Research On Semiconductor Surface Character Quality Detection System Based On Machine Vision

The continuous innovation in the high-tech industry has promoted the development of semiconductor devices in a variety of and miniaturized directions.Manufacturers mark the quality of products by marking logo characters on the surface of semiconductor products to better manage the products,while traditional character recognition mainly relies on Manually completed,not only the recognition efficiency is low,but also the labor cost is high,so the automatic recognition of characters based on machine vision came into being.In order to meet the requirements of automated production,this paper takes workshop production as the background and takes the printed characters on the surface of the chip as the object to study the semiconductor surface character quality detection system based on machine vision.This system can reduce manual participation and improve the recognition accuracy and efficiency of the system.Realizing the automatic recognition of characters of semiconductor products has important practical value.The system research content is determined according to the production needs and the chip to be tested,which mainly includes three parts:mechanical platform design and construction,system software design and image processing.Determine the type of hardware equipment through the mechanical design plan,and complete the construction of the mechanical structure of the semiconductor surface character quality detection system.The system control software is realized based on the Microsoft Visual C++ 6.0 compilation environment,and the real-time control of the system is realized.The point of the system lies in the effective processing of the collected images.In terms of image processing,the image is first smoothed,and the three smoothing algorithms are used to process the image by analyzing the characteristics of the character surface noise,and the optimal smoothing algorithm is selected.Secondly,in terms of character segmentation algorithm research,firstly,the edge detection-based and projection-based segmentation algorithms are researched and analyzed.By comparing the character characteristics of the chip surface,an improved projection segmentation algorithm is proposed.The experimental results show that the algorithm can quickly and accurately realize the character Accurate segmentation.Finally,in the research of character recognition algorithms,the traditional template matching algorithm is studied,and based on the shortcomings of the algorithm,a matching algorithm based on the distance between two characters is proposed to achieve a better recognition effect;the basic convolutional neural network algorithm model is studied.To improve the network according to practical problems,to achieve the recognition of target characters.At the same time,the use of residual network migration learning to achieve the classification of characters.The 30*30 chip was used as the standard for testing on the construction platform,and two different algorithms,template matching and neural network,were used to perform target recognition on the characters to be tested.The recognition accuracy rates were 87.9%and 94.6%,respectively.The experimental results show that the comparison For template matching,the neural network algorithm has a good application effect and meets the actual production requirements.