Patents by Inventor Sukesh Janubhai Patel
Sukesh Janubhai Patel has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20140135970Abstract: A system and method for autonomously determining the impact of respective tool parameters on tool performance in a semiconductor manufacturing system is provided. A parameter impact identification system receives tool parameter and tool performance data for one or more process runs of the semiconductor fabrication system and generates a separate function for each tool parameter characterizing the behavior of a tool performance indicator in terms of a single one of the tool parameters. Each function is then scored according to how well the function predicts the actual behavior of the tool performance indicator, or based on a determined sensitivity of the tool performance indicator to changes in the single tool parameter. The tool parameters are then ranked based on these scores, and a reduced set of critical tool parameters is derived based on the ranking. The tool performance indicator can then be modeled based on this reduced set of tool parameters.Type: ApplicationFiled: November 9, 2012Publication date: May 15, 2014Applicant: TOKYO ELECTRON LIMITEDInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Wolfgang Polak, Aaron Archer Waterman, Orion Wolfe
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Patent number: 8725667Abstract: Autonomous biologically based learning tool system(s) and method(s) that the tool system(s) employs for learning and analysis of performance degradation and mismatch are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. Objectively generated knowledge gleaned from synthetic or production data can be utilized to determine a mathematical relationship among a specific output variable and a set of associated influencing variables. The generated relationship facilitates assessment of performance degradation of a set of tools, and performance mismatch among tools therein.Type: GrantFiled: March 31, 2009Date of Patent: May 13, 2014Assignee: Tokyo Electron LimitedInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima
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Patent number: 8723869Abstract: The subject disclosure relates to automatically learning relationships among a plurality of manufacturing tool parameters as applied to arbitrary semiconductor manufacturing tools and a graphical user interface that is supported, at least in part, by an autonomous learning system. The graphical user interface can create one or more matrixes based on received data and can further generate additional matrices by transforming the one or more matrixes. A series of windows can be output, wherein the series of windows, provide performance analysis that comprises a matching between a focus chamber and a reference chamber. In an aspect, the focus chamber and the reference chamber can be different chambers. In another aspect, the focus chamber and the reference chamber can be the same chamber, which provides analysis of the deterioration in performance of the same chamber over time.Type: GrantFiled: March 21, 2011Date of Patent: May 13, 2014Assignee: Tokyo Electron LimitedInventors: Sanjeev Kaushal, Kenji Sugishima, Sukesh Janubhai Patel, Robert Filman, Wolfgang Polak, Orion Wolfe, Jessie Burger
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Patent number: 8396582Abstract: System(s) and method(s) for optimizing performance of a manufacturing tool are provided. Optimization relies on recipe drifting and generation of knowledge that capture relationships among product output metrics and input material measurement(s) and recipe parameters. Optimized recipe parameters are extracted from a basis of learned functions that predict output metrics for a current state of the manufacturing tool and measurements of input material(s). Drifting and learning are related and lead to dynamic optimization of tool performance, which enables optimized output from the manufacturing tool as the operation conditions of the tool changes. Features of recipe drifting and associated learning can be autonomously or externally configured through suitable user interfaces, which also can be drifted to optimize end-user interaction.Type: GrantFiled: January 29, 2010Date of Patent: March 12, 2013Assignee: Tokyo Electron LimitedInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima
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Publication number: 20120242667Abstract: The subject disclosure relates to automatically learning relationships among a plurality of manufacturing tool parameters as applied to arbitrary semiconductor manufacturing tools and a graphical user interface that is supported, at least in part, by an autonomous learning system. The graphical user interface can create one or more matrixes based on received data and can further generate additional matrices by transforming the one or more matrixes. A series of windows can be output, wherein the series of windows, provide performance analysis that comprises a matching between a focus chamber and a reference chamber. In an aspect, the focus chamber and the reference chamber can be different chambers. In another aspect, the focus chamber and the reference chamber can be the same chamber, which provides analysis of the deterioration in performance of the same chamber over time.Type: ApplicationFiled: March 21, 2011Publication date: September 27, 2012Applicant: TOKYO ELECTRON LIMITEDInventors: Sanjeev Kaushal, Kenji Sugishima, Sukesh Janubhai Patel, Robert Filman, Wolfgang Polak, Orion Wolfe, Jessie Burger
