R. Leonard, Matthew Conrad, E. van Brunt, J. Witry, E. Balkas
{"title":"大规模深度学习无损方法在4H-SiC材料表征中的实现","authors":"R. Leonard, Matthew Conrad, E. van Brunt, J. Witry, E. Balkas","doi":"10.4028/p-08c7e9","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":11306,"journal":{"name":"Defect and Diffusion Forum","volume":"426 1","pages":"3 - 9"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementation of Large Scale Deep Learning Non-Destructive Methods for Characterizing 4H-SiC Materials\",\"authors\":\"R. Leonard, Matthew Conrad, E. van Brunt, J. Witry, E. Balkas\",\"doi\":\"10.4028/p-08c7e9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":11306,\"journal\":{\"name\":\"Defect and Diffusion Forum\",\"volume\":\"426 1\",\"pages\":\"3 - 9\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Defect and Diffusion Forum\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4028/p-08c7e9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Physics and Astronomy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Defect and Diffusion Forum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4028/p-08c7e9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Physics and Astronomy","Score":null,"Total":0}
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.
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.