YOLOv4-Based Semiconductor Wafer Notch Detection Using Deep Learning and Image Enhancement Algorithms

IF 1.9 4区 工程技术 Q2 Engineering International Journal of Precision Engineering and Manufacturing Pub Date : 2024-08-16 DOI:10.1007/s12541-024-01092-7
Hao Wang, Hyo Jun Sim, Jong Jin Hwang, Sung Jin Kwak, Seung Jae Moon
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Abstract

This study designs a system to precisely detect the angle of wafers on an ion implanter's electrostatic chuck (ESC). In specific ion implantation processes, ions may penetrate deeper than intended because of the channeling effect, compromising the device performance. To address this issue, the system adjusts the tilt of the ESC and the twist angles of the wafer to control the ion beam direction. Utilizing a camera-based machine learning system, the system identifies the wafer notch to ensure an accurate alignment of the ESC. However, factors such as insufficient lighting and vibrations affect notch detection, which can degrade image quality. To overcome these issues, this study explored various image-enhancement techniques and evaluated the performance of object detection algorithms on enhanced images.

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利用深度学习和图像增强算法进行基于 YOLOv4 的半导体晶片缺口检测
本研究设计了一种系统,用于精确检测离子注入机静电夹头(ESC)上晶片的角度。在特定的离子注入过程中,由于通道效应,离子可能会穿透得比预期更深,从而影响设备性能。为了解决这个问题,该系统调整了静电吸盘的倾斜度和晶片的扭曲角度,以控制离子束的方向。利用基于摄像头的机器学习系统,该系统可识别晶片缺口,以确保对准 ESC。然而,照明不足和振动等因素会影响缺口检测,从而降低图像质量。为了克服这些问题,本研究探索了各种图像增强技术,并评估了增强图像上物体检测算法的性能。
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来源期刊
CiteScore
4.10
自引率
10.50%
发文量
115
审稿时长
3-6 weeks
期刊介绍: The International Journal of Precision Engineering and Manufacturing accepts original contributions on all aspects of precision engineering and manufacturing. The journal specific focus areas include, but are not limited to: - Precision Machining Processes - Manufacturing Systems - Robotics and Automation - Machine Tools - Design and Materials - Biomechanical Engineering - Nano/Micro Technology - Rapid Prototyping and Manufacturing - Measurements and Control Surveys and reviews will also be planned in consultation with the Editorial Board.
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