公开(公告)号：US20230316493A1

公开(公告)日：2023-10-05

申请号：US18320866

申请日：2023-05-19

Applicant: Samsung Display Co., Ltd.

Inventor： Yan Kang ,  Janghwan Lee ,  Shuhui Qu ,  Jinghua Yao ,  Sai MarapaReddy

IPC: G06T7/00 ,  G06N3/08 ,  G06N20/20 ,  G06V10/82 ,  G06V10/70 ,  G06F18/241 ,  G06F18/25 ,  G06N3/045 ,  G06V20/69

CPC classification number: G06T7/0004 ,  G06N3/08 ,  G06N20/20 ,  G06V10/82 ,  G06V10/70 ,  G06F18/241 ,  G06F18/251 ,  G06N3/045 ,  G06V20/69 ,  G06T2207/10056 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/10116

Abstract: A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.

公开(公告)号：US11568324B2

公开(公告)日：2023-01-31

申请号：US16365485

申请日：2019-03-26

Applicant: Samsung Display Co., Ltd.

Inventor： Janghwan Lee

IPC: G06N20/20

Abstract: A system includes a memory; and a processor configured to train a first machine learning model based on the first dataset labeling; provide the second dataset to the trained first machine learning model to generate an updated second dataset including an updated second dataset labeling, determine a first difference between the updated second dataset labeling and the second dataset labeling; train a second machine learning model based on the updated second dataset labeling if the first difference is greater than a first threshold value; provide the first dataset to the trained second machine learning model to generate an updated first dataset including an updated first dataset labeling, determine a second difference between the updated first dataset labeling and the first dataset labeling; and train the first machine learning model based on the updated first dataset labeling if the second difference is greater than a second threshold value.

公开(公告)号：US20210096530A1

公开(公告)日：2021-04-01

申请号：US16693101

申请日：2019-11-22

Applicant: Samsung Display Co., Ltd.

Inventor： Sai MarapaReddy ,  Shuhui Qu ,  Janghwan Lee

IPC: G05B19/406 ,  G06N3/08

Abstract: A system and method for classifying products manufactured via a manufacturing process. A processor receives an input dataset, and extracts features of the input dataset at two or more levels of abstraction. The processor combines the extracted features and provides the combined extracted features to a classifier. The classifier is trained based on the combined extracted features for learning a pattern of not-faulty products. The trained classifier is configured to receive data for a product to be classified, to output a prediction for the product based on the received data.

公开(公告)号：US10681344B2

公开(公告)日：2020-06-09

申请号：US15909893

申请日：2018-03-01

Applicant: Samsung Display Co., Ltd.

Inventor： Yiwei Zhang ,  Janghwan Lee

IPC: G09G3/00 ,  H04N17/04 ,  G06T7/00

Abstract: A system and method for white spot Mura defects on a display. The system is configured to pre-process an input images to generate a plurality of image patches. A feature vector is then extracted for each of the plurality of image patches. The feature vector includes at least one image moment feature and at least one texture feature. A machine learning classifier then determines the presence of a defect in each patch using the feature vector.

公开(公告)号：US10534852B2

公开(公告)日：2020-01-14

申请号：US15215512

申请日：2016-07-20

Applicant: SAMSUNG DISPLAY CO. LTD.

Inventor： Janghwan Lee ,  Ning Lu

IPC: G06F17/22 ,  G06F17/21 ,  G06F17/27 ,  H04L29/08

Abstract: A virtual device for processing Web-based content to be displayed on a remote rendering device includes: a processor implemented by one or more cloud resources; and a memory, and the memory stores instructions that, when executed, cause the processor to: receive the content; detect an attribute of the remote rendering device and process the content according to the detected attribute; analyze the content to construct a render tree corresponding to the content; prepare render tree data for rendering by the remote rendering device, the render tree data corresponding to the constructed render tree; and transmit the render tree data over a communication network to the remote rendering device.

公开(公告)号：US20190189083A1

公开(公告)日：2019-06-20

申请号：US15978045

申请日：2018-05-11

Applicant: Samsung Display Co., Ltd.

Inventor： Janghwan Lee

IPC: G09G5/10

CPC classification number: G09G5/10 ,  G09G2320/0242

Abstract: A system and method for identifying white spot Mura defects on a display. The system and method generates a first filtered image by filtering an input image using a first image filter. First potential candidate locations are determined using the first filtered image. A second filtered image is generated by filtering an input image using a second image filter and second potential candidate locations are determined using the second filtered image. A list of candidate locations is produced, where the list of candidate locations is of locations in both the first potential candidate locations and the second potential candidate locations.

