2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)最新文献_第8页

Leveraging focus spot monitoring Data in FEOL to resolve a high impact MOL defect: DI: Defect inspection and reduction 利用FEOL中的焦点点监测数据来解决高影响MOL缺陷:DI:缺陷检查和减少

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373181

F. Khatkhatay, Chih-chieh Huang, Ludmila Popova, Jongyoon Yoon, Thomas Zalocha, Phillip Tatti, Krishan Gopal, Hongliang Shen, Ho Young Song, Amit Gupta

{"title":"Leveraging focus spot monitoring Data in FEOL to resolve a high impact MOL defect: DI: Defect inspection and reduction","authors":"F. Khatkhatay, Chih-chieh Huang, Ludmila Popova, Jongyoon Yoon, Thomas Zalocha, Phillip Tatti, Krishan Gopal, Hongliang Shen, Ho Young Song, Amit Gupta","doi":"10.1109/ASMC.2018.8373181","DOIUrl":"https://doi.org/10.1109/ASMC.2018.8373181","url":null,"abstract":"As feature sizes shrink in 14nm technology and beyond, focus window limited immersion lithography steps like those in the middle of line (MOL) are most susceptible to hotspots caused by small localized variations in wafer topography. Hotspots in MOL are almost always killer, with a near 100% probability of the entire die failing due to the presence of a single hotspot defect. Focus spots are identified by the leveling system in the lithography scanner and the reported as a fault detection and classification (FDC) signal, driving wafer disposition and tool actions. Comprehensive reporting of focus spot signals opens up the possibility of utilizing high volume focus spot data in lieu of defect data where spatially unique hotpsot signals may be lost in the random baseline defectivity. We have analyzed focus spot data collected at a lower impact front end of line (FEOL) step to drive down hotspots at a high impact MOL step, both of which are affected by incoming defectivity due to repeat passes on a common identified tool. This work is an example of the smart resolution of a cross-module contamination issue by innovatively leveraging high volume focus spot monitoring data.","PeriodicalId":349004,"journal":{"name":"2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126916723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The value and effectiveness of sensor trace analytics in solving yield impact issues: A case study 传感器跟踪分析在解决产量影响问题中的价值和有效性:一个案例研究

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373212

H. Lam, Michael Zhao, Ski Sim, K. K. Gan, T. Ho, Joe Lee

引用次数: 0

Inline detection for FinFET gate poly footing using e-Tilt metrology 利用e-Tilt测量技术在线检测FinFET栅极多立足点

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373196

Xiaoxiao Zhang, Mert Karakoy, K. Wu, Zhuangfei Chen, Zhenhua Ge, Navi Krishnan, Amit Siany, S. Levi, I. Schwarzband, R. Kris

{"title":"Inline detection for FinFET gate poly footing using e-Tilt metrology","authors":"Xiaoxiao Zhang, Mert Karakoy, K. Wu, Zhuangfei Chen, Zhenhua Ge, Navi Krishnan, Amit Siany, S. Levi, I. Schwarzband, R. Kris","doi":"10.1109/ASMC.2018.8373196","DOIUrl":"https://doi.org/10.1109/ASMC.2018.8373196","url":null,"abstract":"Advanced FinFET device architecture generates new profile control requirement in 1× node and beyond. The corner at the intersection between the Gate and Fin is critical geometry for device yield and reliability. The residue-free corner at Gate-Fin interface after Polysilicon gate etch is desirable yet also a known challenge to detect and control. Current detection methods for gate poly footing, also known as gate skirt, are destructive with long turnaround time, resulting in slow progress in process development and high-volume process control. To meet the gap, e-Tilt imaging technique along with corner rounding algorithm has been developed on CDSEM platform to visualize and quantify residue at fin-gate intersection (poly-footing). In this paper, we'll discuss the challenges, approaches, and results associated with the first-in-industry implementation of the inline poly-footing detection within a HVM Fab. Two sets of DOEs (Design of Experiment) were conducted to validate the sensitivity to Etch recipes and Etch Chambers. The accuracy and precision of the poly-footing measurement were qualified with correlation to commonly used hammer test results and eTest results. Future study includes precision and throughput improvement to meet the need of 1× node.","PeriodicalId":349004,"journal":{"name":"2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122346250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Deep reinforcement learning for semiconductor production scheduling 半导体生产调度的深度强化学习

