Pub Date : 2024-02-14DOI: 10.1109/TSM.2024.3365827
Surin An;Jeong Eun Choi;Ju Eun Kang;Jiseok Lee;Sang Jeen Hong
Semiconductor industry is experiencing a rising demand for environmentally friendly processes with the emphasis on green policies and worldwide environmental sustainability. Nitrogen trifluoride (NF3), the most common plasma chamber cleaning agent gas, poses a significant concern as a potent greenhouse gas since it has global warming potential (GWP), 740 times and 6 times higher than that CO2 and N2O. This study investigated the exhaust gas using quadrupole mass spectroscopy (QMS) and analyzed the change in cleaning speed and the type of exhaust gas through plasma monitoring using optical mass spectroscopy (OES). The objective is to lower the use of the amount of NF3 gas in chamber cleaning process to partially contribute the environmental sustainability in the point of semiconductor manufacturing. When a small amount of N2 was added to NF3 whose ratio of 7:23, the cleaning efficiency reached to 90% compared to NF3 gas alone. Addition of N2 positively affected electron density and temperature to increase the F-radical in remote plasma system. In conclusion, 18% of NF3 usage amount was reduced during the Sio2 deposition chamber cleaning process.
{"title":"Eco-Friendly Dry-Cleaning and Diagnostics of Silicon Dioxide Deposition Chamber","authors":"Surin An;Jeong Eun Choi;Ju Eun Kang;Jiseok Lee;Sang Jeen Hong","doi":"10.1109/TSM.2024.3365827","DOIUrl":"10.1109/TSM.2024.3365827","url":null,"abstract":"Semiconductor industry is experiencing a rising demand for environmentally friendly processes with the emphasis on green policies and worldwide environmental sustainability. Nitrogen trifluoride (NF3), the most common plasma chamber cleaning agent gas, poses a significant concern as a potent greenhouse gas since it has global warming potential (GWP), 740 times and 6 times higher than that CO2 and N2O. This study investigated the exhaust gas using quadrupole mass spectroscopy (QMS) and analyzed the change in cleaning speed and the type of exhaust gas through plasma monitoring using optical mass spectroscopy (OES). The objective is to lower the use of the amount of NF3 gas in chamber cleaning process to partially contribute the environmental sustainability in the point of semiconductor manufacturing. When a small amount of N2 was added to NF3 whose ratio of 7:23, the cleaning efficiency reached to 90% compared to NF3 gas alone. Addition of N2 positively affected electron density and temperature to increase the F-radical in remote plasma system. In conclusion, 18% of NF3 usage amount was reduced during the Sio2 deposition chamber cleaning process.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954668","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}
Curvilinear design was applied to standard cell layout to improve electrical characteristics and reduce manufacturing costs. Its implementation was intelligently co-optimized with 1-D Manhattan shapes and photolithography process to preserve the standard cell area equivalent to that of 1-D Manhattan-only designs. B-spline curve representation was employed to realize the curvilinear design. Curvilinear pathfinding was carried out through the Voronoi diagram to find the optimum routing path, and the A* routing algorithm to determine the shortest path. In the curvilinear-designed standard cells, the majority of standard cells exhibited reduced total metal length, decreased number of vias, and eliminated the need for an extra metal layer when compared to 1-D Manhattan-only standard cell designs. Manufacturability of curvilinear designs was evaluated, and potential solutions are proposed in the context of design rule, design rules check (DRC) and optical proximity correction (OPC). DRC and OPC were carried out within the currently employed electronic design automation (EDA) tools to verify the curvilinear designs.
