Pub Date : 2020-12-15DOI: 10.1109/ISSM51728.2020.9377513
Kosuke Okusa, Toshiya Okazaki, Shunsaku Yasuda
Accurate prediction of product performance is very important in semiconductor manufacturing processes. Manufacturing plants with high-mix low-volume types face the problem of having to create many quality prediction models with a small sample size. In this high-mix-low-volume-type plant, the construction of a highly efficient and accurate prediction model for product performance is an important issue. In this study, we propose a quality prediction model based on the hierarchical Bayesian model that can predict quality with high accuracy even for a small number of samples.
{"title":"A Statistical Study on Highly Accurate Quality Prediction for High-mix Low-Volume Semiconductor Products","authors":"Kosuke Okusa, Toshiya Okazaki, Shunsaku Yasuda","doi":"10.1109/ISSM51728.2020.9377513","DOIUrl":"https://doi.org/10.1109/ISSM51728.2020.9377513","url":null,"abstract":"Accurate prediction of product performance is very important in semiconductor manufacturing processes. Manufacturing plants with high-mix low-volume types face the problem of having to create many quality prediction models with a small sample size. In this high-mix-low-volume-type plant, the construction of a highly efficient and accurate prediction model for product performance is an important issue. In this study, we propose a quality prediction model based on the hierarchical Bayesian model that can predict quality with high accuracy even for a small number of samples.","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131816664","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 : 2020-12-15DOI: 10.1109/ISSM51728.2020.9377523
H. Tsuchiyama, David Wizelman
We have developed Metrology Sampling Optimizer integrated with Factory Scheduler to realize the visualization and control of Time to Defect Detection which is required by Zero defect activity for Automotive Quality. In this paper, the system architecture and deployment result is reviewed. Also, the system integration with AI FDC is under development so that the proactive metrological activity will reduce the overall risk on both equipment and product.
{"title":"Smart Integrated Metrology Sampling","authors":"H. Tsuchiyama, David Wizelman","doi":"10.1109/ISSM51728.2020.9377523","DOIUrl":"https://doi.org/10.1109/ISSM51728.2020.9377523","url":null,"abstract":"We have developed Metrology Sampling Optimizer integrated with Factory Scheduler to realize the visualization and control of Time to Defect Detection which is required by Zero defect activity for Automotive Quality. In this paper, the system architecture and deployment result is reviewed. Also, the system integration with AI FDC is under development so that the proactive metrological activity will reduce the overall risk on both equipment and product.","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125749485","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 : 2020-12-15DOI: 10.1109/ISSM51728.2020.9377524
Yu Hsiang-Meng, Lin Chih-Chen, Hsu Min-hsuan, Chen Yen-Ting, Chen Kuang-Wei, T. Luoh, Yang Ling-Wu, Yang Tahone, Chen Kuang-Chao
Advanced Chemical-mechanical polishing (CMP) process not only needs to maintain stable run-to-run thickness control to achieve better within wafer/within chip planarization performance, but also have capability to cover various topologies and layout densities patterned wafer and preventing the hot spots occurrences. In this study, different Neural-Network algorithm with data pre-processing models are implemented to the in-line CMP CLC tuning and dishing/erosion prediction at various topology/pattern density incoming pattern wafers to resolve the most challenging process issues at next generation.