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373191

Bernd Waschneck, André Reichstaller, Lenz Belzner, Thomas Altenmüller, T. Bauernhansl, Alexander Knapp, A. Kyek

引用次数: 67

Forging basic elements of cyber-physical systems in industry 4.0 with parametric characterization for FDC 通过FDC的参数化表征，锻造工业4.0中信息物理系统的基本元素

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373145

K. Hui, Leo Ke, S. Sheen

引用次数: 2

Nanoprober image based localization techniques for SOI technology 基于纳米探针图像的SOI定位技术

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373218

S. Pendyala, S. Lucarini, M. Tenney

引用次数: 1

A systematic approach to secure data collection across an OEM's fleet of tools 一种系统的方法来确保OEM的工具车队的数据收集

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373164

Doug Suerich, Veronica Consens

引用次数: 1

A multi-factorial approach for middle-of-line design rule validation and optimization in 22FDX® 22FDX®中线设计规则验证和优化的多因素方法

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373216

B. Ramadout, D. Wehella-gamage, T. Staiger, K. Babich, H. Moll, J. Wallner, O. Patterson

引用次数: 3

Effective Epi process window monitoring by high resolution massive CDU metrology: Topic: AM (Advanced metrology) 通过高分辨率大规模CDU计量有效监测Epi过程窗口:主题:AM(高级计量)

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373152

Z. Y. Chen, T. Y. Chen, I. Holcman, Bruce Tseng

{"title":"Effective Epi process window monitoring by high resolution massive CDU metrology: Topic: AM (Advanced metrology)","authors":"Z. Y. Chen, T. Y. Chen, I. Holcman, Bruce Tseng","doi":"10.1109/ASMC.2018.8373152","DOIUrl":"https://doi.org/10.1109/ASMC.2018.8373152","url":null,"abstract":"Advanced technology with shrinking design rules and increasing multi-patterning steps introduce challenging process tolerances. Process marginality is in the form of Edge Placement Errors (EPE), Line Edge Roughness (LER), Line Width Roughness (LWR), Overlay and process induced pattern defects. Advanced metrology and monitoring techniques are needed to trace process induced critical pattern distortion within die, field and wafer. Massive sampling of critical pattern features (i.e. hot spots) measurements can be the solution for tight process margins monitoring. Many measurements across die, field and wafer can reveal clear signature of the process tools from film deposition, lithography, etch, chemical mechanical polishing and epitaxial growth indicate immerging process window marginality issues. Clear signature can be built from thousands to millions measurements. High resolution and throughput metrology tool is needed to provide capabilities in critical dimension uniformity (CDU) and overlay shift for Edge Placement Errors. Channel strain in continuous fin pitch reduction introduces new challenges in crystalline epitaxy growth. It is one of the most challenging tasks in process monitoring and control for lateral growth dimension, abnormal growth and shapes. Epi growth monitoring includes many measurement types and defect detection. A study of new monitoring method based on massive hotspot and CDU measurements is introduced. Examples of its implementation in Epitaxial growth module are provided for engineers to make effective actions in time.","PeriodicalId":349004,"journal":{"name":"2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123110526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Optimization of BEOL R&C monitoring macros for accurate representation of circuit performance 优化BEOL R&C监控宏，以准确表示电路性能

2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2018-04-01 DOI: 10.1109/ASMC.2018.8373150

Dewei Xu, R. Srivastava, Ushasree Katakamsetty, E. G. de la Garza, H. Kim, R. Augur, R. Fox

引用次数: 0