Patents by Inventor Prasanti Uppaluri
Prasanti Uppaluri 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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Patent number: 11676264Abstract: A system for characterizing a specimen is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images a specimen, and a controller communicatively coupled to the characterization sub-system. The controller may be configured to: receive from the characterization sub-system one or more training images of one or more defects of a training specimen; generate one or more augmented images of the one or more defects of the training specimen; generate a machine learning classifier based on the one or more augmented images of the one or more defects of the training specimen; receive from the characterization sub-system one or more target images of one or more target features of a target specimen; and determine one or more defects of the one or more target features with the machine learning classifier.Type: GrantFiled: July 21, 2020Date of Patent: June 13, 2023Assignee: KLA CorporationInventors: Martin Plihal, Saravanan Paramasivam, Jacob George, Niveditha Lakshmi Narasimhan, Sairam Ravu, Somesh Challapalli, Prasanti Uppaluri
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Publication number: 20220383456Abstract: Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem. The one or more components include a deep learning model configured for denoising an image of a specimen generated by an imaging subsystem. The computer subsystem is configured for determining information for the specimen from the denoised image.Type: ApplicationFiled: April 14, 2022Publication date: December 1, 2022Inventors: Aditya Gulati, Raghavan Konuru, Niveditha Lakshmi Narasimhan, Saravanan Paramasivam, Martin Plihal, Prasanti Uppaluri
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Patent number: 11410291Abstract: A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.Type: GrantFiled: July 6, 2020Date of Patent: August 9, 2022Assignee: KLA CorporationInventors: Prasanti Uppaluri, Rajesh Manepalli, Ashok V. Kulkarni, Saibal Banerjee, John Kirkland
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Patent number: 11379967Abstract: Methods and systems for improved detection and classification of defects of interest (DOI) is realized based on values of one or more automatically generated attributes derived from images of a candidate defect. Automatically generated attributes are determined by iteratively training, reducing, and retraining a deep learning model. The deep learning model relates optical images of candidate defects to a known classification of those defects. After model reduction, attributes of the reduced model are identified which strongly relate the optical images of candidate defects to the known classification of the defects. The reduced model is subsequently employed to generate values of the identified attributes associated with images of candidate defects having unknown classification. In another aspect, a statistical classifier is employed to classify defects based on automatically generated attributes and attributes identified manually.Type: GrantFiled: January 16, 2020Date of Patent: July 5, 2022Assignee: KLA CorporationInventors: Jacob George, Saravanan Paramasivam, Martin Plihal, Niveditha Lakshmi Narasimhan, Sairam Ravu, Prasanti Uppaluri
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Patent number: 11379969Abstract: Machine learning approaches provide additional information about semiconductor wafer inspection stability issues that makes it possible to distinguish consequential process variations like process excursions from minor process variations that are within specification. The effect of variable defect of interest (DOI) capture rates in the inspection result and the effect of variable defect count on the wafer can be monitored independently.Type: GrantFiled: July 27, 2020Date of Patent: July 5, 2022Assignee: KLA CORPORATIONInventors: Martin Plihal, Prasanti Uppaluri, Saravanan Paramasivam
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Patent number: 11237119Abstract: Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.Type: GrantFiled: December 7, 2017Date of Patent: February 1, 2022Assignee: KLA-Tencor CorporationInventors: Martin Plihal, Erfan Soltanmohammadi, Saravanan Paramasivam, Sairam Ravu, Ankit Jain, Prasanti Uppaluri, Vijay Ramachandran
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Patent number: 11114324Abstract: Systems and methods for detecting defect candidates on a specimen are provided. One method includes, after scanning of at least a majority of a specimen is completed, applying one or more segmentation methods to at least a substantial portion of output generated during the scanning thereby generating two or more segments of the output. The method also includes separately detecting outliers in the two or more segments of the output. In addition, the method includes detecting defect candidates on the specimen by applying one or more predetermined criteria to results of the separately detecting to thereby designate a portion of the detected outliers as the defect candidates.Type: GrantFiled: October 15, 2019Date of Patent: September 7, 2021Assignee: KLA Corp.Inventors: Martin Plihal, Erfan Soltanmohammadi, Prasanti Uppaluri, Mohit Jani, Chris Maher
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Publication number: 20210035282Abstract: Machine learning approaches provide additional information about semiconductor wafer inspection stability issues that makes it possible to distinguish consequential process variations like process excursions from minor process variations that are within specification. The effect of variable defect of interest (DOI) capture rates in the inspection result and the effect of variable defect count on the wafer can be monitored independently.Type: ApplicationFiled: July 27, 2020Publication date: February 4, 2021Inventors: Martin Plihal, Prasanti Uppaluri, Saravanan Paramasivam