公开(公告)号：US20180301071A1

公开(公告)日：2018-10-18

申请号：US15639859

申请日：2017-06-30

Applicant: Samsung Display Co., Ltd.

Inventor： Yiwei Zhang ,  Janghwan Lee

IPC: G09G3/00

CPC classification number: G09G3/006 ,  G09G2330/08 ,  G09G2330/10 ,  G09G2354/00 ,  G09G2360/14 ,  G09G2360/145

Abstract: A method for detecting one or more white spot MURA defects in a display panel includes receiving an image of the display panel, the image including the one or more white spot MURA defects, dividing the image into a plurality of patches, each one of the plurality of patches corresponding to an m pixel by n pixel area of the image (wherein m and n are integers greater than or equal to one), generating a plurality of feature vectors for the plurality of patches, each of the feature vectors corresponding to one of the plurality of patches and including one or more image texture features and one or more image moment features, and classifying each one of the plurality of patches based on a respective one of the plurality of feature vectors by utilizing a multi-class support vector machine to detect the one or more white spot MURA defects.

公开(公告)号：US20240242494A1

公开(公告)日：2024-07-18

申请号：US18623923

申请日：2024-04-01

Applicant: SAMSUNG DISPLAY CO., LTD.

Inventor： Shuhui Qu ,  Janghwan Lee ,  Yan Kang ,  Jinghua Yao ,  Sai MarapaReddy

IPC: G06V10/82 ,  G06F18/21 ,  G06F18/2113 ,  G06F18/22 ,  G06F18/25 ,  G06N3/045 ,  G06N3/08 ,  G06V10/764 ,  G06V10/80

CPC classification number: G06V10/82 ,  G06F18/2113 ,  G06F18/217 ,  G06F18/22 ,  G06F18/251 ,  G06N3/045 ,  G06N3/08 ,  G06V10/764 ,  G06V10/809 ,  G06V10/811

Abstract: A method and a system are presented for controlling a performance of a fusion model. The method includes obtaining a first set and a second set of candidate models for a first and second neural networks, respectively. Each of the first and second set of candidate models is pre-trained with a first source and a second source, respectively. For each possible pairing of one candidate model from the first neural network and one candidate model from the second neural network, a model distance Dm is determined. A subset of possible pairings of one first candidate model and one second candidate model is selected based on the model distance Dm between them. Using the subset of possible parings, the first neural network and the second neural network are combined to generate two branches for a fusion model neural network.

公开(公告)号：US11948347B2

公开(公告)日：2024-04-02

申请号：US16938857

申请日：2020-07-24

Applicant: Samsung Display Co., Ltd.

Inventor： Shuhui Qu ,  Janghwan Lee ,  Yan Kang ,  Jinghua Yao ,  Sai MarapaReddy

IPC: G06N3/08 ,  G06F18/21 ,  G06F18/2113 ,  G06F18/22 ,  G06F18/25 ,  G06N3/045 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82

CPC classification number: G06V10/82 ,  G06F18/2113 ,  G06F18/217 ,  G06F18/22 ,  G06F18/251 ,  G06N3/045 ,  G06N3/08 ,  G06V10/764 ,  G06V10/809 ,  G06V10/811

Abstract: A method and a system are presented for controlling a performance of a fusion model. The method includes obtaining a first set and a second set of candidate models for a first and second neural networks, respectively. Each of the first and second set of candidate models is pre-trained with a first source and a second source, respectively. For each possible pairing of one candidate model from the first neural network and one candidate model from the second neural network, a model distance Dm is determined. A subset of possible pairings of one first candidate model and one second candidate model is selected based on the model distance Dm between them. Using the subset of possible parings, the first neural network and the second neural network are combined to generate two branches for a fusion model neural network.

公开(公告)号：US20230127852A1

公开(公告)日：2023-04-27

申请号：US18089475

申请日：2022-12-27

Applicant: SAMSUNG DISPLAY CO., LTD.

Inventor： Janghwan Lee

IPC: G06V10/778 ,  G06N20/20 ,  G06V10/82

Abstract: A system includes a memory; and a processor configured to train a first machine learning model based on the first dataset labeling; provide the second dataset to the trained first machine learning model to generate an updated second dataset including an updated second dataset labeling, determine a first difference between the updated second dataset labeling and the second dataset labeling; train a second machine learning model based on the updated second dataset labeling if the first difference is greater than a first threshold value; provide the first dataset to the trained second machine learning model to generate an updated first dataset including an updated first dataset labeling, determine a second difference between the updated first dataset labeling and the first dataset labeling; and train the first machine learning model based on the updated first dataset labeling if the second difference is greater than a second threshold value.