Pub Date : 2024-08-21DOI: 10.1109/TSM.2024.3447074
Gwanjoong Kim;Ji-Won Kwon;Ingyu Lee;Hwiwon Seo;Jong-Bae Park;Jong-Hyeon Shin;Gon-Ho Kim
This study developed Plasma Information-based Virtual Metrology (PI-VM) to predict etching process results and analyze process phenomena. Using a dual-frequency capacitively coupled plasma (CCP) etcher with C4F8/Ar/O2 plasma, we etched low aspect ratio (AR) trench patterns in amorphous carbon layer (ACL) hard masks and $rm SiO_{2}$ molds, and developed the PI-VM statistically by integrating plasma information (PI) variables that reflect domain knowledge. The passivation effect of fluorocarbon plasma was analyzed by varying the gas ratios and the effect of ion energy was analyzed by changing the low frequency (LF) power. In the PI-VM results, the density ratios of the passivation precursor $rm CF_{2}$ to the etchant F and O were selected as key factors for predicting the process. The selection of radical density ratios as features confirmed the dominance of plasma chemistry in low AR etching. Demonstrating high predictive accuracy with minimal data, PI-VM offers significant potential to enhance the development of semiconductor process recipes.
本研究开发了基于等离子体信息的虚拟计量学（PI-VM），用于预测蚀刻工艺结果和分析工艺现象。我们使用带有 C4F8/Ar/O2 等离子体的双频电容耦合等离子体（CCP）刻蚀机，在非晶碳层（ACL）硬掩模和 $rm SiO_{2}$ 模具中刻蚀了低纵横比（AR）沟槽图案，并通过整合反映领域知识的等离子体信息（PI）变量，统计地开发了 PI-VM。通过改变气体比例分析了碳氟化合物等离子体的钝化效果，通过改变低频（LF）功率分析了离子能量的影响。在 PI-VM 结果中，钝化前驱体 $rm CF_{2}$ 与蚀刻剂 F 和 O 的密度比被选为预测过程的关键因素。选择自由基密度比作为特征证实了等离子化学在低 AR 蚀刻中的主导地位。PI-VM 只需极少的数据就能显示出很高的预测准确性，为加强半导体工艺配方的开发提供了巨大的潜力。
{"title":"Application of Plasma Information-Based Virtual Metrology (PI-VM) for Etching in C₄F₈/Ar/O₂ Plasma","authors":"Gwanjoong Kim;Ji-Won Kwon;Ingyu Lee;Hwiwon Seo;Jong-Bae Park;Jong-Hyeon Shin;Gon-Ho Kim","doi":"10.1109/TSM.2024.3447074","DOIUrl":"10.1109/TSM.2024.3447074","url":null,"abstract":"This study developed Plasma Information-based Virtual Metrology (PI-VM) to predict etching process results and analyze process phenomena. Using a dual-frequency capacitively coupled plasma (CCP) etcher with C4F8/Ar/O2 plasma, we etched low aspect ratio (AR) trench patterns in amorphous carbon layer (ACL) hard masks and \u0000<inline-formula> <tex-math>$rm SiO_{2}$ </tex-math></inline-formula>\u0000 molds, and developed the PI-VM statistically by integrating plasma information (PI) variables that reflect domain knowledge. The passivation effect of fluorocarbon plasma was analyzed by varying the gas ratios and the effect of ion energy was analyzed by changing the low frequency (LF) power. In the PI-VM results, the density ratios of the passivation precursor \u0000<inline-formula> <tex-math>$rm CF_{2}$ </tex-math></inline-formula>\u0000 to the etchant F and O were selected as key factors for predicting the process. The selection of radical density ratios as features confirmed the dominance of plasma chemistry in low AR etching. Demonstrating high predictive accuracy with minimal data, PI-VM offers significant potential to enhance the development of semiconductor process recipes.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"602-614"},"PeriodicalIF":2.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1109/TSM.2024.3444720
Jeongsub Choi;Youngdoo Son;Jihoon Kang
Group lasso is a regularization widely used for feature group selection with sparsity at a group level in machine learning. Training a model with the group lasso regularization, however, leads to the selection of all the groups together that are closely related to each other although their features are useful to predict a target. In this study, we propose a new regularization, group-exclusive group lasso, for automatic exclusive feature group selection. The proposed regularization aims to enforce exclusive sparsity at an inter-group level, discouraging the coincident selection of the feature groups that are group-level correlated and share predictive powers toward the targets. The proposed method aims at higher group sparsity for selecting salient feature groups only, and is applied to neural networks. We evaluate the proposed regularization in neural networks on synthetic datasets and a real-life case for virtual metrology with automatic sensor selection in semiconductor manufacturing.
