Pub Date : 2020-08-01DOI: 10.1109/ASMC49169.2020.9185269
S. Goswami, S. Hall, W. Wyko, J. Elson, J. Galea, J. Kretchmer
This paper describes a data driven method to investigate in-line defect elevations, analyze root causes and thereafter, implement systematic improvements in manufacturing process and equipment. The problem described is an elevated random defect, observed post-patterning, and traced to incoming particulates in photoresist and/or spin-on dielectrics. The detailed analysis of inspection and defect metrology data leads to systematic diagnosis and improvement in the point-of-use photoresist filtration along with minimal downtime of the photoresist line. The methodology described is a good reference method for fab lines, when faced with similar ’special-cause’ defect problems that originate from incoming wet chemicals.
{"title":"In-line Photoresist Defect Reduction through Failure Mode and Root-Cause Analysis:Topics/categories: EO (Equipment Optimization)/ DR (Defect Reduction)","authors":"S. Goswami, S. Hall, W. Wyko, J. Elson, J. Galea, J. Kretchmer","doi":"10.1109/ASMC49169.2020.9185269","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185269","url":null,"abstract":"This paper describes a data driven method to investigate in-line defect elevations, analyze root causes and thereafter, implement systematic improvements in manufacturing process and equipment. The problem described is an elevated random defect, observed post-patterning, and traced to incoming particulates in photoresist and/or spin-on dielectrics. The detailed analysis of inspection and defect metrology data leads to systematic diagnosis and improvement in the point-of-use photoresist filtration along with minimal downtime of the photoresist line. The methodology described is a good reference method for fab lines, when faced with similar ’special-cause’ defect problems that originate from incoming wet chemicals.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88704212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/ASMC49169.2020.9185308
Holland M. Smith, C. Nicksic
optimized factory scheduling is a powerful technique for solving the problems of automated fab operations. Scheduling is generally more sophisticated and capable than older rule-based dispatch logic approaches for directing the minute-byminute processing priorities of semiconductor factories but requires greater computational power and a higher fidelity operations digital twin. One of the most important pieces of data a factory scheduler uses is throughput – the processing time required for a tool to run a specified recipe. While throughput data sets were formerly compiled from manual stopwatch studies, modern fab scales and volumes all but guarantee that comprehensive throughput data sets require automatic calculation based on event data from process tools. However, there are many potential data quality issues when automatically calculating throughput from tool events that can be difficult to detect systematically. In this paper we describe a statistical method for analyzing throughput data quality. The method reveals some common sources for noise in throughput data and reveals the importance of correct tool event interpretation.
{"title":"Improving Factory Scheduling with Statistical Analysis of Automatically Calculated Throughput","authors":"Holland M. Smith, C. Nicksic","doi":"10.1109/ASMC49169.2020.9185308","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185308","url":null,"abstract":"optimized factory scheduling is a powerful technique for solving the problems of automated fab operations. Scheduling is generally more sophisticated and capable than older rule-based dispatch logic approaches for directing the minute-byminute processing priorities of semiconductor factories but requires greater computational power and a higher fidelity operations digital twin. One of the most important pieces of data a factory scheduler uses is throughput – the processing time required for a tool to run a specified recipe. While throughput data sets were formerly compiled from manual stopwatch studies, modern fab scales and volumes all but guarantee that comprehensive throughput data sets require automatic calculation based on event data from process tools. However, there are many potential data quality issues when automatically calculating throughput from tool events that can be difficult to detect systematically. In this paper we describe a statistical method for analyzing throughput data quality. The method reveals some common sources for noise in throughput data and reveals the importance of correct tool event interpretation.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"56 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91285011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/ASMC49169.2020.9185201
Jacob Lohmer, Christian Flechsig, R. Lasch, K. Schmidt, Benjamin Zettler, G. Schneider
This contribution presents an industry case study as well as an analysis of the state-of-the-art in science concerning order release methods in wafer manufacturing in the semiconductor industry. The release of orders into the fab significantly influences critical parameters such as WIP, cycle time and throughput. We examine the processes currently applied in industry, indicate the effects of this order release approach on the performance of high-mix, high-volume fabs and establish a link to the analyzed scientific literature to develop a concept for meaningful automation of the release decision.
