2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)最新文献_第9页

A Robust Multi-Stage Scheduling Approach for Semiconductor Manufacturing Production Areas with Time Contraints 具有时间约束的半导体制造生产区域鲁棒多阶段调度方法

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791779

Christian Maleck, Gottfried Nieke, K. Bock, Detlef Pabst, M. Schulze, M. Stehli

引用次数: 1

Full Wafer Stress Metrology for Dielectric Film Characterization: Use Case 介质薄膜特性的全晶圆应力测量:用例

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791784

V. Brouzet, V. Gredy, F. Chenevas-Paule, K. Le-Chao, D. Guiheux, A. Laurent, V. Coutellier, D. Le-Cunff

引用次数: 9

Optimizing antenna voltage balancing for remote helical ICP plasma discharge using Oxygen, Hydrogen, Nitrogen, Ammonia and their mixtures : AEPM: Advanced Equipment Processes and Materials 利用氧、氢、氮、氨及其混合物优化远程螺旋ICP等离子体放电天线电压平衡:AEPM:先进设备工艺和材料

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791803

S. J. Yoon, Jongwoo Park, A. Kim

引用次数: 0

Yield Improvement Using Advanced Data Analytics 使用先进的数据分析提高产量

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791752

Armando Anaya, William Henning, Neeta Basantkumar, James Oliver

引用次数: 1

Failure Isolation in Ring Oscillator Circuit and Defect Detection in CMOS Technology Research 环形振荡器电路故障隔离与CMOS缺陷检测技术研究

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791793

V. Chan, M. Bergendahl, J. Strane, B. Austin, C. Boye, S. Mattam, S. Choi, A. Gaul, K. Cheng, A. Greene, D. Lea, T. Levin, G. Karve, S. Teehan, D. Guo

引用次数: 2

Yield Learning for Complex FinFET Defect Mechanisms Based on Volume Scan Diagnosis Results 基于体积扫描诊断结果的复杂FinFET缺陷机制良率学习

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791755

Huaxing Tang, Manish Sharma, Wu-Tung Cheng, Gaurav Veda, Douglas D. Gehringer, Matt Knowles, Jayant D'Souza, Kannan Sekar, Neerja Bawaskar, Yan Pan

{"title":"Yield Learning for Complex FinFET Defect Mechanisms Based on Volume Scan Diagnosis Results","authors":"Huaxing Tang, Manish Sharma, Wu-Tung Cheng, Gaurav Veda, Douglas D. Gehringer, Matt Knowles, Jayant D'Souza, Kannan Sekar, Neerja Bawaskar, Yan Pan","doi":"10.1109/ASMC.2019.8791755","DOIUrl":"https://doi.org/10.1109/ASMC.2019.8791755","url":null,"abstract":"Device complexity is reaching all-time highs with the adoption of high aspect ratio FinFETs created using multi- patterning process technologies. Simultaneously, new product segments such as AI and automotive are being fabricated on such advanced processes. In this dynamic environment, new complex defect modes have challenged manufacturers to ramp and sustain quality and yield at advanced nodes. Process variability of the standard cell introduces new transistor-level defect modes. Meanwhile the cost of traditional failure analysis has continued to skyrocket. How will the industry reduce the defect-rate and ramp yield to meet these aggressive market demands? This article will detail a new breakthrough in the field of scan diagnosis using machine learning. For the first time, cell-internal defects are detected, diagnosed and now resolved with RCD (Root Cause Deconvolution). Experimental FA results will show how RCD is used to build an accurate defect pareto and pick targeted die for FA for faster and cheaper root cause identification.","PeriodicalId":287541,"journal":{"name":"2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123133591","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

Scanning Frequency Comb Microscopy (SFCM) Shows Promise for Carrier Profiling at and Below the 7-nm Node 扫描频率梳状显微镜(SFCM)显示了在7纳米节点及以下载流子分析的前景

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791772

M. Hagmann, J. Wiedemeier

引用次数: 0

Simultaneous Denoising and Edge Estimation from SEM Images using Deep Convolutional Neural Networks 基于深度卷积神经网络的扫描电镜图像去噪与边缘估计