{"title":"Group-Exclusive Feature Group Lasso and Applications to Automatic Sensor Selection for Virtual Metrology in Semiconductor Manufacturing","authors":"Jeongsub Choi;Youngdoo Son;Jihoon Kang","doi":"10.1109/TSM.2024.3444720","DOIUrl":"10.1109/TSM.2024.3444720","url":null,"abstract":"Group lasso is a regularization widely used for feature group selection with sparsity at a group level in machine learning. Training a model with the group lasso regularization, however, leads to the selection of all the groups together that are closely related to each other although their features are useful to predict a target. In this study, we propose a new regularization, group-exclusive group lasso, for automatic exclusive feature group selection. The proposed regularization aims to enforce exclusive sparsity at an inter-group level, discouraging the coincident selection of the feature groups that are group-level correlated and share predictive powers toward the targets. The proposed method aims at higher group sparsity for selecting salient feature groups only, and is applied to neural networks. We evaluate the proposed regularization in neural networks on synthetic datasets and a real-life case for virtual metrology with automatic sensor selection in semiconductor manufacturing.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"505-517"},"PeriodicalIF":2.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/TSM.2024.3442019
{"title":"Special Section Call for Papers: Bridging the Data Gap in Photovoltaics with Synthetic Data Generation","authors":"","doi":"10.1109/TSM.2024.3442019","DOIUrl":"https://doi.org/10.1109/TSM.2024.3442019","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 3","pages":"412-413"},"PeriodicalIF":2.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/TSM.2024.3444465
Gurpreet S. Lugani;Robert Skaggs;Bryan Morris;Tyler Tolman;Douglas Tervo;Stefan Uhlenbrock;Jon Hacker;Chye Seng Tan;James P. Nehlsen;Robert G. Ridgeway;Lois Wong Broadway;Francis P. Rudy
Plasma Dry-Etch (DE) is one of the key unit-operations in semiconductor manufacturing that use greenhouse gases (GHG) as feed gas (Donnelly and Kornblit, 2013). The exhaust GHG emission reduction or mitigation is one of the main focuses of scope 1 emission reduction at Micron Technology Inc. The reduction and mitigation approaches have been strategized in focus-tiers in order of proximity to the source of emissions. The focus-tiers upstream of exhaust are avoidance, replacement, reduction and downstream of exhaust are recovery/capture/recycle, abatement. This paper focuses on the replacement focus-tier that pertains to replacing high-emission feed gases (HE gas, feedgas that will produce relatively high kgCO2e through exhaust) with relatively low-emission feed gases (LE gas, feedgas that will produce relatively low kgCO2e through exhaust). The paper presents replacement opportunities and challenges through an evaluation study of Carbonyl Floride (COF2) as a replacement gas for NF3 or CF4 as a DE in-situ plasma chamber cleans gas. In conclusion, direct emissions from DE chamber cleans can be lowered by replacing NF3 and CF4 GHGs with COF2 by 90% or more. However, this replacement would require additional safety measures and abatement in operations due to increased toxicity and reactivity of COF2, along with cost roadmap to make its adoption economically feasible. Similar and possibly additional challenges would arise with other replacement options. To overcome challenges in replacement strategy focus-tier, it will require strong industry level collaboration between chemical suppliers, original equipment manufacturers (OEMs), device manufacturers, semiconductor research and collaboration centers and university research groups.