{"title":"CMP Process Optimization Engineering by Machine Learning","authors":"Yu Hsiang-Meng, Lin Chih-Chen, Hsu Min-hsuan, Chen Yen-Ting, Chen Kuang-Wei, T. Luoh, Yang Ling-Wu, Yang Tahone, Chen Kuang-Chao","doi":"10.1109/ISSM51728.2020.9377524","DOIUrl":"https://doi.org/10.1109/ISSM51728.2020.9377524","url":null,"abstract":"Advanced Chemical-mechanical polishing (CMP) process not only needs to maintain stable run-to-run thickness control to achieve better within wafer/within chip planarization performance, but also have capability to cover various topologies and layout densities patterned wafer and preventing the hot spots occurrences. In this study, different Neural-Network algorithm with data pre-processing models are implemented to the in-line CMP CLC tuning and dishing/erosion prediction at various topology/pattern density incoming pattern wafers to resolve the most challenging process issues at next generation.","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124089523","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}
From a point of view for AEC (Advanced/Autonomous Equipment Control) and APC (Advanced/Autonomous Process Control), it is necessary to catch up the condition from semiconductor manufacturing equipment in more detail and to monitor it more closely. After wet cleaning, it is difficult to determine the optimal number of seasoning wafers for the etch equipment. As a solution for this problem, the “Seasoning Index” that shows the progress of seasonings in the chamber is developed by using OES (Optical Emission Spectroscopy). Then, the Seasoning Optimizer function was implemented to make sure that the seasonings are performed appropriately. Furthermore, the principle of the seasoning process was also investigated. As a result, it was shown that the Seasoning Index, which shows similar behavior with the by-product of the reaction, is also effective as an index showing the seasoning state in the chamber. This case shows one of the application examples of AEC/APC.
{"title":"Seasoning Optimization by Using Optical Emission Spectroscopy","authors":"Masahiro Shiga, Haruki Omine, Masaki Kitsunezuka, Hironori Moki, Yuki Kataoka, Takahito Matsuzawa, Yasuhisa Suzuki, Yusuke Fukazawa, Tsuyoshi Makita, Kazuyuki Takasawa, Takuo Yamamoto, Takao Funakubo","doi":"10.1109/ISSM51728.2020.9377498","DOIUrl":"https://doi.org/10.1109/ISSM51728.2020.9377498","url":null,"abstract":"From a point of view for AEC (Advanced/Autonomous Equipment Control) and APC (Advanced/Autonomous Process Control), it is necessary to catch up the condition from semiconductor manufacturing equipment in more detail and to monitor it more closely. After wet cleaning, it is difficult to determine the optimal number of seasoning wafers for the etch equipment. As a solution for this problem, the “Seasoning Index” that shows the progress of seasonings in the chamber is developed by using OES (Optical Emission Spectroscopy). Then, the Seasoning Optimizer function was implemented to make sure that the seasonings are performed appropriately. Furthermore, the principle of the seasoning process was also investigated. As a result, it was shown that the Seasoning Index, which shows similar behavior with the by-product of the reaction, is also effective as an index showing the seasoning state in the chamber. This case shows one of the application examples of AEC/APC.","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124279835","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 : 2020-12-15DOI: 10.1109/ISSM51728.2020.9377503
K. Sakai, K. Takanashi, Tatsuya Sakai
Spin-on glass (SOG), poly-carbosilane (PCS) and metal hardmask (MHM) materials with unique film properties were developed and introduced in this paper. PCS materials showed good gap-fill performance, thermal stability and dielectric constant than standard SOG materials. PCS material form good film quality up to 10 μm film thickness by single coat process. We have also investigated the combination of PCS material and UV irradiation process. UV irradiation alters PCS film property and improved solubility to diluted aqueous HF. MHM materials showed good gap-fill performance, slower dry etching rate and unique optical constant compared to low temperature oxide (LTO). JSR spin-on PCS and MHM materials show different film properties and improved performance compared to chemical vapor deposition (CVD) or physical vapor deposition (PVD) films are expected to simplify the complex/fine semiconductor device manufacturing process.
{"title":"Recent Progress on Spin-on Inorganic Materials","authors":"K. Sakai, K. Takanashi, Tatsuya Sakai","doi":"10.1109/ISSM51728.2020.9377503","DOIUrl":"https://doi.org/10.1109/ISSM51728.2020.9377503","url":null,"abstract":"Spin-on glass (SOG), poly-carbosilane (PCS) and metal hardmask (MHM) materials with unique film properties were developed and introduced in this paper. PCS materials showed good gap-fill performance, thermal stability and dielectric constant than standard SOG materials. PCS material form good film quality up to 10 μm film thickness by single coat process. We have also investigated the combination of PCS material and UV irradiation process. UV irradiation alters PCS film property and improved solubility to diluted aqueous HF. MHM materials showed good gap-fill performance, slower dry etching rate and unique optical constant compared to low temperature oxide (LTO). JSR spin-on PCS and MHM materials show different film properties and improved performance compared to chemical vapor deposition (CVD) or physical vapor deposition (PVD) films are expected to simplify the complex/fine semiconductor device manufacturing process.","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121829183","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 : 2020-12-15DOI: 10.1109/ISSM51728.2020.9377511
Akifumi Kurachi, Katsuyuki Tsuchida, T. Kawai
The Ni diffusion behavior of electroless NiP/PdP/Au film was investigated using Auger electron spectroscopy in this study. The diffusion coefficient of Ni in PdP was estimated from concentration profile by Einstein-Smoluchowski equation and Boltzman-Matano method. The obtained value was 3.5 ~4.7⨯10–20 m2s-1at 553 K. Furthermore, the bondability of the NiP/PdP/Au film before and after heat treatment was also studies to clarify the bonding reliability of the film of various thickness. Thicker NiP/PdP/Au film condition achieved high bonding reliability at least under the condition of this study.
