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：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：US20210201460A1

公开(公告)日：2021-07-01

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

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 he formed from a CNN autoencoder. The classification CNN and the CNN autoencoder may each be a deep neural network.