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公开(公告)号：US20210097400A1
公开(公告)日：2021-04-01
申请号：US16682815
申请日：2019-11-13
Applicant: Samsung Display Co., Ltd.
Inventor： Janghwan Lee
Abstract: A system and method for classifying products. A processor generates first and second instances of a first classifier, and trains the instances based on an input dataset. A second classifier is trained based on the input, where the second classifier is configured to learn a representation of a latent space associated with the input. A first supplemental dataset is generated in the latent space, where the first supplemental dataset is an unlabeled dataset. A first prediction is generated for labeling the first supplemental dataset based on the first instance of the first classifier, and a second prediction is generated for labeling the first supplemental dataset based on the second instance of the first classifier. Labeling annotations are generated for the first supplemental dataset based on the first prediction and the second prediction. A third classifier is trained based on at least the input dataset and the annotated first supplemental dataset.
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公开(公告)号：US20200320439A1
公开(公告)日：2020-10-08
申请号：US16442298
申请日：2019-06-14
Applicant: Samsung Display Co., Ltd.
Inventor： Janghwan Lee
Abstract: A system and method for classification. In some embodiments, the method includes forming a first training dataset and a second training dataset from a labeled input dataset; training a first classifier with the first training dataset; training a variational auto encoder with the second training dataset, the variational auto encoder comprising an encoder and a decoder; generating a third dataset, by feeding pseudorandom vectors into the decoder; labeling the third dataset, using the first classifier, to form a third training dataset; forming a fourth training dataset based on the third dataset; and training a second classifier with the fourth training dataset.
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公开(公告)号：US10453366B2
公开(公告)日：2019-10-22
申请号：US15639859
申请日：2017-06-30
Applicant: Samsung Display Co., Ltd.
Inventor： Yiwei Zhang , Janghwan Lee
IPC: G09G3/00
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.
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公开(公告)号：US20250166266A1
公开(公告)日：2025-05-22
申请号：US18949390
申请日：2024-11-15
Applicant: Samsung Display Co., Ltd.
Inventor： Zhihong Pan , Rahul Shenoy , Kaushik Balakrishnan , Qisen Cheng , Janghwan Lee
Abstract: A system and a method are disclosed for defect image generation using diffusion model sampling. The method includes generating, by a processor via a diffusion model, a noisy image from a defect-free image, generating, by the processor via the diffusion model, a sampled defect image and a sampled defect-free image from the noisy image, generating, by the processor, a mask based on the sampled defect image and the sampled defect-free image, generating, by the processor, a synthetic defect image by generating an additional sampled defect image based on the noisy image and the mask, and transmitting, by the processor, the synthetic defect image.
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公开(公告)号：US12301833B2
公开(公告)日：2025-05-13
申请号：US18074195
申请日：2022-12-02
Applicant: Samsung Display Co., Ltd.
Inventor： Shuhui Qu , Qisen Cheng , Yannick Bliesener , Janghwan Lee
IPC: H04N19/157 , G06V10/44 , G06V10/74 , G06V20/40 , H04N19/94
Abstract: According to some embodiments, a system includes: a memory, an encoder; a decoder, wherein the system is operable to: receive, at the encoder, an input video; divide, by the encoder, the input video into a plurality of video patches; select, by the encoder, codes corresponding to the plurality of video patches of the input video, from a codebook comprising the codes; determine, by the encoder, an assigned code matrix comprising the codes corresponding to the plurality of video patches of the input video; receive, by the decoder, the assigned code matrix from the encoder; and generate, by the decoder, a reconstructed video based on the assigned code matrix.
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Patent Agency Ranking
公开(公告)号：US20240127030A1
公开(公告)日：2024-04-18
申请号：US18109710
申请日：2023-02-14
Applicant: Samsung Display Co., Ltd.
Inventor： Qisen Cheng , Shuhui Qu , Kaushik Balakrishnan , Janghwan Lee
Abstract: A classification system includes: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to: calculate reference Shapley values for features of a data sample based on a first classification model; and train a second classification model though multi-task distillation to: predict Shapley values for the features of the data sample based on the reference Shapley values and a distillation loss; and predict a class label for the data sample based on the predicted Shapley values and a ground truth class label for the data sample.
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公开(公告)号：US11830240B2
公开(公告)日：2023-11-28
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
CPC classification number: G06V10/7784 , 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.
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公开(公告)号：US20230333533A1
公开(公告)日：2023-10-19
申请号：US18339379
申请日：2023-06-22
Applicant: Samsung Display Co., Ltd.
Inventor： Janghwan Lee
IPC: G05B19/4063 , G06N3/08
CPC classification number: G05B19/4063 , G06N3/08 , G05B2219/34477
Abstract: A method for detecting a fault includes: receiving a plurality of time-series sensor data obtained in one or more manufacturing processes of an electronic device; arranging the plurality of time-series sensor data in a two-dimensional (2D) data array; providing the 2D data array to a convolutional neural network model; identifying a pattern in the 2D data array that correlates to a fault condition using the convolutional neural network model; providing a fault indicator of the fault condition in the one or more manufacturing processes of the electronic device; and determining that the electronic device includes a fault based on the fault indicator. The 2D data array has a dimension of an input data to the convolutional neural network model.
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公开(公告)号：US11694319B2
公开(公告)日：2023-07-04
申请号：US16938812
申请日：2020-07-24
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 , G06F18/241 , G06F18/251 , G06N3/045 , G06N3/08 , G06N20/20 , G06V10/70 , G06V10/82 , G06V20/69 , G06T2207/10056 , G06T2207/10116 , G06T2207/20081 , G06T2207/20084
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.
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公开(公告)号：US20220374720A1
公开(公告)日：2022-11-24
申请号：US17367179
申请日：2021-07-02
Applicant: Samsung Display Co., Ltd.
Inventor： Shuhui Qu , Janghwan Lee , Yan Kang
Abstract: Systems and methods for classifying products are disclosed. A first data sample having a first portion and a second portion is identified from a training dataset. A first mask is generated based on the first data sample, where the first mask is associated with the first portion of the first data sample. A second data sample is generated based on a noise input. The first mask is applied to the second data sample for outputting a third portion of the second data sample. The third portion of the second data sample is combined with the second portion of the first data sample for generating a first combined data sample. Confidence and classification of the first combined data sample are predicted. The first combined data sample is added to the training dataset in response to predicting the confidence and the classification.
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