accuracy in quality inspection.