{"title":"Order Release Methods in Semiconductor Manufacturing: State-of-the-Art in Science and Lessons from Industry","authors":"Jacob Lohmer, Christian Flechsig, R. Lasch, K. Schmidt, Benjamin Zettler, G. Schneider","doi":"10.1109/ASMC49169.2020.9185201","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185201","url":null,"abstract":"This contribution presents an industry case study as well as an analysis of the state-of-the-art in science concerning order release methods in wafer manufacturing in the semiconductor industry. The release of orders into the fab significantly influences critical parameters such as WIP, cycle time and throughput. We examine the processes currently applied in industry, indicate the effects of this order release approach on the performance of high-mix, high-volume fabs and establish a link to the analyzed scientific literature to develop a concept for meaningful automation of the release decision.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"24 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90111776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/ASMC49169.2020.9185205
S. Mishra, E. Geiss, Aditya Kumar, A. Malinowski, Gao Wen Zhi, Wenhe Lin, B. Indajang, D. Slisher
This paper discusses major challenges faced in middle of line (MOL) manufacturing for FinFET technology. This throws light on major yield detractors for inline wafer yield as well as challenges involved at wafer sort. Since contact resistance is one of the critical parameters for device performance, it presents major challenges and resolutions for device enhancement due to a reduction in contact resistances. For FinFET, contact to poly pitch is very small, and as a result there were some reliability challenges in the initial development phase, the resolution of which is discussed in detail.
{"title":"Middle of Line: Challenges and Their Resolution for FinFET Technology","authors":"S. Mishra, E. Geiss, Aditya Kumar, A. Malinowski, Gao Wen Zhi, Wenhe Lin, B. Indajang, D. Slisher","doi":"10.1109/ASMC49169.2020.9185205","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185205","url":null,"abstract":"This paper discusses major challenges faced in middle of line (MOL) manufacturing for FinFET technology. This throws light on major yield detractors for inline wafer yield as well as challenges involved at wafer sort. Since contact resistance is one of the critical parameters for device performance, it presents major challenges and resolutions for device enhancement due to a reduction in contact resistances. For FinFET, contact to poly pitch is very small, and as a result there were some reliability challenges in the initial development phase, the resolution of which is discussed in detail.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"69 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79355839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/ASMC49169.2020.9185280
R. Mitra, A. Konuk
This paper reports the analytical methods used to detect the composition of residue on back-side of wafer. Further it discusses impact of this back-side residue on front-side of wafer flatness and how this residue was reduced by a new developed clean.
{"title":"Back-Side Residue Analyses and Reduction in FinFET Middle of Line Wafers","authors":"R. Mitra, A. Konuk","doi":"10.1109/ASMC49169.2020.9185280","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185280","url":null,"abstract":"This paper reports the analytical methods used to detect the composition of residue on back-side of wafer. Further it discusses impact of this back-side residue on front-side of wafer flatness and how this residue was reduced by a new developed clean.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"10 4 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79673266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/ASMC49169.2020.9185349
T. Alcaire, D. B. Cunff, V. Gredy, J. Tortai
Spectroscopic ellipsometry is a very sensitive metrology technique to accurately measure the thickness and the refractive index of the different layers present on specific dedicated metrology targets. In parallel, optical defectivity techniques are widely implemented in production lines to inspect a large number of dies and catch physical and patterning defects during the process flow. It becomes then of interest to explore a new approach overlapping metrology and defectivity by using the sensitivity of metrology tools on a full wafer scale. In our case, spectroscopic ellipsometry’s optical response was collected directly on the dies to capture specific deviations such as film properties and thickness variation. This is an innovative strategy that requires a model-less approach, combining an automatic ellipsometry mapping generation and a smart classification via a machine learning algorithm. In this paper, we will present such approach on two industrial use cases and explain how an image classification algorithm can be implemented to automatically detect the process drift on the latter.
{"title":"Spectroscopic Ellipsometry Imaging for Process Deviation Detection via Machine Learning Approach","authors":"T. Alcaire, D. B. Cunff, V. Gredy, J. Tortai","doi":"10.1109/ASMC49169.2020.9185349","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185349","url":null,"abstract":"Spectroscopic ellipsometry is a very sensitive metrology technique to accurately measure the thickness and the refractive index of the different layers present on specific dedicated metrology targets. In parallel, optical defectivity techniques are widely implemented in production lines to inspect a large number of dies and catch physical and patterning defects during the process flow. It becomes then of interest to explore a new approach overlapping metrology and defectivity by using the sensitivity of metrology tools on a full wafer scale. In our case, spectroscopic ellipsometry’s optical response was collected directly on the dies to capture specific deviations such as film properties and thickness variation. This is an innovative strategy that requires a model-less approach, combining an automatic ellipsometry mapping generation and a smart classification via a machine learning algorithm. In this paper, we will present such approach on two industrial use cases and explain how an image classification algorithm can be implemented to automatically detect the process drift on the latter.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"51 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86540405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/ASMC49169.2020.9185224
Nivedha Rajasekaran, Vikram Arjunwadkar, Richard F. Man
Semiconductor manufacturing (fab) is a highly complex, cost intensive and competitive industry. For a fab, batch factor for furnace tools is a key fab performance metric for capacity and cycle time assessment. Batch production is one of the manufacturing methods where, a group of wafers can be processed together in a batch in a given timeframe. Furnace tools have the ability to batch the wafers together. In this paper, we propose to establish the relationship between batch factor and cycle time to calculate the optimal batch factor within the permissible limits of cycle time. Most furnace tools need to wait for the same kind of wafers to improve its batch factor. Thus, to achieve large batch factor, the cycle time of the WIP at these furnace tools inadvertently becomes high. This creates a need to know how high batch factor can increase without adversely affecting the cycle time and help in making optimized batching decisions.