{"title":"Direct Emissions Reduction in Plasma Dry Etching Using Alternate Chemistries: Opportunities, Challenges, and Need for Collaboration","authors":"Gurpreet S. Lugani;Robert Skaggs;Bryan Morris;Tyler Tolman;Douglas Tervo;Stefan Uhlenbrock;Jon Hacker;Chye Seng Tan;James P. Nehlsen;Robert G. Ridgeway;Lois Wong Broadway;Francis P. Rudy","doi":"10.1109/TSM.2024.3444465","DOIUrl":"10.1109/TSM.2024.3444465","url":null,"abstract":"Plasma Dry-Etch (DE) is one of the key unit-operations in semiconductor manufacturing that use greenhouse gases (GHG) as feed gas (Donnelly and Kornblit, 2013). The exhaust GHG emission reduction or mitigation is one of the main focuses of scope 1 emission reduction at Micron Technology Inc. The reduction and mitigation approaches have been strategized in focus-tiers in order of proximity to the source of emissions. The focus-tiers upstream of exhaust are avoidance, replacement, reduction and downstream of exhaust are recovery/capture/recycle, abatement. This paper focuses on the replacement focus-tier that pertains to replacing high-emission feed gases (HE gas, feedgas that will produce relatively high kgCO2e through exhaust) with relatively low-emission feed gases (LE gas, feedgas that will produce relatively low kgCO2e through exhaust). The paper presents replacement opportunities and challenges through an evaluation study of Carbonyl Floride (COF2) as a replacement gas for NF3 or CF4 as a DE in-situ plasma chamber cleans gas. In conclusion, direct emissions from DE chamber cleans can be lowered by replacing NF3 and CF4 GHGs with COF2 by 90% or more. However, this replacement would require additional safety measures and abatement in operations due to increased toxicity and reactivity of COF2, along with cost roadmap to make its adoption economically feasible. Similar and possibly additional challenges would arise with other replacement options. To overcome challenges in replacement strategy focus-tier, it will require strong industry level collaboration between chemical suppliers, original equipment manufacturers (OEMs), device manufacturers, semiconductor research and collaboration centers and university research groups.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"445-452"},"PeriodicalIF":2.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/TSM.2024.3434277
{"title":"IEEE Transactions on Semiconductor Manufacturing Information for Authors","authors":"","doi":"10.1109/TSM.2024.3434277","DOIUrl":"https://doi.org/10.1109/TSM.2024.3434277","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 3","pages":"C3-C3"},"PeriodicalIF":2.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/TSM.2024.3429588
Jeanne Paulette Bickford;Delphine Le Cunff;Ralf Buengener;Stefan Radloff;Paul Werbaneth
As this Special Section goes to publication, semiconductor manufacturing in the United Status, and globally, continues to expand at a seemingly torrid pace. Assisted by government funding and driven in part by artificial intelligence workloads that gobble up increasing amounts of data center computing capacity, Intel and TSMC fabs are going up in Arizona, TI and Samsung fabs are coming to Texas, and Micron has big plans in New York. Unfortunately, just like those flying cars we were once promised, AI has not yet eliminated the need for the skilled trades and engineers required to build and successfully operate a fab. As a result, workforce development has become an important part of the increasingly complex semiconductor manufacturing process: Where are the thousands of engineers the semiconductor industry needs to staff these new fabs going to come from? How can we make more students excited about science and engineering? While the Guest Editors don’t have all the answers, we are happy that ASMC contributes to the solution by actively supporting student presentations and posters and annually recognizing the best student paper of the conference. And, maybe some day, the artificial intelligence systems that semiconductor manufacturing has enabled will give us those Star Wars or Star Trek robots that can build fabs and make chips too.
{"title":"Guest Editorial Special section on the 2023 SEMI Advanced Semiconductor Manufacturing Conference","authors":"Jeanne Paulette Bickford;Delphine Le Cunff;Ralf Buengener;Stefan Radloff;Paul Werbaneth","doi":"10.1109/TSM.2024.3429588","DOIUrl":"https://doi.org/10.1109/TSM.2024.3429588","url":null,"abstract":"As this Special Section goes to publication, semiconductor manufacturing in the United Status, and globally, continues to expand at a seemingly torrid pace. Assisted by government funding and driven in part by artificial intelligence workloads that gobble up increasing amounts of data center computing capacity, Intel and TSMC fabs are going up in Arizona, TI and Samsung fabs are coming to Texas, and Micron has big plans in New York. Unfortunately, just like those flying cars we were once promised, AI has not yet eliminated the need for the skilled trades and engineers required to build and successfully operate a fab. As a result, workforce development has become an important part of the increasingly complex semiconductor manufacturing process: Where are the thousands of engineers the semiconductor industry needs to staff these new fabs going to come from? How can we make more students excited about science and engineering? While the Guest Editors don’t have all the answers, we are happy that ASMC contributes to the solution by actively supporting student presentations and posters and annually recognizing the best student paper of the conference. And, maybe some day, the artificial intelligence systems that semiconductor manufacturing has enabled will give us those Star Wars or Star Trek robots that can build fabs and make chips too.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 3","pages":"225-228"},"PeriodicalIF":2.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/TSM.2024.3411140
{"title":"Call for Papers: Special Issue on Intelligent Sensor Systems for the IEEE Journal of Electron Devices","authors":"","doi":"10.1109/TSM.2024.3411140","DOIUrl":"https://doi.org/10.1109/TSM.2024.3411140","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 3","pages":"410-411"},"PeriodicalIF":2.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/TSM.2024.3442028
{"title":"IEEE EDS Robert Bosch Micro and Nano Electro Mechanical Systems Award: Call for Nominations","authors":"","doi":"10.1109/TSM.2024.3442028","DOIUrl":"https://doi.org/10.1109/TSM.2024.3442028","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 3","pages":"414-414"},"PeriodicalIF":2.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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