{"title":"Curvilinear Standard Cell Design for Semiconductor Manufacturing","authors":"Ryoung-Han Kim;Soobin Hwang;Apoorva Oak;Yasser Shirazi;Hsinlan Chang;Kiho Yang;Gioele Mirabelli","doi":"10.1109/TSM.2024.3362900","DOIUrl":"10.1109/TSM.2024.3362900","url":null,"abstract":"Curvilinear design was applied to standard cell layout to improve electrical characteristics and reduce manufacturing costs. Its implementation was intelligently co-optimized with 1-D Manhattan shapes and photolithography process to preserve the standard cell area equivalent to that of 1-D Manhattan-only designs. B-spline curve representation was employed to realize the curvilinear design. Curvilinear pathfinding was carried out through the Voronoi diagram to find the optimum routing path, and the A* routing algorithm to determine the shortest path. In the curvilinear-designed standard cells, the majority of standard cells exhibited reduced total metal length, decreased number of vias, and eliminated the need for an extra metal layer when compared to 1-D Manhattan-only standard cell designs. Manufacturability of curvilinear designs was evaluated, and potential solutions are proposed in the context of design rule, design rules check (DRC) and optical proximity correction (OPC). DRC and OPC were carried out within the currently employed electronic design automation (EDA) tools to verify the curvilinear designs.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945816","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 : 2024-02-05DOI: 10.1109/TSM.2023.3334414
{"title":"IEEE Transactions on Semiconductor Manufacturing Information for Authors","authors":"","doi":"10.1109/TSM.2023.3334414","DOIUrl":"https://doi.org/10.1109/TSM.2023.3334414","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10419383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694970","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 : 2024-02-05DOI: 10.1109/TSM.2024.3359520
{"title":"Call for Papers for IEEE Transactions on Materials for Electron Devices","authors":"","doi":"10.1109/TSM.2024.3359520","DOIUrl":"https://doi.org/10.1109/TSM.2024.3359520","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10419869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695013","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 : 2024-02-05DOI: 10.1109/TSM.2024.3356972
{"title":"Joint Call for Papers for IEEE Transactions on Semiconductor Manufacturing and IEEE Transactions on Electron Devices: Special Issue on Semiconductor Design for Manufacturing (DFM)","authors":"","doi":"10.1109/TSM.2024.3356972","DOIUrl":"https://doi.org/10.1109/TSM.2024.3356972","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10419386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695042","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 : 2023-12-27DOI: 10.1109/TSM.2023.3347606
Ajay Kumar Dwivedi;Satyabrata Jit;Shweta Tripathi
This letter reports a SnS2 and ZnO nanocomposite (NC) prepared by dispersion method. The nanocomposite shows promising characteristics for optoelectronic application. SnS2:ZnO NC shows a wide absorption spectrum covering ultraviolet (UV)-visible-near infrared (NIR) regions. Hence, using the proposed nanocomposite a broadband photodetector with a structure comprising Al/ SnS2:ZnO/PEDOT:PSS/ Indium Tin Oxide (ITO) is fabricated. At a bias voltage of 1 V, the measured responsivity values (A/W) of the proposed device are 140.41, 848.63, and 1094.48 at 350 nm (UV), 750 nm (visible) and 900 nm (NIR), respectively.
{"title":"SnS₂ and ZnO Nanocomposite Prepared by Dispersion Method for Photodetector Application","authors":"Ajay Kumar Dwivedi;Satyabrata Jit;Shweta Tripathi","doi":"10.1109/TSM.2023.3347606","DOIUrl":"https://doi.org/10.1109/TSM.2023.3347606","url":null,"abstract":"This letter reports a SnS2 and ZnO nanocomposite (NC) prepared by dispersion method. The nanocomposite shows promising characteristics for optoelectronic application. SnS2:ZnO NC shows a wide absorption spectrum covering ultraviolet (UV)-visible-near infrared (NIR) regions. Hence, using the proposed nanocomposite a broadband photodetector with a structure comprising Al/ SnS2:ZnO/PEDOT:PSS/ Indium Tin Oxide (ITO) is fabricated. At a bias voltage of 1 V, the measured responsivity values (A/W) of the proposed device are 140.41, 848.63, and 1094.48 at 350 nm (UV), 750 nm (visible) and 900 nm (NIR), respectively.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695007","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 : 2023-12-15DOI: 10.1109/TSM.2023.3343633
Andy Ham;Myoung-Ju Park;John Fowler
This paper studies simultaneous scheduling of production and material transfer that arises in the semiconductor photolithography area. In particular, the right reticle and right job both need to be present to process the job. Jobs are transferred by a material handling system that employees a fleet of vehicles. Reticles serving as an auxiliary resource are also transferred from one place to another by a different set of vehicles. This intricate scheduling challenge, encompassing jobs, reticles, machines, and two distinct sets of vehicles, is explored here for the first time. The paper introduces a multi-stage methodology that involves relaxation, a constructive heuristic, constraint programming, and a warm-start approach to address this complex problem.
