为太阳能电池制造过程的质量改进提供知识注入的过程监控

IF 2.6 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL Journal of Quality Technology Pub Date : 2021-10-20 DOI:10.1080/00224065.2021.1983491
Juan Du, Xi Zhang, Wei Ou
{"title":"为太阳能电池制造过程的质量改进提供知识注入的过程监控","authors":"Juan Du, Xi Zhang, Wei Ou","doi":"10.1080/00224065.2021.1983491","DOIUrl":null,"url":null,"abstract":"Abstract Solar conversion efficiency (SCE), an important quality metric in solar cell manufacturing processes, is related to chemical vapor deposition in the epitaxy stage based on the photoelectric effect. A large number of solar cell fabrication plants still lack online process monitoring strategies at the epitaxy stage and instead use offline inspections after fabrication is completed. Consequently, production efficiency is reduced due to offline inspections and the quality of wafers in downstream manufacturing stages is uncertain because only a small portion of wafers can be inspected due to random sampling within a single batch. A knowledge-infused monitoring strategy in the epitaxy stage of solar cell manufacturing processes that enables the direct link of online process monitoring to quality SCE is proposed in this study. A customized nonlinear model based on light interference with parameters that can be physically interpreted and largely accepted by practitioners is proposed to capture key information of reflectance signals. Multiple process features are extracted and a general correlation-based variable ranking procedure is adopted in this nonlinear model to rank SCE-correlated key process features. This model enables online process monitoring of key features at the epitaxy stage and allows practitioners to apply timely remedies in case of unexpected conditions. The proposed knowledge-infused process monitoring approach fully considers the physical knowledge from light interference and interpretability of parameters in the established nonlinear model correlated with the quality metric SCE to facilitate the online process monitoring at the epitaxy stage. A real solar cell manufacturing case shows the effectiveness of the proposed monitoring strategy.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"55 1","pages":"561 - 572"},"PeriodicalIF":2.6000,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Knowledge-infused process monitoring for quality improvement in solar cell manufacturing processes\",\"authors\":\"Juan Du, Xi Zhang, Wei Ou\",\"doi\":\"10.1080/00224065.2021.1983491\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Solar conversion efficiency (SCE), an important quality metric in solar cell manufacturing processes, is related to chemical vapor deposition in the epitaxy stage based on the photoelectric effect. A large number of solar cell fabrication plants still lack online process monitoring strategies at the epitaxy stage and instead use offline inspections after fabrication is completed. Consequently, production efficiency is reduced due to offline inspections and the quality of wafers in downstream manufacturing stages is uncertain because only a small portion of wafers can be inspected due to random sampling within a single batch. A knowledge-infused monitoring strategy in the epitaxy stage of solar cell manufacturing processes that enables the direct link of online process monitoring to quality SCE is proposed in this study. A customized nonlinear model based on light interference with parameters that can be physically interpreted and largely accepted by practitioners is proposed to capture key information of reflectance signals. Multiple process features are extracted and a general correlation-based variable ranking procedure is adopted in this nonlinear model to rank SCE-correlated key process features. This model enables online process monitoring of key features at the epitaxy stage and allows practitioners to apply timely remedies in case of unexpected conditions. The proposed knowledge-infused process monitoring approach fully considers the physical knowledge from light interference and interpretability of parameters in the established nonlinear model correlated with the quality metric SCE to facilitate the online process monitoring at the epitaxy stage. A real solar cell manufacturing case shows the effectiveness of the proposed monitoring strategy.\",\"PeriodicalId\":54769,\"journal\":{\"name\":\"Journal of Quality Technology\",\"volume\":\"55 1\",\"pages\":\"561 - 572\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2021-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Quality Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/00224065.2021.1983491\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Quality Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/00224065.2021.1983491","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 1

摘要

摘要太阳能转换效率(SCE)是太阳能电池制造过程中重要的质量指标,它与外延阶段基于光电效应的化学气相沉积有关。大量的太阳能电池制造厂在外延阶段仍然缺乏在线过程监控策略,而是在制造完成后使用离线检测。因此,由于离线检查,生产效率降低,下游制造阶段的晶圆质量不确定,因为在单个批次中随机抽样,只能检查一小部分晶圆。本研究提出了一种在太阳能电池制造过程外延阶段的知识注入监测策略,使在线过程监测与质量SCE直接联系起来。提出了一种基于光干涉的定制非线性模型,该模型具有可物理解释且被从业者广泛接受的参数,用于捕获反射信号的关键信息。该非线性模型提取了多个过程特征,并采用一种通用的基于相关性的变量排序方法对sce相关的关键过程特征进行排序。该模型支持在外延阶段对关键特性进行在线过程监控,并允许从业者在出现意外情况时及时采取补救措施。所提出的知识注入过程监测方法充分考虑了光干涉的物理知识和所建立的非线性模型中与质量度量SCE相关的参数的可解释性,便于在外延阶段进行在线过程监测。一个实际的太阳能电池制造案例表明了所提出的监测策略的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Knowledge-infused process monitoring for quality improvement in solar cell manufacturing processes
Abstract Solar conversion efficiency (SCE), an important quality metric in solar cell manufacturing processes, is related to chemical vapor deposition in the epitaxy stage based on the photoelectric effect. A large number of solar cell fabrication plants still lack online process monitoring strategies at the epitaxy stage and instead use offline inspections after fabrication is completed. Consequently, production efficiency is reduced due to offline inspections and the quality of wafers in downstream manufacturing stages is uncertain because only a small portion of wafers can be inspected due to random sampling within a single batch. A knowledge-infused monitoring strategy in the epitaxy stage of solar cell manufacturing processes that enables the direct link of online process monitoring to quality SCE is proposed in this study. A customized nonlinear model based on light interference with parameters that can be physically interpreted and largely accepted by practitioners is proposed to capture key information of reflectance signals. Multiple process features are extracted and a general correlation-based variable ranking procedure is adopted in this nonlinear model to rank SCE-correlated key process features. This model enables online process monitoring of key features at the epitaxy stage and allows practitioners to apply timely remedies in case of unexpected conditions. The proposed knowledge-infused process monitoring approach fully considers the physical knowledge from light interference and interpretability of parameters in the established nonlinear model correlated with the quality metric SCE to facilitate the online process monitoring at the epitaxy stage. A real solar cell manufacturing case shows the effectiveness of the proposed monitoring strategy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Quality Technology
Journal of Quality Technology 管理科学-工程:工业
CiteScore
5.20
自引率
4.00%
发文量
23
审稿时长
>12 weeks
期刊介绍: The objective of Journal of Quality Technology is to contribute to the technical advancement of the field of quality technology by publishing papers that emphasize the practical applicability of new techniques, instructive examples of the operation of existing techniques and results of historical researches. Expository, review, and tutorial papers are also acceptable if they are written in a style suitable for practicing engineers. Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days
期刊最新文献
SpTe2M: An R package for nonparametric modeling and monitoring of spatiotemporal data Spatial modeling and monitoring considering long-range dependence A change-point–based control chart for detecting sparse mean changes in high-dimensional heteroscedastic data Best practices for multi- and mixed-level supersaturated designs Sequential Latin hypercube design for two-layer computer simulators
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1