Pub Date : 2022-12-12DOI: 10.1109/ISSM55802.2022.10026898
Masakazu Tanuma, Joong‐Won Shin, S. Ohmi
In this research, deposition rate dependence of 5 nm-thick ferroelectric nondoped HfO2 (FeND-HfO2) on the device characteristics was investigated. The equivalent oxide thickness (EOT) and leakage current were decreased by increasing deposition rate of HfO2 from 5.0 nm/min to 6.0 nm/min. The subthreshold swing (SS) of 107 mV/dec. and saturation mobility (μsat) of 150 cm2/(Vs) were obtained with deposition rate of 6.0 nm/min. Furthermore, the threshold voltage (VTH) was controllable as the number of identical erase pulse of 4 V/1 μs was increased, which suggested the VTH control of approximately 10 mV.
{"title":"Deposition rate dependence of the 5 nm-thick ferroelectric nondoped HfO2 on MFSFET characteristics","authors":"Masakazu Tanuma, Joong‐Won Shin, S. Ohmi","doi":"10.1109/ISSM55802.2022.10026898","DOIUrl":"https://doi.org/10.1109/ISSM55802.2022.10026898","url":null,"abstract":"In this research, deposition rate dependence of 5 nm-thick ferroelectric nondoped HfO2 (FeND-HfO2) on the device characteristics was investigated. The equivalent oxide thickness (EOT) and leakage current were decreased by increasing deposition rate of HfO2 from 5.0 nm/min to 6.0 nm/min. The subthreshold swing (SS) of 107 mV/dec. and saturation mobility (μsat) of 150 cm2/(Vs) were obtained with deposition rate of 6.0 nm/min. Furthermore, the threshold voltage (VTH) was controllable as the number of identical erase pulse of 4 V/1 μs was increased, which suggested the VTH control of approximately 10 mV.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130222370","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 : 2022-12-12DOI: 10.1109/ISSM55802.2022.10027105
Y. Kasashima, Shinji Kuniie, Toshiyuki Sayama, T. Tabaru
We have developed the load impedance monitoring method for plasma etching process, which can be externally installed in mass-production equipment. The monitoring system can detect micro-arc discharge and monitor the condition of the film deposited on inner wall of process chamber. In this study, we have upgraded the monitoring system to enhance precision, practicality, and versatility. The system can be used as an effective method for real-time and noninvasive monitoring of plasma etching process.
{"title":"Practical Load Impedance Monitoring System Externally Installed in Plasma Etching Equipment","authors":"Y. Kasashima, Shinji Kuniie, Toshiyuki Sayama, T. Tabaru","doi":"10.1109/ISSM55802.2022.10027105","DOIUrl":"https://doi.org/10.1109/ISSM55802.2022.10027105","url":null,"abstract":"We have developed the load impedance monitoring method for plasma etching process, which can be externally installed in mass-production equipment. The monitoring system can detect micro-arc discharge and monitor the condition of the film deposited on inner wall of process chamber. In this study, we have upgraded the monitoring system to enhance precision, practicality, and versatility. The system can be used as an effective method for real-time and noninvasive monitoring of plasma etching process.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130250195","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 : 2022-12-12DOI: 10.1109/ISSM55802.2022.10026893
Daisuke Kobayashi, Shunsaku Yasuda, Takashi Iuti, Shiho Ito
Recently, natural language processing has been making great progress in the AI field and is attracting attention. This paper describes the application of natural language processing in semiconductor manufacturing. No numerical or image data were used in any of the analyses. Using the natural language processing engine developed by SONY, we were able to analyze trends in quality troubles and extract features of manufacturing equipment from text data alone by performing various natural language processing represented by Bag-of-Ngrams and Chi-square test. By this, it can contribute to quality and productivity improvements from a different perspective.
