大规模深度学习无损方法在4H-SiC材料表征中的实现

Q4 Physics and Astronomy
R. Leonard, Matthew Conrad, E. van Brunt, J. Witry, E. Balkas
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引用次数: 0

摘要

提出了一种用于工业大批量、非破坏性地表征150mm和200mm 4H-SiC晶圆扩展缺陷的整片方法。深度学习(DL)与非破坏性技术(NDT, DL-NDT)相结合,涉及高容量,快速光学显微镜方法,将工业公认的基于化学和物理的蚀刻和衍射技术用于缺陷表征。DL-NDT方法的应用显示了通过公认的蚀刻技术再现螺纹位错(TD),基面位错(BPD)和螺纹位错(TSD)扩展缺陷的缺陷分布。本文描述了算法开发的一个例子,以展示实现该方法的进展,以及DL-NDT缺陷密度与多个晶圆的蚀刻密度的比较。将该技术应用于大规模工业硅片生产的发展现状包括对结果进行蚀刻验证,以确保该技术的一致性和可靠性。使用这种非破坏性技术的能力最终将导致与器件行为更好的相关性,并为晶体生长过程提供反馈,以改善衬底晶圆,同时减少对蚀刻方法的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of Large Scale Deep Learning Non-Destructive Methods for Characterizing 4H-SiC Materials
A whole wafer method for industrial high volume, non-destructive characterizing of extended defects is demonstrated for 150 mm and 200 mm 4H-SiC wafers. Deep learning (DL) coupled with non-destructive techniques (NDT, DL-NDT) involving high volume, fast optical microscopy methods correlates industry accepted chemistry and physics-based etch and diffraction techniques for defect characterization. The application of the DL-NDT method is shown to reproduce defect distributions achieved by accepted etch techniques for extended defects of threading dislocations (TD), basal plane dislocations (BPD), and threading screw dislocations (TSD). An example of algorithm development is described to show progress toward implementing the method, as well as DL-NDT defect density compared to etch density for multiple wafers. The development status for implementing this technique for large-scale industrial wafer production includes etch validation of the results to ensure the technique is consistent and reliable. The ability to use this non-destructive technique ultimately will result in better correlation with device behavior and provide feedback to crystal growth processes to improve substrate wafers, while reducing the need for etch methods.
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来源期刊
Defect and Diffusion Forum
Defect and Diffusion Forum Physics and Astronomy-Radiation
CiteScore
1.20
自引率
0.00%
发文量
127
期刊介绍: Defect and Diffusion Forum (formerly Part A of ''''Diffusion and Defect Data'''') is designed for publication of up-to-date scientific research and applied aspects in the area of formation and dissemination of defects in solid materials, including the phenomena of diffusion. In addition to the traditional topic of mass diffusion, the journal is open to papers from the area of heat transfer in solids, liquids and gases, materials and substances. All papers are peer-reviewed and edited. Members of Editorial Boards and Associate Editors are invited to submit papers for publication in “Defect and Diffusion Forum” . Authors retain the right to publish an extended and significantly updated version in another periodical.
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