1. Chip Appearance Defect Recognition Based on Convolutional Neural Network
- Author
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Xiaomeng Zhou, Jingjing Wu, and Jun Wang
- Subjects
chip appearance defects ,Computer science ,Training time ,convolutional neural network ,Image processing ,TP1-1185 ,Interference (wave propagation) ,Biochemistry ,Convolutional neural network ,Article ,Analytical Chemistry ,Image Processing, Computer-Assisted ,Electrical and Electronic Engineering ,Instrumentation ,data cleaning ,business.industry ,Chemical technology ,pattern recognition ,Recognition, Psychology ,Pattern recognition ,Chip ,Atomic and Molecular Physics, and Optics ,Research Design ,Pattern recognition (psychology) ,Neural Networks, Computer ,Artificial intelligence ,Detection rate ,business ,Algorithms - Abstract
To improve the recognition rate of chip appearance defects, an algorithm based on a convolution neural network is proposed to identify chip appearance defects of various shapes and features. Furthermore, to address the problems of long training time and low accuracy caused by redundant input samples, an automatic data sample cleaning algorithm based on prior knowledge is proposed to reduce training and classification time, as well as improve the recognition rate. First, defect positions are determined by performing image processing and region-of-interest extraction. Subsequently, interference samples between chip defects are analyzed for data cleaning. Finally, a chip appearance defect classification model based on a convolutional neural network is constructed. The experimental results show that the recognition miss detection rate of this algorithm is zero, and the accuracy rate exceeds 99.5%, thereby fulfilling industry requirements.
- Published
- 2021
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