公开(公告)号：US20240289597A1

公开(公告)日：2024-08-29

申请号：US18656130

申请日：2024-05-06

Applicant: Micron Technology, Inc.

Inventor： Jing Gong ,  Stewart R. Watson ,  Dmitry Vengertsev ,  Ameya Parab

IPC: G06N3/049 ,  G06N3/065 ,  G06N3/084 ,  G06N5/046 ,  G11C13/00 ,  H03M1/12

CPC classification number: G06N3/049 ,  G06N3/065 ,  G06N3/084 ,  G06N5/046 ,  G11C13/0002 ,  H03M1/12

Abstract: 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.

公开(公告)号：US11585654B2

公开(公告)日：2023-02-21

申请号：US16890364

申请日：2020-06-02

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Zahra Hosseinimakarem ,  Jonathan D. Harms ,  Alyssa N. Scarbrough ,  Dmitry Vengertsev ,  Yi Hu

IPC: G01B11/25 ,  G01B11/30

Abstract: 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.

公开(公告)号：US20240185406A1

公开(公告)日：2024-06-06

申请号：US18438256

申请日：2024-02-09

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Yutao Gong ,  Dmitry Vengertsev ,  Seth A. Eichmeyer ,  Jing Gong

IPC: G06T7/00 ,  G01N21/88 ,  G01N21/95 ,  G06V10/762 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  H01L21/67

CPC classification number: G06T7/0004 ,  G01N21/9501 ,  G06T7/001 ,  G06V10/762 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G01N2021/8887 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30148 ,  H01L21/67288

Abstract: 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.

公开(公告)号：US20210201195A1

公开(公告)日：2021-07-01

申请号：US16854107

申请日：2020-04-21

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Dmitry Vengertsev ,  Zahra Hosseinimakarem ,  Jonathan D. Harms

IPC: G06N20/00 ,  G06K9/62 ,  G06N3/04

Abstract: 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.

公开(公告)号：US11983619B2

公开(公告)日：2024-05-14

申请号：US16994302

申请日：2020-08-14

Applicant: Micron Technology, Inc.

Inventor： Jing Gong ,  Stewart R. Watson ,  Dmitry Vengertsev ,  Ameya Parab

IPC: G06N3/049 ,  G06N3/065 ,  G06N3/084 ,  G06N5/046 ,  G11C13/00 ,  H03M1/12

CPC classification number: G06N3/049 ,  G06N3/065 ,  G06N3/084 ,  G06N5/046 ,  G11C13/0002 ,  H03M1/12

Abstract: 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.

公开(公告)号：US11681906B2

公开(公告)日：2023-06-20

申请号：US17006602

申请日：2020-08-28

Applicant: Micron Technology, Inc.

Inventor： Dmitry Vengertsev ,  Stewart R. Watson ,  Jing Gong ,  Ameya Parab

IPC: G11C11/54 ,  G06N3/063 ,  G06N3/084 ,  H03M1/36 ,  G11C13/00 ,  G06N5/046 ,  G06N7/01

CPC classification number: G06N3/063 ,  G06N3/084 ,  G06N5/046 ,  G06N7/01 ,  G11C11/54 ,  G11C13/004 ,  G11C13/0009 ,  G11C13/0069 ,  H03M1/36

Abstract: 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.

公开(公告)号：US20220138612A1

公开(公告)日：2022-05-05

申请号：US17083768

申请日：2020-10-29

Applicant: Micron Technology, Inc.

Inventor： Dmitry Vengertsev ,  Zahra Hosseinimakarem ,  Marta Egorova

IPC: G06N20/00 ,  G06F16/28 ,  G05B19/4155

Abstract: 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.

公开(公告)号：US20220067544A1

公开(公告)日：2022-03-03

申请号：US17005036

申请日：2020-08-27

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Yi Hu ,  Dmitry Vengertsev ,  Zahra Hosseinimakarem ,  Jonathan D. Harms

IPC: G06N5/04 ,  G06T7/90 ,  G06T7/00 ,  G06T7/521 ,  G06N20/00 ,  G06T11/00

Abstract: 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.

公开(公告)号：US11922613B2

公开(公告)日：2024-03-05

申请号：US16925243

申请日：2020-07-09

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Yutao Gong ,  Dmitry Vengertsev ,  Seth A. Eichmeyer ,  Jing Gong

IPC: G06T7/00 ,  G01N21/95 ,  G06V10/762 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G01N21/88 ,  H01L21/67

CPC classification number: G06T7/0004 ,  G01N21/9501 ,  G06T7/001 ,  G06V10/762 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G01N2021/8887 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30148 ,  H01L21/67288

Abstract: 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.

公开(公告)号：US20220366224A1

公开(公告)日：2022-11-17

申请号：US17319765

申请日：2021-05-13

Applicant: Micron Technology, Inc.

Inventor： Dmitry Vengertsev ,  Seth A. Eichmeyer ,  Jing Gong ,  John Christopher M. Sancon ,  Nicola Ciocchini ,  Tom Tangelder

IPC: G06N3/063 ,  G06N3/04 ,  G11C11/54 ,  G11C13/00

Abstract: 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.