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Far-end chip defect detection system based on deep learning technology

A chip defect and deep learning technology, applied in image data processing, instruments, biological neural network models, etc., can solve the problems of incapable chip defect classification and classification, so as to improve image processing capacity, improve accuracy, and facilitate acquisition. Effect

Pending Publication Date: 2021-12-31
中科海拓(无锡)科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The main purpose of the present invention is to provide a remote chip defect detection system based on deep learning technology. Through the algorithm of the software system, a method for classifying chip defect images is proposed, which solves the problem that the existing chip defect IC detection equipment can only The quality of the chip is detected, but the chip defect cannot be divided into categories and grades. At the same time, the standard data set including all defect categories can be obtained by manual marking, which can improve the accuracy of detection

Method used

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  • Far-end chip defect detection system based on deep learning technology
  • Far-end chip defect detection system based on deep learning technology

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Embodiment 1

[0046] A remote chip defect detection system based on deep learning technology, including hardware system, software system and training module, the hardware system includes network service equipment, image storage equipment, database and load balancer, the software system includes image processing software, algorithm processing Software and server terminal platform software, the detection process of the detection system includes the following steps:

[0047] Step 1. Through the network service equipment, use 5G network transmission for docking, obtain the picture to be inspected of the existing chip defect, and store the picture to be inspected in the image storage device, start the defect test software, and start reading after entering the SN code of the test software Chip defect image information in the image storage folder to be inspected;

[0048] Step 2: Use image processing software to preprocess the chip defect image; preprocessing includes cutting the multi-chip defect...

Embodiment 2

[0061] A remote chip defect detection system based on deep learning technology, including hardware system, software system and training module, the hardware system includes network service equipment, image storage equipment, database and load balancer, the software system includes image processing software, algorithm processing Software and server terminal platform software, the detection process of the detection system includes the following steps:

[0062] Step 1. Through the network service equipment, use 5G network transmission for docking, obtain the picture to be inspected of the existing chip defect, and store the picture to be inspected in the image storage device, start the defect test software, and start reading after entering the SN code of the test software Chip defect image information in the image storage folder to be inspected;

[0063] Step 2, using image processing software to preprocess the chip defect image;

[0064] Step 3, perform manual labeling to obtai...

Embodiment 3

[0076] A remote chip defect detection system based on deep learning technology, including hardware system, software system and training module, the hardware system includes network service equipment, image storage equipment, database and load balancer, the software system includes image processing software, algorithm processing Software and server terminal platform software, the detection process of the detection system includes the following steps:

[0077] Step 1. Through the network service equipment, use 5G network transmission for docking, obtain the picture to be inspected of the existing chip defect, and store the picture to be inspected in the image storage device, start the defect test software, and start reading after entering the SN code of the test software Chip defect image information in the image storage folder to be inspected;

[0078] Step 2, using image processing software to preprocess the chip defect image;

[0079] Step 3, perform manual labeling to obtai...

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Abstract

The invention discloses a far-end chip defect detection system based on deep learning technology, which comprises a hardware system, a software system and a training module, and is characterized in that the hardware system comprises network service equipment, image storage equipment, a database and a load balancer; the software system comprises image processing software, algorithm processing software and server terminal platform software. According to the far-end chip defect detection system based on the deep learning technology, through an algorithm of a software system, a chip defect image grading method is provided, and the problems that existing chip defect IC detection equipment can only detect the quality of a chip, but cannot classify and grade the chip defects is solved, a standard data set including all defect categories is obtained through manual marking, the detection accuracy can be improved, meanwhile, the system can process a large number of defect images in unit time, and the detection speed is improved.

Description

technical field [0001] The invention relates to the field of chip defect detection, in particular to a remote chip defect detection system based on deep learning technology. Background technique [0002] The existing chip defect detection system is to first take pictures of the chip through the optical vision scanning mechanism to obtain high-quality chip surface images, and then analyze the chip defects through algorithms after processing the images, and because IC testing equipment can only detect The chip is good or bad, but it cannot be classified and graded, and the existing equipment is detected online in real time, and the processing amount of images per unit time is small. When the amount of chip images is large, it is difficult to process a large number of images. processing, which leads to low processing efficiency and is difficult to meet production needs. Therefore, we propose a remote chip defect detection system based on deep learning technology. Contents of ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/13G06T7/136G06T7/62G06T7/66G06K9/32G06K9/46G06K9/62G06N3/02G06T3/60G06T5/20
CPCG06T7/0004G06T3/608G06T7/136G06T7/11G06T7/13G06T5/20G06T7/62G06T7/66G06N3/02G06T2207/10004G06T2207/20032G06T2207/20081G06T2207/20084G06T2207/20116G06T2207/30148G06F18/24G06F18/214
Inventor 程坦邹爱刚刘涛汪玮
Owner 中科海拓(无锡)科技有限公司
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