{"title":"Application of Natural Language Processing in Semiconductor Manufacturing","authors":"Daisuke Kobayashi, Shunsaku Yasuda, Takashi Iuti, Shiho Ito","doi":"10.1109/ISSM55802.2022.10026893","DOIUrl":"https://doi.org/10.1109/ISSM55802.2022.10026893","url":null,"abstract":"Recently, natural language processing has been making great progress in the AI field and is attracting attention. This paper describes the application of natural language processing in semiconductor manufacturing. No numerical or image data were used in any of the analyses. Using the natural language processing engine developed by SONY, we were able to analyze trends in quality troubles and extract features of manufacturing equipment from text data alone by performing various natural language processing represented by Bag-of-Ngrams and Chi-square test. By this, it can contribute to quality and productivity improvements from a different perspective.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121406621","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 : 2022-12-12DOI: 10.1109/ISSM55802.2022.10026947
Christopher Bode
One bedrock requirement within the semiconductor industry is the need for a comprehensive maintenance program to support reliable and predictable tool performance. While this is true, it is certainly not the case where “more is better,” but rather an effort to define the truly necessary actions and best-known methods in maintaining tools to optimize availability, productivity, and product quality. The ongoing development and integration of Smart Manufacturing solutions across the factory systems landscape is now playing a role toward these objectives in providing a foundation for defining, deploying, and managing optimized maintenance task workflows. This paper will present both the efforts of companies using FabRecover, a novel maintenance management decision support framework, and the resulting demonstrable improvements that were achieved through such investments.
{"title":"Maintenance Content Reduction and Digitalization for Performance Optimization","authors":"Christopher Bode","doi":"10.1109/ISSM55802.2022.10026947","DOIUrl":"https://doi.org/10.1109/ISSM55802.2022.10026947","url":null,"abstract":"One bedrock requirement within the semiconductor industry is the need for a comprehensive maintenance program to support reliable and predictable tool performance. While this is true, it is certainly not the case where “more is better,” but rather an effort to define the truly necessary actions and best-known methods in maintaining tools to optimize availability, productivity, and product quality. The ongoing development and integration of Smart Manufacturing solutions across the factory systems landscape is now playing a role toward these objectives in providing a foundation for defining, deploying, and managing optimized maintenance task workflows. This paper will present both the efforts of companies using FabRecover, a novel maintenance management decision support framework, and the resulting demonstrable improvements that were achieved through such investments.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115029011","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 : 2022-12-12DOI: 10.1109/ISSM55802.2022.10027006
R. Busch, Michael G. Wahl, P. Czerner, B. Choubey
Yield is an important cost factor in wafer production. Therefore, continuous data-driven yield monitoring and optimization provides opportunities to reduce production costs. Predicting yield during production would reveal its relationships with production parameters enabling dynamic optimization with a preventive and active increase in yield. In our investigations, we will first predict the yield based on one yield critical process step and later on with the data of four process steps. We will use different machine learning methods for this. Furthermore, we will look at whether the classification into good and bad yield values with these methods provides better results for the prediction. Another point of our investigations are the parameter limits of the individual methods. We show that these can be controlled by a simple method and optimised, if necessary.
{"title":"Yield Prediction with Machine Learning and Parameter Limits in Semiconductor Production","authors":"R. Busch, Michael G. Wahl, P. Czerner, B. Choubey","doi":"10.1109/ISSM55802.2022.10027006","DOIUrl":"https://doi.org/10.1109/ISSM55802.2022.10027006","url":null,"abstract":"Yield is an important cost factor in wafer production. Therefore, continuous data-driven yield monitoring and optimization provides opportunities to reduce production costs. Predicting yield during production would reveal its relationships with production parameters enabling dynamic optimization with a preventive and active increase in yield. In our investigations, we will first predict the yield based on one yield critical process step and later on with the data of four process steps. We will use different machine learning methods for this. Furthermore, we will look at whether the classification into good and bad yield values with these methods provides better results for the prediction. Another point of our investigations are the parameter limits of the individual methods. We show that these can be controlled by a simple method and optimised, if necessary.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132079450","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 : 2022-12-12DOI: 10.1109/ISSM55802.2022.10026887
Chia-Cheng Kuo, Po-Chih Chen, Chang-Tsun Tseng
The system automatically produces yield reports, shipping reports, real-time yield monitoring and statistics, abnormal cause statistics, Yield Chart, etc., to assist manufacturing, process, integration and other personnel to quickly Obtain the finished product/work in process yield report, and grasp the product yield information in real time, and find possible yield problems in real time. With the yield analysis tool provided by this system, we can quickly find the possible abnormal reasons to reduce the abnormal yield rate and the impact of production lines and shipments.
