IEEE Transactions on Semiconductor Manufacturing最新文献

{"title":"Defect Detection of Photovoltaic Panels to Suppress Endogenous Shift Phenomenon","authors":"Haiyong Chen;Yaxiu Zhang;Yan Zhang;Xingwei Yan;Xin Zhang;Kunlin Zou","doi":"10.1109/TSM.2024.3510358","DOIUrl":"https://doi.org/10.1109/TSM.2024.3510358","url":null,"abstract":"Efficient and intelligent surface defect detection of photovoltaic modules is crucial for improving the quality of photovoltaic modules and ensuring the reliable operation of large-scale infrastructure. However, the scenario characteristics of data distribution deviation make the construction of defect detection models for open world scenarios such as photovoltaic manufacturing and power plant inspections a challenge. Therefore, we propose the Gather and Distribute Domain shift Suppression Network. It adopts a single domain generalized method that is completely independent of the test samples to address the problem of distribution shift. Using a one-stage network as the baseline network breaks through the limitations of traditional domain generalization methods that typically use two-stage networks. It not only balances detection accuracy and speed but also simplifies the model deployment and application process. The network first employs the DeepSpine module to capture a wider range of contextual information. By concatenating and aligning multi-scale channel features, it effectively suppresses background style shifts. Building upon this, the Gather and Distribute Module performs cross layer interactive learning on multi-scale channel features. The multi-level features and semantic dependencies learned enhance the localization and recognition ability of target defects, thereby achieving the suppression of defect instance shift. Furthermore, we utilizes normalized Wasserstein distance for similarity measurement, reducing measurement errors caused by bounding box position deviations. We conducted a comprehensive evaluation of our network on the Electroluminescence Endogenous Shift Dataset and Photovoltaic Inspection Infrared Dataset. In scenarios with three production lines and four heights on two datasets, the detection accuracy of GDDS reached 91.2%, 82.3%, 79.9%, and 92.8%, 82.7%, 77.2%, and 69.2%, respectively. The experimental results showed that our method can adapt to defect detection in open world scenarios faster and better than other state-of-the-art methods.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"83-95"},"PeriodicalIF":2.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489082","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":"Product Design Enhancement With Test Structures for Non-Contact Detection of Yield Detractors","authors":"Tomasz Brozek;Stephen Lam;Christopher Hess;Larg Weiland;Matthew Moe;Xumin Shen;John Chen;Indranil De;Marcin Strojwas;Andrzej Strojwas;John K. Kibarian","doi":"10.1109/TSM.2024.3510232","DOIUrl":"https://doi.org/10.1109/TSM.2024.3510232","url":null,"abstract":"Detection and monitoring of the yield loss mechanisms and defects in product chips have been a subject of extensive efforts, resulting in multiple useful Design-for-Manufacturing (DFM) and Design-for-Test (DFT) techniques. Defect inspection techniques extend optical inspection further into sub-10 nm nodes, but many buried defects are formed as a result of multi-layer 3-D interaction, and they are difficult to detect by surface optical scans. In case of a functional failure related to a defect (an open or a short), the localization of the fail site for failure analysis and root cause identification is often difficult, especially for random logic design. In this paper we describe a new -DFM methodology which inserts into the product design special test structures to support New Product Introduction (NPI) and a product yield ramp. The structures are part of PDF Solutions’ proprietary Design-for-Inspection (DFI) system with no penalty to the product layout. They are designed to be electrically tested in a non-contact way using a dedicated and specially optimized e-Beam tool. The layouts of these structures are based on the standard cell design therefore they can be used as filler cells in standard cell-based logic designs. The paper presents the concept of the test structures and their design to cover specific failure modes and enable fail mechanism identification. We describe the design flow to integrate the structures into the product floorplan and the non-contact test methodology to scan product wafers and detect failures. Finally, we demonstrate usage of such DFI structures and provide results collected from scanning product wafers containing embedded DFI filler cells.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 2","pages":"126-133"},"PeriodicalIF":2.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896465","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":"HotspotFusion: A Generative AI Approach to Predicting CMP Hotspot in Semiconductor Manufacturing","authors":"Hsiu-Hui Hsiao;Kung-Jeng Wang","doi":"10.1109/TSM.2024.3510376","DOIUrl":"https://doi.org/10.1109/TSM.2024.3510376","url":null,"abstract":"The semiconductor industry thrives on rapid technological advancements, crucial for superior product performance and cost efficiency. Chip design houses and consumer electronics companies must continuously pursue New Tape Out (NTO) to maintain technological leadership. Timely NTO completion expedites product launches, crucial in the competitive semiconductor market. This paper addresses Chemical Mechanical Polishing (CMP) hotspot, critical in NTO quality and cycle time, affecting wafer surface topology. Hotspot defects can degrade wafer performance, demanding swift detection and resolution. Traditional methods can only identify CMP hotspot after manufacturing, necessitating repeated adjustments to IC design. We propose HotspotFusion, leveraging pattern density data from Graphic Design System (GDS) to predict CMP hotspot early in the design phase. Utilizing a generative AI model, HotspotFusion significantly reduces NTO cycle time by enabling proactive hotspot detection and process optimization, fostering efficiency and competitiveness in semiconductor manufacturing.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"73-82"},"PeriodicalIF":2.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489220","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":"2024 Index IEEE Transactions on Semiconductor Manufacturing Vol. 37","authors":"","doi":"10.1109/TSM.2024.3506312","DOIUrl":"https://doi.org/10.1109/TSM.2024.3506312","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"649-667"},"PeriodicalIF":2.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10768858","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713825","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 Nominations for Editor-in-Chief: IEEE Transactions on Semiconductor Manufacturing","authors":"","doi":"10.1109/TSM.2024.3490742","DOIUrl":"https://doi.org/10.1109/TSM.2024.3490742","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"647-647"},"PeriodicalIF":2.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694657","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":"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}