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Publication number: 20210027445Abstract: A system for characterizing a specimen is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images a specimen, and a controller communicatively coupled to the characterization sub-system. The controller may be configured to: receive from the characterization sub-system one or more training images of one or more defects of a training specimen; generate one or more augmented images of the one or more defects of the training specimen; generate a machine learning classifier based on the one or more augmented images of the one or more defects of the training specimen; receive from the characterization sub-system one or more target images of one or more target features of a target specimen; and determine one or more defects of the one or more target features with the machine learning classifier.Type: ApplicationFiled: July 21, 2020Publication date: January 28, 2021Inventors: Martin Plihal, Saravanan Paramasivam, Jacob George, Niveditha Lakshmi Narasimhan, Sairam Ravu, Somesh Challapalli, Prasanti Uppaluri
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Publication number: 20200334807Abstract: A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.Type: ApplicationFiled: July 6, 2020Publication date: October 22, 2020Inventors: Prasanti Uppaluri, Rajesh Manepalli, Ashok V. Kulkarni, Saibal Banerjee, John Kirkland
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Publication number: 20200328104Abstract: Systems and methods for detecting defect candidates on a specimen are provided. One method includes, after scanning of at least a majority of a specimen is completed, applying one or more segmentation methods to at least a substantial portion of output generated during the scanning thereby generating two or more segments of the output. The method also includes separately detecting outliers in the two or more segments of the output. In addition, the method includes detecting defect candidates on the specimen by applying one or more predetermined criteria to results of the separately detecting to thereby designate a portion of the detected outliers as the defect candidates.Type: ApplicationFiled: October 15, 2019Publication date: October 15, 2020Inventors: Martin Plihal, Erfan Soltanmohammadi, Prasanti Uppaluri, Mohit Jani, Chris Maher
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Publication number: 20200234428Abstract: Methods and systems for improved detection and classification of defects of interest (DOI) is realized based on values of one or more automatically generated attributes derived from images of a candidate defect. Automatically generated attributes are determined by iteratively training, reducing, and retraining a deep learning model. The deep learning model relates optical images of candidate defects to a known classification of those defects. After model reduction, attributes of the reduced model are identified which strongly relate the optical images of candidate defects to the known classification of the defects. The reduced model is subsequently employed to generate values of the identified attributes associated with images of candidate defects having unknown classification. In another aspect, a statistical classifier is employed to classify defects based on automatically generated attributes and attributes identified manually.Type: ApplicationFiled: January 16, 2020Publication date: July 23, 2020Inventors: Jacob George, Saravanan Paramasivam, Martin Plihal, Niveditha Lakshmi Narasimhan, Sairam Ravu, Prasanti Uppaluri
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Patent number: 10706522Abstract: A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.Type: GrantFiled: December 29, 2016Date of Patent: July 7, 2020Assignee: KLA-Tencor CorporationInventors: Prasanti Uppaluri, Rajesh Manepalli, Ashok V. Kulkarni, Saibal Banerjee, John Kirkland
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Patent number: 10670536Abstract: Methods and systems for selecting a mode for inspection of a specimen are provided. One method includes determining how separable defects of interest (DOIs) and nuisances detected on a specimen are in one or more modes of an inspection subsystem. The separability of the modes for the Dais and nuisances is used to select a subset of the modes for inspection of other specimens of the same type. Other characteristics of the performance of the modes may be used in combination with the separability to select the modes. The subset of modes selected based on the separability may also be an initial subset of modes for which additional analysis is performed to determine the final subset of the modes.Type: GrantFiled: March 25, 2019Date of Patent: June 2, 2020Assignee: KLA-Tencor Corp.Inventors: Martin Plihal, Saravanan Paramasivam, Ankit Jain, Prasanti Uppaluri, Raghavan Konuru
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Patent number: 10620134Abstract: Methods and systems for creating a sample of defects for a specimen are provided. One method includes detecting defects on a specimen based on output generated by a detector of an output acquisition subsystem. For the defects detected in an array region on the specimen, where the array region includes multiple array cell types, the method includes stacking information for the defects based on the multiple array cell types. The stacking includes overlaying design information for only a first of the multiple array cell types with the information for only the defects detected in the first of the multiple array cell types. In addition, the method includes selecting a portion of the detected defects based on results of the stacking thereby creating a sample of the detected defects.Type: GrantFiled: July 26, 2018Date of Patent: April 14, 2020Assignee: KLA-Tencor Corp.Inventors: Vidyasagar Anantha, Manikandan Mariyappan, Raghav Babulnath, Gangadharan Sivaraman, Satya Kurada, Thirupurasundari Jayaraman, Prasanti Uppaluri, Srikanth Kandukuri