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791764

N. Chaudhary, S. Savari

{"title":"Simultaneous Denoising and Edge Estimation from SEM Images using Deep Convolutional Neural Networks","authors":"N. Chaudhary, S. Savari","doi":"10.1109/ASMC.2019.8791764","DOIUrl":"https://doi.org/10.1109/ASMC.2019.8791764","url":null,"abstract":"We propose deep convolutional neural networks LineNet1 and LineNet2 for simultaneous denoising and edge image prediction from low-dose scanning electron microscope images. Edge estimation of nanostructures from SEM images is needed for line edge roughness (LER) and line width roughness (LWR) estimation. Our method uses supervised learning datasets of single-line SEM images and multiple-line SEM images together with edge positions information for the training of LineNet1 and LineNet2. We simulate single-line and multiple-line SEM images with Poisson noise and other artifacts using the ARTIMAGEN library developed by the National Institute of Standards and Technology. The line edges were generated using the Thorsos method and the Palasantzas spectral model. The convolutional neural networks LineNet1 and LineNet2 each contain 17 con- volutional layers, 16 batch-normalization layers and 16 dropout layers. Our results show that this approach (1) facilitates edge estimation in multiple-line images and (2) significantly reduces the memory needed for edge estimation in single-line images with a slight impact on accuracy.","PeriodicalId":287541,"journal":{"name":"2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115794226","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: 6

a-Si Pinhole Detection and Characterization using Haze Monitoring : CFM: Contamination Free Manufacturing 利用雾霾监测的a-Si针孔检测和表征:CFM:无污染制造

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791809

Asli Sirman, Fuad H. Al-amoody, Chandar Palamadai, B. Saville, Ankit Jain, Kha X. Tran

引用次数: 1

Convergence towards large perimeter overlay Run-to-Run using multivariate APC system 多变量APC系统对大周长覆盖的收敛

2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) Pub Date : 2019-05-01 DOI: 10.1109/ASMC.2019.8791771

B. Duclaux, A. Pelletier, J. de-Caunes, R. Perrier, L. Babaud, M. Gatefait, Olivier Fagart, Nicolas Thivolle, Mathieu Guerabsi, J. Chapon, Bruno Perrin, C. Monget

{"title":"Convergence towards large perimeter overlay Run-to-Run using multivariate APC system","authors":"B. Duclaux, A. Pelletier, J. de-Caunes, R. Perrier, L. Babaud, M. Gatefait, Olivier Fagart, Nicolas Thivolle, Mathieu Guerabsi, J. Chapon, Bruno Perrin, C. Monget","doi":"10.1109/ASMC.2019.8791771","DOIUrl":"https://doi.org/10.1109/ASMC.2019.8791771","url":null,"abstract":"I.IntroductionWith overlay requirements getting more and more critical, a lot of work has been done in the industry to improve the overlay correction capability by using high order process corrections, corrections per exposure and heating control (lens and reticle). Another part of the overlay budget is linked to our ability to control and stabilize it through time as well as being reactive to changes via the advanced process control system of the fab (APC)[1]. This paper describes the steps taken from an individual feedback loops configuration (one technology, one or several similar layers) to large perimeter overlay run- to-run for a high-mix 300mm semiconductor logic fab[2]. First, a multivariate APC system is defined with all the specificities needed to enable a large perimeter configuration. Then, technology/layer grouping is explained as well as filters and limits settings to start the new feedback loops simulation. The simulation phase or \"learning mode\" allows to have an overview on the expected gains: enhanced reactivity to parameters drift and easier maintenance by engineers in charge of following overlay run-to-run, which indirectly leads to better overall APC performance. After overlay large perimeter activation, the alert number drastically decreases, risk of measurement sampling is minimized in the fab and a similar approach is started on energy large perimeter (CD: Critical Dimensions).","PeriodicalId":287541,"journal":{"name":"2019 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127663370","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: 4