{"title":"Application of Big Data Science in High Reliability Automotive Wafer Yield Management System and Failure Analysis","authors":"Chia-Cheng Kuo, Po-Chih Chen, Chang-Tsun Tseng","doi":"10.1109/ISSM55802.2022.10026887","DOIUrl":"https://doi.org/10.1109/ISSM55802.2022.10026887","url":null,"abstract":"The system automatically produces yield reports, shipping reports, real-time yield monitoring and statistics, abnormal cause statistics, Yield Chart, etc., to assist manufacturing, process, integration and other personnel to quickly Obtain the finished product/work in process yield report, and grasp the product yield information in real time, and find possible yield problems in real time. With the yield analysis tool provided by this system, we can quickly find the possible abnormal reasons to reduce the abnormal yield rate and the impact of production lines and shipments.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127561784","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 : 2022-12-12DOI: 10.1109/ISSM55802.2022.10026901
Takumi Ito, Wang Xueting, Y. Oomuro, Kazutaka Nagashima
In the semiconductor manufacturing, the manufacturing equipment is managed via the quality control (QC). The shape of the pattern is checked whether it meets the specification. If the shape is out of the specification, some recipe parameters are modified so that the shape meets the specification. The calculation method of the recipe parameters depends on the know-how of the individual engineers, which causes difficulties in the QC. We develop the automatic calculation of the optimal recipe parameters with Advanced Process Control (APC) model in order to solve these problems.
{"title":"Advanced Process Control Model for Trench Shape of Power Devices","authors":"Takumi Ito, Wang Xueting, Y. Oomuro, Kazutaka Nagashima","doi":"10.1109/ISSM55802.2022.10026901","DOIUrl":"https://doi.org/10.1109/ISSM55802.2022.10026901","url":null,"abstract":"In the semiconductor manufacturing, the manufacturing equipment is managed via the quality control (QC). The shape of the pattern is checked whether it meets the specification. If the shape is out of the specification, some recipe parameters are modified so that the shape meets the specification. The calculation method of the recipe parameters depends on the know-how of the individual engineers, which causes difficulties in the QC. We develop the automatic calculation of the optimal recipe parameters with Advanced Process Control (APC) model in order to solve these problems.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121579121","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 : 2022-12-12DOI: 10.1109/ISSM55802.2022.10027013
Sumit Choudhary, D. Schwarz, H. Funk, K. P. Sharma, S. Sharma, J. Schulze
The p-Ge layers are epitaxially grown by MBE over the n-Ge and strain-free Ge buffer layers on the Si substrate. The drain-source & channel mesa is patterned in the p-Ge layer to create the raised active channel. Post-plasma oxidation was carried out to improve the interface properties of Ge channel. The proposed process doesn't involve source-drain implants, ease channel patterning using MAPDST, a -ve tone resist with high etch resistance and selectivity w.r.t. Ge. The process flow scheme will utilize the “beyond Si” channel materials over Si substrates, concurrently exploiting the standard well, established state-of-art Si CMOS fabrication technology.
