IEEE Transactions on Semiconductor Manufacturing最新文献

{"title":"Special Section Call for Papers: Bridging the Data Gap in Photovoltaics with Synthetic Data Generation","authors":"","doi":"10.1109/TSM.2024.3455875","DOIUrl":"https://doi.org/10.1109/TSM.2024.3455875","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"645-646"},"PeriodicalIF":2.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10765976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694678","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}

{"title":"Call for Papers: Special Issue on Intelligent Sensor Systems for the IEEE Journal of Electron Devices","authors":"","doi":"10.1109/TSM.2024.3455873","DOIUrl":"https://doi.org/10.1109/TSM.2024.3455873","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"643-644"},"PeriodicalIF":2.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694680","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}

{"title":"Guest Editorial Special Section on Sustainability","authors":"Oliver D. Patterson;Tomasz Brozek;Kaushik Balamukundhan;David M. Fried;Bill Nehrer;Suresh Ramarajan","doi":"10.1109/TSM.2024.3485049","DOIUrl":"https://doi.org/10.1109/TSM.2024.3485049","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"418-421"},"PeriodicalIF":2.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691709","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}

{"title":"IEEE Transactions on Semiconductor Manufacturing Information for Authors","authors":"","doi":"10.1109/TSM.2024.3455877","DOIUrl":"https://doi.org/10.1109/TSM.2024.3455877","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"C3-C3"},"PeriodicalIF":2.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10765978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694662","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}

{"title":"Blank Page","authors":"","doi":"10.1109/TSM.2024.3487439","DOIUrl":"https://doi.org/10.1109/TSM.2024.3487439","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"C4-C4"},"PeriodicalIF":2.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694649","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}

{"title":"TechRxiv: Share Your Preprint Research With the World!","authors":"","doi":"10.1109/TSM.2024.3504213","DOIUrl":"https://doi.org/10.1109/TSM.2024.3504213","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"648-648"},"PeriodicalIF":2.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694661","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}

{"title":"IEEE Transactions on Semiconductor Manufacturing Publication Information","authors":"","doi":"10.1109/TSM.2024.3455869","DOIUrl":"https://doi.org/10.1109/TSM.2024.3455869","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"C2-C2"},"PeriodicalIF":2.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10765977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691702","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}

{"title":"Prevention of Moisture Invasion by Flow Isolation Device (FID) for Mask Automatic Storage System (Stocker Room) in a Semiconductor Fabrication Plant (Fab)","authors":"Pin-Yen Liao;Tee Lin;Omid Ali Zargar;Jhang-Kun Li;Yang-Cheng Shih;Shih-Cheng Hu;Graham Leggett","doi":"10.1109/TSM.2024.3492173","DOIUrl":"https://doi.org/10.1109/TSM.2024.3492173","url":null,"abstract":"recent developments in semiconductor manufacturing have seen feature sizes reduce to as small as 3 nm. It is predicted that 2 nm, or even 1 nanometer will be achieved soon. Improving the level of cleanliness of the wafer mask during manufacturing can lead to improved product yield and quality. The quality of lithography technology and the reticle is one of the most important items in the wafer manufacturing process. The cleanliness of this process directly affects the wafer quality and yield. Because the wafer manufacturing process involves the stacking of multiple reticles through lithography technology, semiconductor factories mostly use a reticle stocker room to store the photomasks. However, the reticle is susceptible to defects caused by moisture, particles, and molecular contaminants in the air. Therefore, the reticle stocker room environment requires high cleanliness and humidity control. In this study, the flow stream lines, velocity and humidity fields associated with a flow isolation device (FID) installed in a reticle stocker room were analyzed with the assistance of computational fluid dynamics (CFD) software developed by ANSYS Fluent. Different velocity (V=1 m/s, 1.5 m/s, 2 m/s) of the flow isolation device were examined. The results show that under the same velocity (V=1 m/s), the wider the outlet width of the flow isolation device (W <inline-formula> <tex-math>${=}0$ </tex-math></inline-formula>.2 m), the higher the isolation efficiency (<inline-formula> <tex-math>$eta {=}83.9$ </tex-math></inline-formula>%). The results also show that the faster the velocity of the flow isolation device (V =2 m/s), the better the isolation efficiency (<inline-formula> <tex-math>$eta {=}88.2$ </tex-math></inline-formula>%) under the same outlet width (W <inline-formula> <tex-math>${=}0$ </tex-math></inline-formula>.1 m). The use of the flow isolation device can effectively reduce the supply of clean dry air (CDA) by up to 40%, greatly reducing energy consumption during semiconductor manufacturing. According to the results of this study, when using both a hollow fiber adsorption dryer and a flow isolation device with a width of 0.1 m and an outlet wind speed of 2 m/s, it can save 118,514 kWh per year, and its energy saving rate is 92.03%.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"57-64"},"PeriodicalIF":2.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489109","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}

