Open AccessOn the Determination of the Chip Nozzle Recognition System by Using Machine Vision
Author(s) -
Jing Qiu,
Yun Zhao Xu,
Siyi Liu
Publication year - 2021
Publication title -
frontiers in business, economics and management
Language(s) - English
Resource type - Journals
ISSN - 2766-824X
DOI - 10.54097/fbem.v1i3.21
Subject(s) - nozzle , rectangle , backlight , sorting , artificial intelligence , computer vision , machine vision , computer science , feature (linguistics) , mechanical engineering , mathematics , engineering , geometry , algorithm , liquid crystal display , operating system , linguistics , philosophy
To solve the problem of chip damage caused by the using the wrong type of vacuum nozzle during the packaging of semiconductor chips. A recognition system of vacuum nozzle based on machine vision was proposed. In this research, 29 kinds of lifting nozzles are selected as test samples. The backlight intensity of two lifting nozzle images (one strong and one weak separately) is collected at the first beginning. Then, the Blob analysis method is using to analyze the weak backlighting image. The area of the lifting nozzle and the minimum outer rectangular feature can be obtained subsequently. To identify the shape of the liftin nozzle (round or square), the area ratio is calculated. At the same time, the minimum outer rectangular of the lifting nozzle is selected as the reference rectangle. Then, construct the measurement rectangle. The 2-dimensional size of the lifting nozzle is measured as well. Meanwhile, for the strong backlight image, the average value of the grayscale which located within the minimum outer rectangle is calculated. Therefore, the color (black, white, or beige) of the nozzle can be identified. Finally, the sample data is saved to the database as the sample database. During the recognition process, the shape, color, and size of the lifting nozzle being analyzing are using as the parameter to realize the condition inquire. The experimental results show that the recognition accuracy of this method is 98.85%, and the recognition time of one nozzle is around 1 second, which meets the requirements of practical application.