Patents by Inventor Dmitry A. Vengertsev
Dmitry A. Vengertsev 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: 11995567Abstract: An image or a spectrum of a surface may be acquired by a computing device, which may be included in a mobile device in some examples. The computing device may extract a measured spectrum from the image and generate a corrected spectrum of the surface. In some examples, the corrected spectrum may be generated to compensate for ambient light influence. The corrected spectrum may be analyzed to provide a result, such as a diagnosis or a product recommendation. In some examples, the result is based, at least in part, on a comparison of the corrected spectrum to reference spectra. In some examples, the result is based, at least in part, on an inference of a machine learning model.Type: GrantFiled: August 27, 2020Date of Patent: May 28, 2024Assignee: Micron Technology, Inc.Inventors: Yi Hu, Dmitry Vengertsev, Zahra Hosseinimakarem, Jonathan D. Harms
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Patent number: 11983619Abstract: Apparatuses and methods can be related to implementing a transformer neural network in a memory. A transformer neural network can be implemented utilizing a resistive memory array. The memory array can comprise programmable memory cells that can be programed and used to store weights of the transformer neural network and perform operations consistent with the transformer neural network.Type: GrantFiled: August 14, 2020Date of Patent: May 14, 2024Assignee: Micron Technology, Inc.Inventors: Jing Gong, Stewart R. Watson, Dmitry Vengertsev, Ameya Parab
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Patent number: 11922613Abstract: An inspection system for determining wafer defects in semiconductor fabrication may include an image capturing device to capture a wafer image and a classification convolutional neural network (CNN) to determine a classification from a plurality of classes for the captured image. Each of the plurality of classes indicates a type of a defect in the wafer. The system may also include an encoder to encode to convert a training image into a feature vector; a cluster system to cluster the feature vector to generate soft labels for the training image; and a decoder to decode the feature vector into a re-generated image. The system may also include a classification system to determine a classification from the plurality of classes for the training image. The encoder and decoder may be formed from a CNN autoencoder. The classification CNN and the CNN autoencoder may each be a deep neural network.Type: GrantFiled: July 9, 2020Date of Patent: March 5, 2024Assignee: Micron Technology, Inc.Inventors: Yutao Gong, Dmitry Vengertsev, Seth A. Eichmeyer, Jing Gong
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Patent number: 11861493Abstract: Data may be abstracted and/or masked prior to being provided to a machine learning model for training. A machine learning model may provide a confidence level associated with a result. If the confidence level is too high, the machine learning model or an application including the machine learning model may refrain from providing the result as an output. In some examples, the machine learning model may provide a “second best” result that has an acceptable confidence level. In other examples, an error signal may be provided as the output. In accordance with examples of the present disclosure, data may be abstracted and/or masked prior to being provided to a machine learning model for training and confidence levels of results of the trained machine learning model may be used to determine when a result should be withheld.Type: GrantFiled: April 21, 2020Date of Patent: January 2, 2024Assignee: Micron Technology, Inc.Inventors: Dmitry Vengertsev, Zahra Hosseinimakarem, Jonathan D. Harms
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Publication number: 20230306291Abstract: A system for generating simulated data is disclosed. The system may determine items of content utilized by a network. The system may also retrieve one or more data patterns associated with one or more features associated with the content. The system may also determine a plurality of indices associated with the data patterns. The system may also generate, based on the data patterns and the plurality of indices, simulated data associated with the content.Type: ApplicationFiled: March 22, 2022Publication date: September 28, 2023Inventors: Martin SCHATZ, Dmitry Vengertsev, Yifan Liu, Ilana Marisa Arbisser, Yuchen Hao, Muhammet Mustafa Ozdal
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Patent number: 11681906Abstract: Apparatuses and methods can be related to implementing a Bayesian neural network in a memory. A Bayesian neural network can be implemented utilizing a resistive memory array. The memory array can comprise programmable memory cells that can be programed and used to store weights of the Bayesian neural network and perform operations consistent with the Bayesian neural network.Type: GrantFiled: August 28, 2020Date of Patent: June 20, 2023Assignee: Micron Technology, Inc.Inventors: Dmitry Vengertsev, Stewart R. Watson, Jing Gong, Ameya Parab
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Patent number: 11585654Abstract: Embodiments of the disclosure are drawn to projecting light on a surface and analyzing the scattered light to obtain spatial information of the surface and generate a three dimensional model of the surface. The three dimensional model may then be analyzed to calculate one or more surface characteristics, such as roughness. The surface characteristics may then be analyzed to provide a result, such as a diagnosis or a product recommendation. In some examples, a mobile device is used to analyze the surface.Type: GrantFiled: June 2, 2020Date of Patent: February 21, 2023Assignee: MICRON TECHNOLOGY, INC.Inventors: Zahra Hosseinimakarem, Jonathan D. Harms, Alyssa N. Scarbrough, Dmitry Vengertsev, Yi Hu