{"title":"A Data-Driven Approach for Improving Energy Efficiency in a Semiconductor Manufacturing Plant","authors":"Zhao Hong;Chew Ze Yong;Kosasih Lucky;Goh Jun Rong;Wang Joheng","doi":"10.1109/TSM.2024.3483781","DOIUrl":"https://doi.org/10.1109/TSM.2024.3483781","url":null,"abstract":"The semiconductor industry faces increasing pressure to improve energy efficiency while maintaining competitiveness and sustainability. Apart from more conventional energy efficiency measures look at equipment modernization and process and design optimization, this paper explores the potential of data-driven approaches to address these challenges and optimize energy consumption across both the facility and manufacturing space of a semiconductor manufacture plant. By harnessing advanced analytics, machine learning algorithms, and IoT technologies, semiconductor manufacturers can gain real-time insights into energy usage patterns, and identify areas of opportunities that leads to the implementation of targeted interventions to optimize performance. The paper first looks into the challenges and measures of enabling and enhancing data visibility which is the foundation of the data-driven approach, then it examines case studies, best practices and various systematic approaches, demonstrating the transformative impact of data-driven energy efficiency measures which leads to operational efficiency, cost reduction, and environmental sustainability. Ultimately, this paper aims to provide a fresh angle into the energy efficiency study for peers in semiconductor industries to leverage in their journey towards a more sustainable and energy efficient future.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"475-480"},"PeriodicalIF":2.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691763","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}

{"title":"Characterization of Multimodal Spot Scanning Imaging System for Wafer Defect Inspection","authors":"Zuoda Zhou;Haiyan Luo;Wei Xiong;Dingjun Qu;Ruizhe Ding;Zhiwei Li;Wei Jin;Yu Ru;Shihao Jia;Jin Hong","doi":"10.1109/TSM.2024.3481291","DOIUrl":"https://doi.org/10.1109/TSM.2024.3481291","url":null,"abstract":"Typical defects on unpatterned wafers include particles, residues, scratches, and cracks. Various dark-field scattering methods have been applied to detect unpatterned wafer surface defects. However, these methods have only one optical detection channel, making handling multiple types of wafer defects difficult. In response, the theory of multimodal defect inspection is improved, and a multimodal spot-scanning imaging system is developed. The laser beam is focused on the wafer surface, generating micron-level high-intensity focused spot illumination. Scattered light from the wafer surface is collected by the dark-field objective, and the intensity is measured by the photodiode. Reflected light from the wafer surface is collected by the bright-field objective. After polarization splitting, it is measured by two four-quadrant detectors to analyze the topography, film, and reflected signal. The turntable and linear guide drive the optical head and wafer, allowing the focused spot to scan along the wafer in a spiral trajectory, enabling fast and accurate detection. The defect inspection system has been verified through experiments. The minimum detectable PSL particle size is less than 200 nm, the minimum detectable scratch width is less than <inline-formula> <tex-math>$1~mu $ </tex-math></inline-formula>m, and the minimum detectable stain width is less than <inline-formula> <tex-math>$20~mu $ </tex-math></inline-formula>m.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"4-11"},"PeriodicalIF":2.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489096","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}