{"title":"Ni Diffusion Behavior and Bondability of Electroless NiP/PdP/Au Film","authors":"Akifumi Kurachi, Katsuyuki Tsuchida, T. Kawai","doi":"10.1109/ISSM51728.2020.9377511","DOIUrl":"https://doi.org/10.1109/ISSM51728.2020.9377511","url":null,"abstract":"The Ni diffusion behavior of electroless NiP/PdP/Au film was investigated using Auger electron spectroscopy in this study. The diffusion coefficient of Ni in PdP was estimated from concentration profile by Einstein-Smoluchowski equation and Boltzman-Matano method. The obtained value was 3.5 ~4.7⨯10–20 m2s-1at 553 K. Furthermore, the bondability of the NiP/PdP/Au film before and after heat treatment was also studies to clarify the bonding reliability of the film of various thickness. Thicker NiP/PdP/Au film condition achieved high bonding reliability at least under the condition of this study.","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117022760","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 : 2020-12-15DOI: 10.1109/ISSM51728.2020.9377501
Hiromi Kimura, Noriko Tsujinaka, Toshimasa Katou
In this paper, the ultra-trace metal quantification up to 1 ng/L in isopropyl alcohol (IPA) are discussed. In order to quantify the metal concentrations, “re-dissolved method” in which the solvent is completely evaporated and re-dissolved in acid for analysis with an inductively coupled plasma mass spectrometer (ICP-MS), and “direct method” in which the solvent is directly introduced into the ICP-MS are evaluated. In both methods, it was found that impurities derived from the instruments used in the analysis affect the measurement, and it was possible to reduce metal elution from the instruments by performing appropriate pre-cleaning. In addition, the analysis accuracy could be ensured. As a result, we were able to measure the metal contained in IPA the lower limit of quantification of 1 ng/L by each method. Furthermore, it was confirmed that there was no difference in the analytical values between the both methods. By properly using the two methods, we believe that it can be widely applied to the analysis of organic solvents other than IPA and the analysis of multiple samples of a single solvent.
{"title":"Study on ultra-trace metal analysis in organic solvent by re-dissolved method and direct method","authors":"Hiromi Kimura, Noriko Tsujinaka, Toshimasa Katou","doi":"10.1109/ISSM51728.2020.9377501","DOIUrl":"https://doi.org/10.1109/ISSM51728.2020.9377501","url":null,"abstract":"In this paper, the ultra-trace metal quantification up to 1 ng/L in isopropyl alcohol (IPA) are discussed. In order to quantify the metal concentrations, “re-dissolved method” in which the solvent is completely evaporated and re-dissolved in acid for analysis with an inductively coupled plasma mass spectrometer (ICP-MS), and “direct method” in which the solvent is directly introduced into the ICP-MS are evaluated. In both methods, it was found that impurities derived from the instruments used in the analysis affect the measurement, and it was possible to reduce metal elution from the instruments by performing appropriate pre-cleaning. In addition, the analysis accuracy could be ensured. As a result, we were able to measure the metal contained in IPA the lower limit of quantification of 1 ng/L by each method. Furthermore, it was confirmed that there was no difference in the analytical values between the both methods. By properly using the two methods, we believe that it can be widely applied to the analysis of organic solvents other than IPA and the analysis of multiple samples of a single solvent.","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127698714","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 : 2020-12-15DOI: 10.1109/ISSM51728.2020.9377526
Kenta Kamizono, Kazutaka Ikeda, Hiroaki Kitajima, S. Yasuda, Tomoya Tanaka
In order to further improve the productivity of manufacturing equipment, it is indispensable to monitor the conditions of all the manufacturing equipment and not just the processing chambers. In this paper, we present a robust machine learning based degradation diagnosis technology with harmonic sensor. The example of wafer transfer robots in the ion implanter and the LP-CVD and the coater/developer show that wear degradation of machine components can be detected from the level of degradation output and it is possible to prevent errors of the wafer transfer robots because of maintenance based on increases in the level of degradation.