{"title":"Process Optimizations for Ge-On-Si Depletion Mode Transistors Using Mesa Architecture","authors":"Sumit Choudhary, D. Schwarz, H. Funk, K. P. Sharma, S. Sharma, J. Schulze","doi":"10.1109/ISSM55802.2022.10027013","DOIUrl":"https://doi.org/10.1109/ISSM55802.2022.10027013","url":null,"abstract":"The p-Ge layers are epitaxially grown by MBE over the n-Ge and strain-free Ge buffer layers on the Si substrate. The drain-source & channel mesa is patterned in the p-Ge layer to create the raised active channel. Post-plasma oxidation was carried out to improve the interface properties of Ge channel. The proposed process doesn't involve source-drain implants, ease channel patterning using MAPDST, a -ve tone resist with high etch resistance and selectivity w.r.t. Ge. The process flow scheme will utilize the “beyond Si” channel materials over Si substrates, concurrently exploiting the standard well, established state-of-art Si CMOS fabrication technology.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130973268","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 : 2022-12-12DOI: 10.1109/issm55802.2022.10027028
Hibiki Kato, H. Hanafusa, Takuma Sato, S. Higashi
We have developed a new plasma source, reactive atmospheric-pressure micro-thermal-plasma-jet (R-μTPJ) for ultra-fast etching of photoresist. R-μTPJ was generated by DC arc discharge of Ar and O2 with input power of 260 W. local heating and simultaneous supply of reactive oxygen species has achieved an etching rate as high as 46.3 μm/s.
{"title":"Ultra-Fast Etching of Photoresist by Reactive Atmospheric-Pressure Thermal Plasma Jet","authors":"Hibiki Kato, H. Hanafusa, Takuma Sato, S. Higashi","doi":"10.1109/issm55802.2022.10027028","DOIUrl":"https://doi.org/10.1109/issm55802.2022.10027028","url":null,"abstract":"We have developed a new plasma source, reactive atmospheric-pressure micro-thermal-plasma-jet (R-μTPJ) for ultra-fast etching of photoresist. R-μTPJ was generated by DC arc discharge of Ar and O2 with input power of 260 W. local heating and simultaneous supply of reactive oxygen species has achieved an etching rate as high as 46.3 μm/s.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125139499","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 : 2022-12-12DOI: 10.1109/ISSM55802.2022.10027078
Guo Fengjie, Rang Yang, Wang Shuo, Ma Kui, Yang Fa Shun
SiO2 insulating layer is an indispensable part of a TSV. In the current process, the SiO2 insulating layer is commonly deposited on the inner wall of the TSV based on deep trench sputtering method. The thickness at different position (neck, middle, bottom) of the SiO2 insulating layer, deposited by deep trench sputtering, is non-uniform. In this paper, the thickness uniformity of SiO2 insulating layer prepared on the inner wall of TSV based on CVD&PVD process and thermal oxidation method is comparatively studied. The experimental results show that, based on the CVD&PVD process, the average thickness of the SiO2 insulating layer at middle and bottom position of the TSV has changed by - 54.02% and - 58.30% compared with that at the top position, respectively. Based on the thermal oxidation method, the average thickness of the SiO2 insulating layer at middle and bottom position of the TSV has changed by 1.17% and 0.26% compared with that at the top position, respectively. The thermal oxidation method can realize the SiO2 insulating layer with uniform thickness on the inner wall of TSV.
{"title":"Preparation of Uniform SiO2 Insulating Layer on the Inner Wall of TSV by Thermal Oxidation","authors":"Guo Fengjie, Rang Yang, Wang Shuo, Ma Kui, Yang Fa Shun","doi":"10.1109/ISSM55802.2022.10027078","DOIUrl":"https://doi.org/10.1109/ISSM55802.2022.10027078","url":null,"abstract":"SiO2 insulating layer is an indispensable part of a TSV. In the current process, the SiO2 insulating layer is commonly deposited on the inner wall of the TSV based on deep trench sputtering method. The thickness at different position (neck, middle, bottom) of the SiO2 insulating layer, deposited by deep trench sputtering, is non-uniform. In this paper, the thickness uniformity of SiO2 insulating layer prepared on the inner wall of TSV based on CVD&PVD process and thermal oxidation method is comparatively studied. The experimental results show that, based on the CVD&PVD process, the average thickness of the SiO2 insulating layer at middle and bottom position of the TSV has changed by - 54.02% and - 58.30% compared with that at the top position, respectively. Based on the thermal oxidation method, the average thickness of the SiO2 insulating layer at middle and bottom position of the TSV has changed by 1.17% and 0.26% compared with that at the top position, respectively. The thermal oxidation method can realize the SiO2 insulating layer with uniform thickness on the inner wall of TSV.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121085525","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：