IEEE Transactions on Semiconductor Manufacturing最新文献

Call for Papers for a Special Issue of IEEE Transactions on Electron Devices on "Wide and Ultrawide Band Gap Semiconductor Devices for RF and Power Applications" IEEE电子器件学报特刊“用于射频和功率应用的宽带和超宽带隙半导体器件”征文

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IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-08-04 DOI: 10.1109/TSM.2023.3277155

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IEEE Transactions on Semiconductor Manufacturing Information for Authors IEEE半导体制造信息汇刊

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IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-08-04 DOI: 10.1109/TSM.2023.3277157

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Call for Papers: 8th IEEE Electron Devices Technology and Manufacturing (EDTM) Conference 2024 第8届IEEE电子器件技术与制造(EDTM)会议2024

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IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-08-04 DOI: 10.1109/TSM.2023.3301288

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Guest Editorial Special Section on the 2022 SEMI Advanced Semiconductor Manufacturing Conference 2022年SEMI先进半导体制造会议特邀编辑专区

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IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-08-04 DOI: 10.1109/TSM.2023.3297059

Jeanne Paulette Bickford;Oliver D. Patterson;Delphine Le Cunff;Ralf Buengener;Stefan Radloff;Paul Werbaneth

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IEEE Transactions on Semiconductor Manufacturing Publication Information IEEE半导体制造学报

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IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-08-04 DOI: 10.1109/TSM.2023.3277151

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IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-08-04 DOI: 10.1109/TSM.2023.3298262

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Improvement of Plasma Etching Endpoint Detection With Data-Driven Wavelength Selection and Gaussian Mixture Model 基于数据驱动波长选择和高斯混合模型的等离子体蚀刻端点检测改进

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-07-14 DOI: 10.1109/TSM.2023.3295356

Chae Sun Kim;Hye Ji Lee;Hae Rang Roh;Taekyoon Park;Yongseok Lee;Jewoo Han;Sungun Kwon;Chanmin Lee;Jongwoo Sun;Kukhan Yoon;Jong Min Lee

{"title":"Improvement of Plasma Etching Endpoint Detection With Data-Driven Wavelength Selection and Gaussian Mixture Model","authors":"Chae Sun Kim;Hye Ji Lee;Hae Rang Roh;Taekyoon Park;Yongseok Lee;Jewoo Han;Sungun Kwon;Chanmin Lee;Jongwoo Sun;Kukhan Yoon;Jong Min Lee","doi":"10.1109/TSM.2023.3295356","DOIUrl":"https://doi.org/10.1109/TSM.2023.3295356","url":null,"abstract":"The signal-to-noise ratio of optical emission spectroscopy (OES) data has decreased as the plasma etching process has advanced. As a result, not only the advanced endpoint detection method was required, but also the selection of more informative wavelengths. This paper proposes an improved endpoint detection algorithm by combining data-driven wavelength selection and a Gaussian mixture model (GMM). The data-driven wavelength selection algorithm finds the correlation between training data and a sigmoid function of time. Then, using the fitted GMM of the training data in latent space, the endpoint of the test data is determined in real-time. The proposed algorithm’s performance was evaluated using real OES data, comprised of seven operations. The correlation-based wavelength selection algorithm significantly reduced detection error by 70.2% when compared to the conventional method, which selects a few wavelengths manually based on prior knowledge. Additionally, the GMM detection method clustered OES data from low open area wafers much more clearly than the recently proposed method using GMM. This demonstrates that combining correlation-based wavelength selection with GMM is an effective method for detecting endpoints during plasma etching of small open area wafers.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"36 3","pages":"389-397"},"PeriodicalIF":2.7,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3506068","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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Spatial and Channel-Wise Co-Attention-Based Twin Network System for Inspecting Integrated Circuit Substrate 基于空间和通道双向关注的集成电路衬底检测双网络系统

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-06-26 DOI: 10.1109/TSM.2023.3289294

Eunjeong Choi;Jeongtae Kim

{"title":"Spatial and Channel-Wise Co-Attention-Based Twin Network System for Inspecting Integrated Circuit Substrate","authors":"Eunjeong Choi;Jeongtae Kim","doi":"10.1109/TSM.2023.3289294","DOIUrl":"https://doi.org/10.1109/TSM.2023.3289294","url":null,"abstract":"We propose a deep learning-based reference comparison system based on a twin network (also known as a Siamese network) for high-performance inspection of integrated circuit (IC) substrates. However, reference comparison-based inspection methods may suffer from false positives when inspecting image pairs with variations, such as mis-registration and color changes. To address these problems, we also propose a novel co-attention module that jointly considers the spatial-wise and channel-wise correlations between a feature block in one image and all other feature blocks in the other image to find similar feature blocks in the other image. By comparing the feature block in one image with similar feature blocks in the other image, the module can reduce the differences in areas where registration errors and/or color variation exist, thereby making the proposed inspection method more robust to image variation than existing methods. We verified the usefulness of the proposed method through experiments using an IC substrate dataset. In the experiments, the proposed method achieved significantly improved performance compared with existing methods in terms of precision and f1-score when the recall is almost the same.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"36 3","pages":"434-444"},"PeriodicalIF":2.7,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3505928","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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Virtual Metrology Modeling for Wafer Edges via Graph Attention Networks 基于图关注网络的晶圆边缘虚拟计量建模

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-06-12 DOI: 10.1109/TSM.2023.3284817

Jaehyeon Joo;Keun Woo Yang;Yeoung Je Choi;Byungwook Min;Chang Ouk Kim

{"title":"Virtual Metrology Modeling for Wafer Edges via Graph Attention Networks","authors":"Jaehyeon Joo;Keun Woo Yang;Yeoung Je Choi;Byungwook Min;Chang Ouk Kim","doi":"10.1109/TSM.2023.3284817","DOIUrl":"https://doi.org/10.1109/TSM.2023.3284817","url":null,"abstract":"Quality monitoring is an essential element of defect detection in semiconductor manufacturing processes, but semiconductor companies use virtual metrology (VM) in addition to actual metrology to prevent productivity degradation due to the time and costs required to obtain measurements. Past VM studies aimed to predict average wafer measurement values via equipment sensor data and focused on achieving improved predictive performance by selecting or extracting important variables among high-dimensional variables such as equipment sensor data. However, the management of wafer chip quality requires not only average measurement values but also measurement value predictions for chips located at the edges, which are vulnerable to defects. In this paper, we therefore propose a graph attention (GAT) network-based VM model that predicts the measurement values of chips located at wafer edges by constructing graph data with measurement data (i.e., the measurement location information in wafers and the measurement values). To verify the performance of the proposed model, we conduct a comparative experiment with conventional machine learning methods. The experimental results show that the proposed VM model contributes to a predictive performance improvement in terms of the measurement values of chips located at wafer edges.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"36 3","pages":"359-366"},"PeriodicalIF":2.7,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"3493481","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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Novel Control Method and Applications for Negative Mode E-Beam Inspection 负模电子束检测的新控制方法及应用

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-06-09 DOI: 10.1109/TSM.2023.3284367

Oliver D. Patterson;Datong Zhang;Ralf Buengener;Guanchen He;Yufei Duan;Joy Chu;Brian Sheumaker

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