{"title":"FDC Based on Neural Network with Harmonic Sensor to Prevent Error of Robot","authors":"Kenta Kamizono, Kazutaka Ikeda, Hiroaki Kitajima, S. Yasuda, Tomoya Tanaka","doi":"10.1109/ISSM51728.2020.9377526","DOIUrl":"https://doi.org/10.1109/ISSM51728.2020.9377526","url":null,"abstract":"In order to further improve the productivity of manufacturing equipment, it is indispensable to monitor the conditions of all the manufacturing equipment and not just the processing chambers. In this paper, we present a robust machine learning based degradation diagnosis technology with harmonic sensor. The example of wafer transfer robots in the ion implanter and the LP-CVD and the coater/developer show that wear degradation of machine components can be detected from the level of degradation output and it is possible to prevent errors of the wafer transfer robots because of maintenance based on increases in the level of degradation.","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127952214","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 : 2020-12-15DOI: 10.1109/issm51728.2020.9377522
{"title":"[Copyright notice]","authors":"","doi":"10.1109/issm51728.2020.9377522","DOIUrl":"https://doi.org/10.1109/issm51728.2020.9377522","url":null,"abstract":"","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128360272","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 : 2020-12-15DOI: 10.1109/ISSM51728.2020.9377496
Kaito Date, Y. Tanaka
Quality control is an important issue in semiconductor manufacturing. Statistical process control (SPC) is known as a powerful method for accomplishing process stability and reducing variability. In this paper, we adopt the quality-oriented statistical process control (QOSPC) method. In QOSPC, product quality test data, such as electrical performance and product reliability, are incorporated in the process control procedure. QOSPC has two major challenges: extracting process variables that affect product quality, and determining quality control limits (QCLs) for each variable. In this work, we fully exploit a Bayesian approach to resolve both of these challenges simultaneously. We introduced a linear bathtub model (LBM) that contains parameters corresponding to QCLs as obvious change points and fit the model to the observed data by Bayesian inference (BI). In our experiments with artificial datasets, the values of QCLs and their probability of existence can be estimated robustly by BI. Using the proposed method, the human labor cost for determining QCLs is reduced by 93%.
{"title":"Quality-Oriented Statistical Process Control Utilizing Bayesian Modeling","authors":"Kaito Date, Y. Tanaka","doi":"10.1109/ISSM51728.2020.9377496","DOIUrl":"https://doi.org/10.1109/ISSM51728.2020.9377496","url":null,"abstract":"Quality control is an important issue in semiconductor manufacturing. Statistical process control (SPC) is known as a powerful method for accomplishing process stability and reducing variability. In this paper, we adopt the quality-oriented statistical process control (QOSPC) method. In QOSPC, product quality test data, such as electrical performance and product reliability, are incorporated in the process control procedure. QOSPC has two major challenges: extracting process variables that affect product quality, and determining quality control limits (QCLs) for each variable. In this work, we fully exploit a Bayesian approach to resolve both of these challenges simultaneously. We introduced a linear bathtub model (LBM) that contains parameters corresponding to QCLs as obvious change points and fit the model to the observed data by Bayesian inference (BI). In our experiments with artificial datasets, the values of QCLs and their probability of existence can be estimated robustly by BI. Using the proposed method, the human labor cost for determining QCLs is reduced by 93%.","PeriodicalId":270309,"journal":{"name":"2020 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126150249","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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