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公开(公告)号：US20240333739A1
公开(公告)日：2024-10-03
申请号：US18192812
申请日：2023-03-30
发明人： Bhavna Agrawal , Robert Jeffrey Baseman , Jeffrey Owen Kephart , Anuradha Bhamidipaty , Elham Khabiri , Yingjie Li , Srideepika Jayaraman
CPC分类号： H04L63/1425 , H04L41/16
摘要： Detecting and mitigating anomalous system behavior by providing a machine learning model comprising a knowledge graph depicting system entity relationships, and modeling behavioral correlations among system entities according to historical time-series data, receiving real-time time-series data for the system, detecting an anomalous system behavior in a system locale, according to the real-time time-series data, according to the machine learning model and multivariate sensor metrics, diagnosing the anomalous system behavior according to an upstream portion of the knowledge graph and a statistical behavior model for the system locale, and mitigating the anomalous behavior by deriving a recommended action according to the anomalous behavior and generating a work order to implement the recommended action.
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公开(公告)号：US20230281363A1
公开(公告)日：2023-09-07
申请号：US17685645
申请日：2022-03-03
IPC分类号： G06F30/27
CPC分类号： G06F30/27 , G06F2111/06
摘要： A system and method for optimizing materials and devices design. The method includes building machine learning models to predict a quality of target measurements based on an experimental design input by formulating a regularized multi-objective optimization to recommend the final experimental design using a logistic curve for the loss function and a model uncertainty quantification term for the final solution. Alternately, the system and method uses a black-box optimization for optimal process design that includes iteratively building a sequence of surrogate functions, where intermediate designs are generated to improve the quality of the surrogate function. Further a derivative-free optimization is performed that utilizes global optimization techniques (global search) with Gaussian process (local method) with a Bayesian optimization to produce a sequence of designs that leads to an optimal design. The system and method is used in machine learning/deep learning for tuning hyperparameters and an architecture search of prediction models.
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3.发明授权Method and system for wafer quality predictive modeling based on multi-source information with heterogeneous relatedness 有权基于具有异构相关性的多源信息的晶圆质量预测建模方法和系统公开(公告)号：US09176183B2
公开(公告)日：2015-11-03
申请号：US13651974
申请日：2012-10-15
发明人： Yada Zhu , Jingrui He , Robert Jeffrey Baseman
IPC分类号： G06F19/00 , G05B13/02 , G06F17/00 , G06F11/00 , H01L21/00 , G06F7/60 , G06F17/50 , G06F11/16 , G06F11/22 , G01R31/28 , H01L21/66
CPC分类号： G01R31/2831 , H01L22/14 , H01L22/20 , H01L2924/0002 , H01L2924/00
摘要： The present invention generally relates to the monitoring and controlling of a semiconductor manufacturing environment and, more particularly, to methods and systems for virtual meteorology (VM) applications based on data from multiple tools having heterogeneous relatedness. The methods and systems leverage the natural relationship of the multiple tools and take advantage of the relationship embedded in process variables to improve the prediction performance of the VM predictive wafer quality modeling. The prediction results of the methods and systems can be used as a substitute for or in conjunction with actual metrology samples in order to monitor and control a semiconductor manufacturing environment, and thus reduce delays and costs associated with obtaining actual physical measurements.
摘要翻译： 本发明一般涉及半导体制造环境的监视和控制，更具体地说，涉及基于具有异构相关性的多个工具的数据的虚拟气象（VM）应用的方法和系统。 该方法和系统利用了多种工具的自然关系，利用嵌入在过程变量中的关系来提高VM预测晶圆质量建模的预测性能。 方法和系统的预测结果可以用作实际计量样本的替代或与实际计量样本结合，以便监测和控制半导体制造环境，从而减少与获得实际物理测量相关联的延迟和成本。
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4.发明申请Method and System for Wafer Quality Predictive Modeling based on Multi-Source Information with Heterogeneous Relatedness 有权基于具有异构相关性的多源信息的晶片质量预测建模方法与系统公开(公告)号：US20140107824A1
公开(公告)日：2014-04-17
申请号：US13677542
申请日：2012-11-15
发明人： Yada Zhu , Jingrui He , Robert Jeffrey Baseman
IPC分类号： G06F19/00
CPC分类号： G01R31/2831 , H01L22/14 , H01L22/20 , H01L2924/0002 , H01L2924/00
摘要： The present invention generally relates to the monitoring and controlling of a semiconductor manufacturing environment and, more particularly, to methods and systems for virtual meteorology (VM) applications based on data from multiple tools having heterogeneous relatedness. The methods and systems leverage the natural relationship of the multiple tools and take advantage of the relationship embedded in process variables to improve the prediction performance of the VM predictive wafer quality modeling. The prediction results of the methods and systems can be used as a substitute for or in conjunction with actual metrology samples in order to monitor and control a semiconductor manufacturing environment, and thus reduce delays and costs associated with obtaining actual physical measurements.
