Research On Laser Chip Defect Detection Technology Based On Deep Convolutional Network

In recent years,China’s laser science and technology is in a stage of rapid development.In all aspects of social development,industrial economic improvement,national defense security applications and economic structural transformation,and national competitive development,the comprehensive innovation of laser technology and the transformation and development of industrial applications have very important strategic significance.Laser is the core of laser science and technology.However,in the production process of laser chips,damage defects will inevitably occur.Traditional inspection methods are timeconsuming,rely heavily on models,require extensive operating experience,and cannot locate defects.In this article,we mainly study the application of convolutional neural network in laser chip defect detection,and do the following work:First,mark and pre-process the collected laser chip defect images.By rotating,flipping,and miscutting the preprocessed defect image,a series of image processing operations are performed to artificially expand the data to construct the laser chip defect Image data set;Second,the combination of convolutional neural network and class-activated mapping technology to improve it based on the Alex Net model and obtain the Laser Chip Net network;Third,the use of laser chip defect image data after image preprocessing Set to train the Laser Chip Net network;Fourth,design three sets of experiments to verify the effect of image preprocessing on the accuracy of the model,and the model’s excellent performance in laser chip defect classification and recognition and defect area location.The final results show that the network model proposed in this paper has high classification accuracy,and can locate defect regions without using manual annotation at the regional level,and the classification and positioning functions can be realized in a network,which is very fast and convenient.It can be seen that the model proposed in this paper is of positive significance for the defect detection of laser chips.