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Patent Agency Ranking
公开(公告)号：US20160163035A1
公开(公告)日：2016-06-09
申请号：US14956326
申请日：2015-12-01
Applicant: KLA-TENCOR CORPORATION
Inventor： Wei Chang , Ramon Olavarria , Krishna Rao
CPC classification number: G06T7/0004 , G06K9/4628 , G06K9/6255 , G06K9/6274 , G06K9/66 , G06K2209/19 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
Abstract: Systems and methods for defection classification in a semiconductor process are provided. The system includes a communication line configured to receive a defect image of a wafer from the semiconductor process and a deep-architecture neural network in electronic communication with the communication line. The neural network has a first convolution layer of neurons configured to convolve pixels from the defect image with a filter to generate a first feature map. The neural network also includes a first subsampling layer configured to reduce the size and variation of the first feature map. A classifier is provided for determining a defect classification based on the feature map. The system may include more than one convolution layers and/or subsampling layers. A method includes extracting one or more features from a defect image using a deep-architecture neural network, for example a convolutional neural network.
Abstract translation: 提供半导体工艺中的缺陷分类的系统和方法。 该系统包括被配置为从半导体处理接收晶片的缺陷图像的通信线路和与通信线路进行电子通信的深层架构神经网络。 神经网络具有第一卷积层的神经元，其被构造成用来过滤来自缺陷图像的像素来卷积第一特征图。 神经网络还包括被配置为减小第一特征图的大小和变化的第一子采样层。 提供了一种基于特征图来确定缺陷分类的分类器。 系统可以包括多于一个卷积层和/或子采样层。 一种方法包括使用深层架构神经网络（例如卷积神经网络）从缺陷图像中提取一个或多个特征。
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2.发明授权Statistical overlay error prediction for feed forward and feedback correction of overlay errors, root cause analysis and process control 有权反馈前馈和反馈校正的统计叠加误差预测，根本原因分析和过程控制公开(公告)号：US09087176B1
公开(公告)日：2015-07-21
申请号：US14220665
申请日：2014-03-20
Applicant: KLA-Tencor Corporation
Inventor： Wei Chang , Krishna Rao , Joseph Gutierrez , Ramon Olavarria , Craig Macnaughton , Amir Azordegan , Prasanna Dighe , Jaydeep Sinha
CPC classification number: G03F7/705 , G03F1/144 , G03F1/36 , G03F7/70633 , G06F17/5068 , G06F17/5081 , G06F2217/12 , G06N5/04 , G06N99/005 , Y02P90/265
Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.
Abstract translation: 收集数据和训练，验证和部署统计模型以使用图案化晶片几何数据和其他相关信息来预测覆盖误差的方法包括选择训练晶片组，在多个光刻步骤测量并计算几何差异，将多个预测模型应用于 训练晶片几何差异并将预测覆盖与训练晶片组上测量的覆盖层进行比较。 识别最准确的预测模型，并将结果反馈到光刻扫描仪工具，可以校正这些影响并减少晶片扫描和曝光过程中的重叠误差。
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公开(公告)号：US10650508B2
公开(公告)日：2020-05-12
申请号：US14956326
申请日：2015-12-01
Applicant: KLA-TENCOR CORPORATION
Inventor： Wei Chang , Ramon Olavarria , Krishna Rao
Abstract: Systems and methods for defection classification in a semiconductor process are provided. The system includes a communication line configured to receive a defect image of a wafer from the semiconductor process and a deep-architecture neural network in electronic communication with the communication line. The neural network has a first convolution layer of neurons configured to convolve pixels from the defect image with a filter to generate a first feature map. The neural network also includes a first subsampling layer configured to reduce the size and variation of the first feature map. A classifier is provided for determining a defect classification based on the feature map. The system may include more than one convolution layers and/or subsampling layers. A method includes extracting one or more features from a defect image using a deep-architecture neural network, for example a convolutional neural network.
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公开(公告)号：US10545412B2
公开(公告)日：2020-01-28
申请号：US15123980
申请日：2015-03-05
Applicant: KLA-Tencor Corporation
Inventor： Wei Chang , Krishna Rao , Joseph Gutierrez , Ramon Olavarria , Craig MacNaughton , Amir Azordegan , Prasanna Dighe
Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.
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5.发明申请Statistical Overlay Error Prediction for Feed Forward and Feedback Correction of Overlay Errors, Root Cause Analysis and Process Control 审中-公开统计叠加误差预测的前馈和反馈校正重叠错误，根本原因分析和过程控制公开(公告)号：US20170017162A1
公开(公告)日：2017-01-19
申请号：US15123980
申请日：2015-03-05
Applicant: KLA-Tencor Corporation
Inventor： Wei Chang , Krishna Rao , Joseph Gutierrez , Ramon Olavarria , Craig MacNaughton , Amir Azordegan , Prasanna Dighe
CPC classification number: G03F7/705 , G03F1/144 , G03F1/36 , G03F7/70633 , G06F17/5068 , G06F17/5081 , G06F2217/12 , G06N5/04 , G06N20/00 , Y02P90/265
Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.
Abstract translation: 收集数据和训练，验证和部署统计模型以使用图案化晶片几何数据和其他相关信息来预测覆盖误差的方法包括选择训练晶片组，在多个光刻步骤测量并计算几何差异，将多个预测模型应用于 训练晶片几何差异并将预测覆盖与训练晶片组上测量的覆盖层进行比较。 识别最准确的预测模型，并将结果反馈到光刻扫描仪工具，可以校正这些影响并减少晶片扫描和曝光过程中的重叠误差。
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