摘要翻译： 本发明一般涉及半导体制造环境的监视和控制，更具体地说，涉及基于具有异构相关性的多个工具的数据的虚拟气象（VM）应用的方法和系统。 该方法和系统利用了多种工具的自然关系，利用嵌入在过程变量中的关系来提高VM预测晶圆质量建模的预测性能。 方法和系统的预测结果可以用作实际计量样本的替代或与实际计量样本结合，以便监测和控制半导体制造环境，从而减少与获得实际物理测量相关联的延迟和成本。
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公开(公告)号：US20240047279A1
公开(公告)日：2024-02-08
申请号：US17817688
申请日：2022-08-05
发明人： Robert Jeffrey Baseman , Elham Khabiri , Anuradha Bhamidipaty , Yingjie Li , Srideepika Jayaraman , Bhavna Agrawal , Jeffrey Owen Kephart
CPC分类号： H01L22/20 , G06K9/6232 , H01L21/67253 , H01L22/10
摘要： Embodiments of the invention are directed to a computer-implemented method. A non-limiting example of the computer-implemented method includes accessing, using a processor system, a process-step sequence that includes a plurality process-steps and a plurality of queue-times. A process-step sequence mining operation is applied to the process-step sequence, wherein the process-step sequence mining operation is operable to make a prediction of an impact of a portion of the process-step sequence on a characteristic of a product generated by the process-step sequence.
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公开(公告)号：US20230281364A1
公开(公告)日：2023-09-07
申请号：US17686708
申请日：2022-03-04
IPC分类号： G06F30/27
CPC分类号： G06F30/27 , G06F2111/08
摘要： A system and method for learning a predictive function that can automatically learn different operating modes for a multi-modal system and predict the number of operating states for a multi-modal system and additionally the detailed structure for each state. Once learned, the predictive function (model) can be used to determine a mode of a new sample (an asset). Based on the determined components that maximize a log likelihood function, a mode of the new sample is detected into the model via dependency graphs. One aspect includes enforcing a lower bound for the number of sample points to form an operational mode for an asset. While a mode relates to sample points which maximizes like log-likelihood, an ability is provided to remove artifact modes due to noisy data by considering a sufficient sample data condition and maximizing log-likelihood. Domain knowledge can be incorporated into the model via dependency graphs.
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公开(公告)号：US11599690B2
公开(公告)日：2023-03-07
申请号：US16933972
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
IPC分类号： G06F30/20 , G06F30/27 , G06F40/40 , G01N21/95 , G06F111/10
摘要： A computing device includes a processor and a storage device. A wafer asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts identifying and clustering a plurality of assets based on static properties of a wafer asset using a first module of the wafer asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the wafer asset using a second module of the wafer asset modeling module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module of the wafer asset modeling module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the wafer asset and/or an event based on past patterns. Prediction information is stored in the storage device.
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公开(公告)号：US12073152B2
公开(公告)日：2024-08-27
申请号：US16933977
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
摘要： A computing device includes a processor and a storage device. A vehicle asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts of identifying and clustering a plurality of assets based on static properties of a vehicle asset using a first module of the vehicle asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the vehicle asset using a second module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the vehicle asset and/or an event based on past patterns. Prediction information is stored in the storage device.
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公开(公告)号：US20220019710A1
公开(公告)日：2022-01-20
申请号：US16933972
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
摘要： A computing device includes a processor and a storage device. A wafer asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts identifying and clustering a plurality of assets based on static properties of a wafer asset using a first module of the wafer asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the wafer asset using a second module of the wafer asset modeling module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module of the wafer asset modeling module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the wafer asset and/or an event based on past patterns. Prediction information is stored in the storage device.
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公开(公告)号：US20220019708A1
公开(公告)日：2022-01-20
申请号：US16933977
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
摘要： A computing device includes a processor and a storage device. A vehicle asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts of identifying and clustering a plurality of assets based on static properties of a vehicle asset using a first module of the vehicle asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the vehicle asset using a second module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the vehicle asset and/or an event based on past patterns. Prediction information is stored in the storage device.
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