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Surface Inspection｜Corerain x The Domestic Microelectronics Group

Home> Cases>The Domestic Microelectronics Group
Introduction Challenges Solutions Results
Introduction

As one of the largest manufacturers of lithography machine in China, the microelectronics group is mainly dedicated to the development, design, manufacture, sales and technical services of semiconductor equipment, pan-semiconductor equipment and high-end intelligent equipment.


Challenges
Insufficient Amount of Defect Data
Insufficient Amount of Defect Data
Some defect samples are small in number and not easy to collect in the short term, and there is a lack of effective public data sets in the industry.
High Problem Complexity
High Problem Complexity
The diversity of wafer defect types is high and the imaging of the same defect is affected by various factors such as process, batch, and lighting conditions of AOI.
High Requirements of Real-time Computation
High Requirements of Real-time Computation
AOI has a high imaging resolution, and the complexity of image computation is positively correlated with the image resolution.
Solutions

In response to the complicated problems of wafer surface defect detection, Corerain provides a hybrid solution based on deep learning algorithms and supplemented by traditional computer vision algorithms to integrate the detection, classification, regression  and recognition algorithms of the target. Combined with the self-developed AI accelerators with low latency, high performance and high cost-efficiency, an integrated AI machine vision wafer inspection solution is developed by Corerain.

Results
7*24 all-weather security analysis, alert in seconds and rapid positioning of problem floors are realized throught AI.
Identify defects such as wafer overdisplay, tarnish, probe, residue and scuff with an algorithm accuracy over 99%.
Achieve accurate and efficient full-line inspection and classification, effectively shorten the product development cycle and reduce production cost by 50% for companies.
Achieve accurate and efficient full-line inspection and classification, effectively shorten the product development cycle and reduce production cost by 50% for companies.
Relevant Cases
The Research Institute in Shandong
Smart Park
The Research Institute in Shandong
Corerain AI video analysis platform realizes the functions of visitors management, recognition of smoking, personnel wandering, personnel falling down, and passenger flow counting, all-day warning alert in seconds, and quick positioning of problem floors.The platform accurately identifies the flow direction of people, provides intelligent operation and management with visual basis for decision-making, improving the security performance and management efficiency of the park.
大型钢铁集团
Smart Metallurgy
The Large Steel Group
Through Corerain's video system to acheive smart upgrade, combined with deep learning algorithms, it identifies equipment operating status and gives warning of potential accidents, and makes centralized visualization of safety supervision for the key areas within the plant, improving the intelligent management level and reducing the incidence of accidents with technical assurance.
The National Mining Group
Smart Mining
The National Mining Group
Corerain uses image recognition algorithm technology based on deep learning to detect scenarios such as mixed human and machine operations in the production process, intrusion of people around the earth-discharge vehicles and cracks occurring on the ground in real time, and to link the sound and light alarms on site for real-time warning to realize supervision automation and guarantee the safety of the production process.
The Famous Beer Group
Smart Factory
The Famous Beer Group
Corerain provides a complete accelerator-algorithm-platform integration solution, and uses AI video intelligent analysis platform to realize automatic alarming of environmental status and safety operations, timely detection of hidden dangers, and compatibility with the existing equipment, greatly saving manpower and equipment cost, improving safety management from extensive to intensive mode.

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