IEEE Transactions on Semiconductor Manufacturing最新文献_第9页

IEEE Transactions on Semiconductor Manufacturing Publication Information 电气和电子工程师学会半导体制造期刊》出版信息

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IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-02-05 DOI: 10.1109/TSM.2023.3334410

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Call for Papers for IEEE Transactions on Materials for Electron Devices 电气和电子工程师学会《电子器件材料学报》征稿启事

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IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-02-05 DOI: 10.1109/TSM.2024.3359520

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Joint Call for Papers for IEEE Transactions on Semiconductor Manufacturing and IEEE Transactions on Electron Devices: Special Issue on Semiconductor Design for Manufacturing (DFM) IEEE Transactions on Semiconductor Manufacturing》和《IEEE Transactions on Electron Devices》杂志联合征稿：半导体制造设计 (DFM) 特刊

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IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-02-05 DOI: 10.1109/TSM.2024.3356972

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SnS₂ and ZnO Nanocomposite Prepared by Dispersion Method for Photodetector Application 用分散法制备的用于光探测器的 SnS₂ 和 ZnO 纳米复合材料

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-12-27 DOI: 10.1109/TSM.2023.3347606

Ajay Kumar Dwivedi;Satyabrata Jit;Shweta Tripathi

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Integrated Scheduling of Jobs, Tools, Machines, and Two Different Set of Transbots 工作、工具、机器和两组不同的横向机器人的综合调度

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-12-15 DOI: 10.1109/TSM.2023.3343633

Andy Ham;Myoung-Ju Park;John Fowler

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A Model Averaging Prediction of Two-Way Functional Data in Semiconductor Manufacturing 半导体制造中双向功能数据的平均预测模型

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-12-06 DOI: 10.1109/TSM.2023.3339731

Soobin Kim;Youngwook Kwon;Joonpyo Kim;Kiwook Bae;Hee-Seok Oh

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A Unified Machine Learning Through Focus Resist 3-D Structure Model 通过 Focus Resist 三维结构模型进行统一机器学习

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-12-06 DOI: 10.1109/TSM.2023.3340110

Mingyang Xia;Yan Yan;Chen Li;Xuelong Shi

{"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":"37 1","pages":"59-66"},"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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Machine Learning on Multiplexed Optical Metrology Pattern Shift Response Targets to Predict Electrical Properties 利用多路复用光学计量模式偏移响应目标的机器学习来预测电气特性

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-12-05 DOI: 10.1109/TSM.2023.3339330

Thomas J. Ashby;Vincent Truffert;Dorin Cerbu;Kit Ausschnitt;Anne-Laure Charley;Wilfried Verachtert;Roel Wuyts

{"title":"Machine Learning on Multiplexed Optical Metrology Pattern Shift Response Targets to Predict Electrical Properties","authors":"Thomas J. Ashby;Vincent Truffert;Dorin Cerbu;Kit Ausschnitt;Anne-Laure Charley;Wilfried Verachtert;Roel Wuyts","doi":"10.1109/TSM.2023.3339330","DOIUrl":"https://doi.org/10.1109/TSM.2023.3339330","url":null,"abstract":"Doing high throughput high accuracy metrology in small geometries is challenging. One approach is to build easily measurable proxy targets onto dies and make a predictive model based on those signals. We use optical Pattern Shift Response (PSR) proxy targets to build predictive models of the electrical characteristics of devices in the Back End Of Line (BEOL). Given the wide choice of PSR targets, we explore how to select combinations of them to maximise the utility of the features for building an accurate Machine Learning (ML) model; we call this approach Multiplexed Optical Metrology. We also explore the trade-off between chip area dedicated to targets and achievable accuracy. We run ML experiments using different selections of targets measured at different stages of BEOL processing: post-lithography and post-Chemical-Mechanical-Planarisation (CMP). Our results show that a) reasonable predictive performance can be achieved for a reasonable area budget; b) ML model performance across target families varies significantly, thus justifying the need for careful selection of targets; c) longitudinal measurements of targets increases accuracy for no extra area penalty; d) increasing the number of targets gives some improvement in accuracy for a dataset of this size, but relatively small compared to the increase in area budget needed. Ultimately we aim to do die-level yield prediction using these techniques. We discuss how collecting a larger dataset with appropriate yield information is the logical next step to achieving this.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"46-58"},"PeriodicalIF":2.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10342754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695064","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}

引用次数: 0

Coherent Fourier Scatterometry for Detection of Killer Defects on Silicon Carbide Samples 相干傅立叶散射测量法检测碳化硅样品上的杀手缺陷

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-11-29 DOI: 10.1109/TSM.2023.3337720

Jila Rafighdoost;Dmytro Kolenov;Silvania F. Pereira

{"title":"Coherent Fourier Scatterometry for Detection of Killer Defects on Silicon Carbide Samples","authors":"Jila Rafighdoost;Dmytro Kolenov;Silvania F. Pereira","doi":"10.1109/TSM.2023.3337720","DOIUrl":"10.1109/TSM.2023.3337720","url":null,"abstract":"It has been a widely growing interest in using silicon carbide (SiC) in high-power electronic devices. Yet, SiC wafers may contain killer defects that could reduce fabrication yield and make the device fall into unexpected failures. To prevent these failures from happening, it is very important to develop inspection tools that can detect, characterize and locate these defects in a non-invasive way. Current inspection techniques such as Dark Field or Bright field microscopy are effectively able to visualize most such defects; however, there are some scenarios where the inspection becomes problematic or almost impossible, such as when the defects are too small or have low contrast or if the defects lie deep into the substrate. Thus, an alternative method is needed to face these challenges. In this paper, we demonstrate the application of coherent Fourier scatterometry (CFS) as a complementary tool in addition to the conventional techniques to overcome different and problematic scenarios of killer defects inspection on SiC samples. Scanning electron microscopy (SEM) has been used to assess the same defects to validate the findings of CFS. Great consistency has been demonstrated in the comparison between the results obtained with CFS and SEM.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"124-128"},"PeriodicalIF":2.7,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256281","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}

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Single-Mask Fabrication of Sharp SiOx Nanocones 单掩模制造尖锐氧化硅纳米锥体

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-11-28 DOI: 10.1109/TSM.2023.3336169

Eric Herrmann;Xi Wang

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