{"title":"Empirical Relationship between Cycle time Impact and Batching on Furnaces in Semiconductor Foundry","authors":"Nivedha Rajasekaran, Vikram Arjunwadkar, Richard F. Man","doi":"10.1109/ASMC49169.2020.9185224","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185224","url":null,"abstract":"Semiconductor manufacturing (fab) is a highly complex, cost intensive and competitive industry. For a fab, batch factor for furnace tools is a key fab performance metric for capacity and cycle time assessment. Batch production is one of the manufacturing methods where, a group of wafers can be processed together in a batch in a given timeframe. Furnace tools have the ability to batch the wafers together. In this paper, we propose to establish the relationship between batch factor and cycle time to calculate the optimal batch factor within the permissible limits of cycle time. Most furnace tools need to wait for the same kind of wafers to improve its batch factor. Thus, to achieve large batch factor, the cycle time of the WIP at these furnace tools inadvertently becomes high. This creates a need to know how high batch factor can increase without adversely affecting the cycle time and help in making optimized batching decisions.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"19 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73691087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/ASMC49169.2020.9185256
S. DeVries, E. D. De Silva, D. Canaperi, A. Simon, A. A. de la peña, Wei Wang, J. Maniscalco, Luciana Meli, B. Mendoza
Two sources of physical vapor deposition (PVD) titanium nitride (TiN) are compared for beyond 7 nm extreme ultraviolet (EUV) single expose patterning applications. The film density, stress, and grain size affect etch characteristics and refractive index affects lithography and overlay. It was learned that tuning and controlling the film characteristics using radio frequency physical vapor deposition (RFPVD) is critical to patterning applications beyond the 7 nm node.
{"title":"Comparing PVD Titanium Nitride Film Properties and their Effect on Beyond 7 nm EUV Patterning","authors":"S. DeVries, E. D. De Silva, D. Canaperi, A. Simon, A. A. de la peña, Wei Wang, J. Maniscalco, Luciana Meli, B. Mendoza","doi":"10.1109/ASMC49169.2020.9185256","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185256","url":null,"abstract":"Two sources of physical vapor deposition (PVD) titanium nitride (TiN) are compared for beyond 7 nm extreme ultraviolet (EUV) single expose patterning applications. The film density, stress, and grain size affect etch characteristics and refractive index affects lithography and overlay. It was learned that tuning and controlling the film characteristics using radio frequency physical vapor deposition (RFPVD) is critical to patterning applications beyond the 7 nm node.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"5 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75327267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-01DOI: 10.1109/ASMC49169.2020.9185229
Timothy H. Conway
An important consideration for APC feedback control of photolithography overlay is the uncertainty in estimating the correctable overlay parameters. This uncertainty is affected by the metrology sampling plan, particularly the spatial layout of the sampling points along with the number of sampling points. This current study looks at the combined effect of the spatial coverage and sample size factors, under two levels of random noise and a systematic non-correctable factor.
{"title":"Effect of Sparse or Asymmetric Sampling on the Estimation of Photolithography Overlay Regression Parameters","authors":"Timothy H. Conway","doi":"10.1109/ASMC49169.2020.9185229","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185229","url":null,"abstract":"An important consideration for APC feedback control of photolithography overlay is the uncertainty in estimating the correctable overlay parameters. This uncertainty is affected by the metrology sampling plan, particularly the spatial layout of the sampling points along with the number of sampling points. This current study looks at the combined effect of the spatial coverage and sample size factors, under two levels of random noise and a systematic non-correctable factor.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76577384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research compares the traditional machine learning algorithms and deep learning technology. We report our distributed computing convolutional neural network deep learning platform design and results in wafer defect classification. The result shows that the classification accuracy and purity performance is better than that of traditional machine learning models like Random Forest.
{"title":"Automated Wafer Defect Classification using a Convolutional Neural Network Augmented with Distributed Computing","authors":"Hairong Lei, Cho-Huak Teh, Hetong Li, Po-Hsuan Lee, Wei Fang","doi":"10.1109/ASMC49169.2020.9185253","DOIUrl":"https://doi.org/10.1109/ASMC49169.2020.9185253","url":null,"abstract":"This research compares the traditional machine learning algorithms and deep learning technology. We report our distributed computing convolutional neural network deep learning platform design and results in wafer defect classification. The result shows that the classification accuracy and purity performance is better than that of traditional machine learning models like Random Forest.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"9 9 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78647419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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