{"title":"Integrated Scheduling of Jobs, Tools, Machines, and Two Different Set of Transbots","authors":"Andy Ham;Myoung-Ju Park;John Fowler","doi":"10.1109/TSM.2023.3343633","DOIUrl":"https://doi.org/10.1109/TSM.2023.3343633","url":null,"abstract":"This paper studies simultaneous scheduling of production and material transfer that arises in the semiconductor photolithography area. In particular, the right reticle and right job both need to be present to process the job. Jobs are transferred by a material handling system that employees a fleet of vehicles. Reticles serving as an auxiliary resource are also transferred from one place to another by a different set of vehicles. This intricate scheduling challenge, encompassing jobs, reticles, machines, and two distinct sets of vehicles, is explored here for the first time. The paper introduces a multi-stage methodology that involves relaxation, a constructive heuristic, constraint programming, and a warm-start approach to address this complex problem.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694969","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}
This paper proposes a linear regression model for scalar-valued responses and two-way functional (bivariate) predictors. Our motivation stems from the quality evaluation of products based on optical emission spectroscopy data from virtual metrology of semiconductor manufacturing. We focus on multivariate cases where the smoothness and shapes of the data vary significantly across variables. We propose a two-step solution to this problem, consisting of decomposition and prediction. First, we decompose the two-way functional data into pairs of component functions using functional singular value decomposition. Next, we build functional linear models for the decomposed functional variables and obtain the final predictor by averaging the models. Results from numerical studies, including simulation studies and real data analysis, demonstrate the promising empirical properties of the proposed approach, especially when the number of predictors is large.
{"title":"A Model Averaging Prediction of Two-Way Functional Data in Semiconductor Manufacturing","authors":"Soobin Kim;Youngwook Kwon;Joonpyo Kim;Kiwook Bae;Hee-Seok Oh","doi":"10.1109/TSM.2023.3339731","DOIUrl":"https://doi.org/10.1109/TSM.2023.3339731","url":null,"abstract":"This paper proposes a linear regression model for scalar-valued responses and two-way functional (bivariate) predictors. Our motivation stems from the quality evaluation of products based on optical emission spectroscopy data from virtual metrology of semiconductor manufacturing. We focus on multivariate cases where the smoothness and shapes of the data vary significantly across variables. We propose a two-step solution to this problem, consisting of decomposition and prediction. First, we decompose the two-way functional data into pairs of component functions using functional singular value decomposition. Next, we build functional linear models for the decomposed functional variables and obtain the final predictor by averaging the models. Results from numerical studies, including simulation studies and real data analysis, demonstrate the promising empirical properties of the proposed approach, especially when the number of predictors is large.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694892","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 : 2023-12-06DOI: 10.1109/TSM.2023.3340110
Mingyang Xia;Yan Yan;Chen Li;Xuelong Shi
To ensure post OPC data quality, examination based on estimated resist contours at resist bottom alone is insufficient, reliable prediction of lithography performance within process window must rely on complete information of on-wafer resist 3D structures. In this regard, resist 3D structure model, in particular, the through focus resist 3D structure model, with full chip capability will be the ultimate model in demand. To develop machine learning resist 3D structure models,we have proposed the physics-based information encoding scheme, together with carefully chosen deep convolution neural network and model training strategies. Our proposed through focus resist 3D structure model is based on conditional U-net structure with first five eigen images as model’s main inputs and the focus setting as the conditional input. The average normalized cross correlation (NCC) or mean structure similarity index between ground truth and model predicted resist 3D structures can reach 0.92. With single GPU (Tesla M60), it takes 6.1ms for the model to produce resist 3D structure covering area of 1.8umx1.8 $mu {mathrm{ m}}$ . The model is fast enough and can be engineered for full chip implementation. The model can extend the capability of detecting lithography process window aware resist loss related hotspots.
{"title":"A Unified Machine Learning Through Focus Resist 3-D Structure Model","authors":"Mingyang Xia;Yan Yan;Chen Li;Xuelong Shi","doi":"10.1109/TSM.2023.3340110","DOIUrl":"https://doi.org/10.1109/TSM.2023.3340110","url":null,"abstract":"To ensure post OPC data quality, examination based on estimated resist contours at resist bottom alone is insufficient, reliable prediction of lithography performance within process window must rely on complete information of on-wafer resist 3D structures. In this regard, resist 3D structure model, in particular, the through focus resist 3D structure model, with full chip capability will be the ultimate model in demand. To develop machine learning resist 3D structure models,we have proposed the physics-based information encoding scheme, together with carefully chosen deep convolution neural network and model training strategies. Our proposed through focus resist 3D structure model is based on conditional U-net structure with first five eigen images as model’s main inputs and the focus setting as the conditional input. The average normalized cross correlation (NCC) or mean structure similarity index between ground truth and model predicted resist 3D structures can reach 0.92. With single GPU (Tesla M60), it takes 6.1ms for the model to produce resist 3D structure covering area of 1.8umx1.8 \u0000<inline-formula> <tex-math>$mu {mathrm{ m}}$ </tex-math></inline-formula>\u0000. The model is fast enough and can be engineered for full chip implementation. The model can extend the capability of detecting lithography process window aware resist loss related hotspots.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695012","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}
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