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Publication number: 20120209798Abstract: An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Knowledge generated and accumulated in the autonomous learning system(s) can be cast into semantic networks that can be employed for learning and driving tool goals based on context.Type: ApplicationFiled: April 27, 2012Publication date: August 16, 2012Applicant: TOKYO ELECTRON LIMITEDInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima
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Publication number: 20120185813Abstract: System(s) and method(s) are provided for adjustment and analysis of performance of a tool through integration of tool operational data and spectroscopic data related to the tool. Such integration results in consolidated data that enable, in part, learning at least one relationship amongst selected portions of the consolidated data. Learning is performed autonomously without human intervention. Adjustment of performance of the tool relies at least in part on a learned relationship and includes generation of process recipe parameter(s) that can adjust a manufacturing process in order to produce a satisfactory tool performance in response to implementation of the manufacturing process. A process recipe parameter can be generated by solving an inverse problem based on the learned relationship.Type: ApplicationFiled: January 19, 2011Publication date: July 19, 2012Applicant: TOKYO ELECTRON LIMITEDInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima
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Patent number: 8190543Abstract: An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. Both the memory platform and the processing platform include functional components and memories that can be defined recursively. Knowledge generated and accumulated in the autonomous learning system(s) can be cast into semantic networks that can be employed for learning and driving tool goals based on context.Type: GrantFiled: March 8, 2008Date of Patent: May 29, 2012Assignee: Tokyo Electron LimitedInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima
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Patent number: 8078552Abstract: An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. The network receives data from the tool system and from an external actor through the interaction manager. Both the memory platform and the processing platform include functional components and memories that can be defined recursively.Type: GrantFiled: March 8, 2008Date of Patent: December 13, 2011Assignee: Tokyo Electron LimitedInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima
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Publication number: 20110131162Abstract: An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. The network receives data from the tool system and from an external actor through the interaction manager. Both the memory platform and the processing platform include functional components and memories that can be defined recursively.Type: ApplicationFiled: March 8, 2008Publication date: June 2, 2011Applicant: TOKYO ELECTRON LIMITEDInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima
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Publication number: 20100138026Abstract: System(s) and method(s) for optimizing performance of a manufacturing tool are provided. Optimization relies on recipe drifting and generation of knowledge that capture relationships among product output metrics and input material measurement(s) and recipe parameters. Optimized recipe parameters are extracted from a basis of learned functions that predict output metrics for a current state of the manufacturing tool and measurements of input material(s). Drifting and learning are related and lead to dynamic optimization of tool performance, which enables optimized output from the manufacturing tool as the operation conditions of the tool changes. Features of recipe drifting and associated learning can be autonomously or externally configured through suitable user interfaces, which also can be drifted to optimize end-user interaction.Type: ApplicationFiled: January 29, 2010Publication date: June 3, 2010Applicant: TOKYO ELECTRON LIMITEDInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima
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Publication number: 20090240366Abstract: Autonomous biologically based learning tool system(s) and method(s) that the tool system(s) employs for learning and analysis of performance degradation and mismatch are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. Objectively generated knowledge gleaned from synthetic or production data can be utilized to determine a mathematical relationship among a specific output variable and a set of associated influencing variables. The generated relationship facilitates assessment of performance degradation of a set of tools, and performance mismatch among tools therein.Type: ApplicationFiled: March 31, 2009Publication date: September 24, 2009Applicant: Tokyo Electron LimitedInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima
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Publication number: 20090228408Abstract: An autonomous biologically based learning tool system and a method that the tool system employs for learning and analysis are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. The autonomous learning system comprises a memory platform and a processing platform that communicate through a network. The network receives data from the tool system and from an external actor through the interaction manager. Both the memory platform and the processing platform include functional components and memories that can be defined recursively.Type: ApplicationFiled: March 8, 2008Publication date: September 10, 2009Applicant: TOKYO ELECTRON LIMITEDInventors: Sanjeev Kaushal, Sukesh Janubhai Patel, Kenji Sugishima