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Publication number: 20220366224Abstract: Apparatuses and methods can be related to implementing a binary neural network in memory. A binary neural network can be implemented utilizing a resistive memory array. The memory array can comprise programmable memory cells that can be programed and used to store weights of the binary neural network and perform operations consistent with the binary neural network. The weights of the binary neural network can correspond to non-zero values.Type: ApplicationFiled: May 13, 2021Publication date: November 17, 2022Inventors: Dmitry Vengertsev, Seth A. Eichmeyer, Jing Gong, John Christopher M. Sancon, Nicola Ciocchini, Tom Tangelder
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Publication number: 20220138612Abstract: Methods, apparatuses, and systems associated with anomaly detection and resolution are described. Examples can include detecting, via a sensor of a robot, an object in a path of the robot while the robot is performing a task in an environment and classifying the object as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a machine learning model. Examples can include proceeding with the task responsive to classification of the object as a non-anomaly and the environment as non-anomalous and resolving the anomaly or the anomalous environment and proceeding with the task responsive to classification of the object as an anomaly or the environment as anomalous.Type: ApplicationFiled: October 29, 2020Publication date: May 5, 2022Inventors: Dmitry Vengertsev, Zahra Hosseinimakarem, Marta Egorova
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Publication number: 20220067544Abstract: An image or a spectrum of a surface may be acquired by a computing device, which may be included in a mobile device in some examples. The computing device may extract a measured spectrum from the image and generate a corrected spectrum of the surface. In some examples, the corrected spectrum may be generated to compensate for ambient light influence. The corrected spectrum may be analyzed to provide a result, such as a diagnosis or a product recommendation. In some examples, the result is based, at least in part, on a comparison of the corrected spectrum to reference spectra. In some examples, the result is based, at least in part, on an inference of a machine learning model.Type: ApplicationFiled: August 27, 2020Publication date: March 3, 2022Applicant: MICRON TECHNOLOGY, INC.Inventors: Yi Hu, Dmitry Vengertsev, Zahra Hosseinimakarem, Jonathan D. Harms
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Publication number: 20220067491Abstract: Apparatuses and methods can be related to implementing a Bayesian neural network in a memory. A Bayesian neural network can be implemented utilizing a resistive memory array. The memory array can comprise programmable memory cells that can be programed and used to store weights of the Bayesian neural network and perform operations consistent with the Bayesian neural network.Type: ApplicationFiled: August 28, 2020Publication date: March 3, 2022Inventors: Dmitry Vengertsev, Stewart R. Watson, Jing Gong, Ameya Parab
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Publication number: 20220051078Abstract: Apparatuses and methods can be related to implementing a transformer neural network in a memory. A transformer neural network can be implemented utilizing a resistive memory array. The memory array can comprise programmable memory cells that can be programed and used to store weights of the transformer neural network and perform operations consistent with the transformer neural network.Type: ApplicationFiled: August 14, 2020Publication date: February 17, 2022Inventors: Jing Gong, Stewart R. Watson, Dmitry Vengertsev, Ameya Parab
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Publication number: 20210372785Abstract: Embodiments of the disclosure are drawn to projecting light on a surface and analyzing the scattered light to obtain spatial information of the surface and generate a three dimensional model of the surface. The three dimensional model may then be analyzed to calculate one or more surface characteristics, such as roughness. The surface characteristics may then be analyzed to provide a result, such as a diagnosis or a product recommendation. In some examples, a mobile device is used to analyze the surface.Type: ApplicationFiled: June 2, 2020Publication date: December 2, 2021Applicant: MICRON TECHNOLOGY, INC.Inventors: Zahra Hosseinimakarem, Jonathan D. Harms, Alyssa N. Scarbrough, Dmitry Vengertsev, Yi Hu
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Publication number: 20210201195Abstract: Data may be abstracted and/or masked prior to being provided to a machine learning model for training. A machine learning model may provide a confidence level associated with a result. If the confidence level is too high, the machine learning model or an application including the machine learning model may refrain from providing the result as an output. In some examples, the machine learning model may provide a “second best” result that has an acceptable confidence level. In other examples, an error signal may be provided as the output. In accordance with examples of the present disclosure, data may be abstracted and/or masked prior to being provided to a machine learning model for training and confidence levels of results of the trained machine learning model may be used to determine when a result should be withheld.Type: ApplicationFiled: April 21, 2020Publication date: July 1, 2021Applicant: MICRON TECHNOLOGY, INC.Inventors: Dmitry Vengertsev, Zahra Hosseinimakarem, Jonathan D. Harms