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Publication number: 20190346375Abstract: Methods and systems for creating a sample of defects for a specimen are provided. One method includes detecting defects on a specimen based on output generated by a detector of an output acquisition subsystem. For the defects detected in an array region on the specimen, where the array region includes multiple array cell types, the method includes stacking information for the defects based on the multiple array cell types. The stacking includes overlaying design information for only a first of the multiple array cell types with the information for only the defects detected in the first of the multiple array cell types. In addition, the method includes selecting a portion of the detected defects based on results of the stacking thereby creating a sample of the detected defects.Type: ApplicationFiled: July 26, 2018Publication date: November 14, 2019Inventors: Vidyasagar Anantha, Manikandan Mariyappan, Raghav Babulnath, Gangadharan Sivaraman, Satya Kurada, Thirupurasundari Jayaraman, Prasanti Uppaluri, Srikanth Kandukuri
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Publication number: 20190302031Abstract: Methods and systems for selecting a mode for inspection of a specimen are provided. One method includes determining how separable defects of interest (DOIs) and nuisances detected on a specimen are in one or more modes of an inspection subsystem. The separability of the modes for the Dais and nuisances is used to select a subset of the modes for inspection of other specimens of the same type. Other characteristics of the performance of the modes may be used in combination with the separability to select the modes. The subset of modes selected based on the separability may also be an initial subset of modes for which additional analysis is performed to determine the final subset of the modes.Type: ApplicationFiled: March 25, 2019Publication date: October 3, 2019Inventors: Martin Plihal, Saravanan Paramasivam, Ankit Jain, Prasanti Uppaluri, Raghavan Konuru
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Patent number: 10267748Abstract: Methods and systems for training an inspection-related algorithm are provided. One system includes one or more computer subsystems configured for performing an initial training of an inspection-related algorithm with a labeled set of defects thereby generating an initial version of the inspection-related algorithm and applying the initial version of the inspection-related algorithm to an unlabeled set of defects. The computer subsystem(s) are also configured for altering the labeled set of defects based on results of the applying. The computer subsystem(s) may then iteratively re-train the inspection-related algorithm and alter the labeled set of defects until one or more differences between results produced by a most recent version and a previous version of the algorithm meet one or more criteria. When the one or more differences meet the one or more criteria, the most recent version of the inspection-related algorithm is outputted as the trained algorithm.Type: GrantFiled: October 12, 2017Date of Patent: April 23, 2019Assignee: KLA-Tencor Corp.Inventors: Martin Plihal, Erfan Soltanmohammadi, Saravanan Paramasivam, Sairam Ravu, Ankit Jain, Sarath Shekkizhar, Prasanti Uppaluri
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Patent number: 10209628Abstract: A method for automatically classifying one or more defects based on electrical design properties includes receiving one or more images of a selected region of a sample, receiving one or more sets of design data associated with the selected region of the sample, locating one or more defects in the one or more images of the selected region of the sample by comparing the one or more images of the selected region of the sample to the one or more sets of design data, retrieving one or more patterns of interest from the one or more sets of design data corresponding to the one or more defects, and classifying the one or more defects in the one or more images of the selected region of the sample based on one or more annotated electrical design properties included in the one or more patterns of interest.Type: GrantFiled: October 4, 2016Date of Patent: February 19, 2019Assignee: KLA-Tencor CorporationInventors: Prasanti Uppaluri, Thirupurasundari Jayaraman, Ardis Liang, Srikanth Kandukuri, Sagar Kekare
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Publication number: 20180197714Abstract: Wafer inspection with stable nuisance rates and defect of interest capture rates are disclosed. This technique can be used for discovery of newly appearing defects that occur during the manufacturing process. Based on a first wafer, defects of interest are identified based on the classified filtered inspection results. For each remaining wafer, the defect classifier is updated and defects of interest in the next wafer are identified based on the classified filtered inspection results.Type: ApplicationFiled: December 7, 2017Publication date: July 12, 2018Inventors: Martin Plihal, Erfan Soltanmohammadi, Saravanan Paramasivam, Sairam Ravu, Ankit Jain, Prasanti Uppaluri, Vijay Ramachandran