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Publication number: 20210201460Abstract: An inspection system for determining wafer defects in semiconductor fabrication may include an image capturing device to capture a wafer image and a classification convolutional neural network (CNN) to determine a classification from a plurality of classes for the captured image. Each of the plurality of classes indicates a type of a defect in the wafer. The system may also include an encoder to encode to convert a training image into a feature vector; a cluster system to cluster the feature vector to generate soft labels for the training image; and a decoder to decode the feature vector into a re-generated image. The system may also include a classification system to determine a classification from the plurality of classes for the training image. The encoder and decoder may he formed from a CNN autoencoder. The classification CNN and the CNN autoencoder may each be a deep neural network.Type: ApplicationFiled: July 9, 2020Publication date: July 1, 2021Applicant: MICRON TECHNOLOGY, INC.Inventors: Yutao Gong, Dmitry Vengertsev, Seth A. Eichmeyer, Jing Gong
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Patent number: 10146036Abstract: In the methods and systems, optical images of inspection care areas on a semiconductor wafer are acquired and analyzed to detect defects. However, during this analysis, the same threshold setting is not used for all inspection care areas. Instead, care areas are grouped into different care area groups, based on different design layouts and properties. Each group is associated with a corresponding threshold setting that is optimal for detecting defects in the inspection care areas belonging to that group. The assignment of the care areas to the different groups and the association of the different threshold settings with the different groups are noted in an index. This index is accessible during the analysis and used to ensure that each of the inspection care areas in a specific care area group is analyzed based on a corresponding threshold setting that is optimal for that specific care area group.Type: GrantFiled: June 7, 2016Date of Patent: December 4, 2018Assignee: GLOBALFOUNDRIES INC.Inventors: Parul Dhagat, Ananthan Raghunathan, Vikas Sachan, Dmitry A. Vengertsev
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Patent number: 10042973Abstract: Systems, methods, and computer program products for design rules checking in which the waiver of design rules is optimized while ensuring compliant designs that are manufacturable. A first design rule and a plurality of patterns of a layout that violate the first design rule are received by a design rule waiver system. The design rule waiver system may process the first design rule to extract a plurality of descriptors that can be perturbed. The design rule waiver system may perturb an attribute associated with at least one of the plurality of descriptors extracted from the first design rule in order to define a second design rule that is satisfied by the plurality of patterns.Type: GrantFiled: September 30, 2016Date of Patent: August 7, 2018Assignee: GLOBALFOUNDRIES Inc.Inventors: Ioana C. Graur, Dmitry Vengertsev
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Publication number: 20180096093Abstract: Systems, methods, and computer program products for design rules checking in which the waiver of design rules is optimized while ensuring compliant designs that are manufacturable. A first design rule and a plurality of patterns of a layout that violate the first design rule are received by a design rule waiver system. The design rule waiver system may process the first design rule to extract a plurality of descriptors that can be perturbed. The design rule waiver system may perturb an attribute associated with at least one of the plurality of descriptors extracted from the first design rule in order to define a second design rule that is satisfied by the plurality of patterns.Type: ApplicationFiled: September 30, 2016Publication date: April 5, 2018Inventors: Ioana C. Graur, Dmitry Vengertsev
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Publication number: 20170352145Abstract: In the methods and systems, optical images of inspection care areas on a semiconductor wafer are acquired and analyzed to detect defects. However, during this analysis, the same threshold setting is not used for all inspection care areas. Instead, care areas are grouped into different care area groups, based on different design layouts and properties. Each group is associated with a corresponding threshold setting that is optimal for detecting defects in the inspection care areas belonging to that group. The assignment of the care areas to the different groups and the association of the different threshold settings with the different groups are noted in an index. This index is accessible during the analysis and used to ensure that each of the inspection care areas in a specific care area group is analyzed based on a corresponding threshold setting that is optimal for that specific care area group.Type: ApplicationFiled: June 7, 2016Publication date: December 7, 2017Applicant: GLOBALFOUNDRIES INC.Inventors: Parul Dhagat, Ananthan Raghunathan, Vikas Sachan, Dmitry A. Vengertsev
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Patent number: 9690898Abstract: Candidate layout patterns can be generated using a generative model trained based on known data, such as historical hot spot data, features extraction, and geometrical primitives. The generative model can be sampled to obtain candidate layouts that can be ranked and repaired using error optimization, design rule checking, optical proximity checking, and other methods to ensure that resulting candidates are manufacturable.Type: GrantFiled: June 25, 2015Date of Patent: June 27, 2017Assignee: GLOBALFOUNDRIES INC.Inventors: Ioana C. Graur, Ian P. Stobert, Dmitry A. Vengertsev