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    SYSTEMS AND METHODS FOR IDENTIFYING MANUFACTURING DEFECTS
    21.
    发明申请
    SYSTEMS AND METHODS FOR IDENTIFYING MANUFACTURING DEFECTS 有权

    公开(公告)号：US20220318672A1

    公开(公告)日：2022-10-06

    申请号：US17306737

    申请日：2021-05-03

    Applicant: Samsung Display Co., Ltd.

    Inventor： Shuhui Qu ,  Janghwan Lee ,  Yan Kang

    IPC: G06N20/00 ,  G06N5/04

    Abstract: Systems and method for classifying manufacturing defects are disclosed. In one embodiment, a first data sample satisfying a first criterion is identified from a training dataset, and the first data sample is removed from the training dataset. A filtered training dataset including a second data sample is output. A first machine learning model is trained with the filtered training dataset. A second machine learning model is trained based on at least one of the first data sample or the second data sample. Product data associated with a manufactured product is received, and the second machine learning model is invoked for predicting confidence of the product data. In response to predicting the confidence of the product data, the first machine learning model is invoked for generating a classification based the product data.

    Systems and methods for concept intervals clustering for defect visibility regression
    24.
    发明授权
    Systems and methods for concept intervals clustering for defect visibility regression 有权

    公开(公告)号：US12229706B2

    公开(公告)日：2025-02-18

    申请号：US17401216

    申请日：2021-08-12

    Applicant: Samsung Display Co., Ltd.

    Inventor： Janghwan Lee

    IPC: G06Q10/0639 ,  G06N20/00 ,  G06Q50/04 ,  G06T7/00

    Abstract: Systems and methods for making predictions relating to products manufactured via a manufacturing process. A processor receives a plurality of input vectors associated with a plurality of output values and a plurality of time intervals. The processor clusters the plurality of input vectors based on the time intervals associated with the input vectors. The processor trains a machine learning model for each time interval of the plurality of time intervals, where the training of the machine learning model is based on the input vectors associated with the time interval, and the output values associated with the input vectors. The processor further trains a classifier for selecting one of the plurality of time intervals for input data received for a product. In one embodiment, the machine learning model associated with the time interval selected by the classifier is invoked to predict an output based on the input data.

    System and method for knowledge distillation
    26.
    发明授权
    System and method for knowledge distillation 有权

    公开(公告)号：US12106226B2

    公开(公告)日：2024-10-01

    申请号：US18207005

    申请日：2023-06-07

    Applicant: Samsung Display Co., Ltd.

    Inventor： Janghwan Lee

    IPC: G06N3/04 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06N3/088

    CPC classification number: G06N3/088 ,  G06N3/045 ,  G06N3/047

    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.

    System and method for generating machine learning model with trace data
    27.
    发明授权
    System and method for generating machine learning model with trace data 有权

    公开(公告)号：US11714397B2

    公开(公告)日：2023-08-01

    申请号：US16403381

    申请日：2019-05-03

    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.

    SYSTEMS AND METHODS FOR CONCEPT INTERVALS CLUSTERING FOR DEFECT VISIBILITY REGRESSION
    28.
    发明申请
    SYSTEMS AND METHODS FOR CONCEPT INTERVALS CLUSTERING FOR DEFECT VISIBILITY REGRESSION 有权

    公开(公告)号：US20220398525A1

    公开(公告)日：2022-12-15

    申请号：US17401216

    申请日：2021-08-12

    Applicant: Samsung Display Co., Ltd.

    Inventor： Janghwan Lee

    IPC: G06Q10/06 ,  G06Q50/04 ,  G06N20/00 ,  G06T7/00

    Abstract: Systems and methods for making predictions relating to products manufactured via a manufacturing process. A processor receives a plurality of input vectors associated with a plurality of output values and a plurality of time intervals. The processor clusters the plurality of input vectors based on the time intervals associated with the input vectors. The processor trains a machine learning model for each time interval of the plurality of time intervals, where the training of the machine learning model is based on the input vectors associated with the time interval, and the output values associated with the input vectors. The processor further trains a classifier for selecting one of the plurality of time intervals for input data received for a product. In one embodiment, the machine learning model associated with the time interval selected by the classifier is invoked to predict an output based on the input data.

    SYSTEM AND METHOD FOR REASSIGNMENT CLUSTERING FOR DEFECT VISIBILITY REGRESSION
    29.
    发明申请
    SYSTEM AND METHOD FOR REASSIGNMENT CLUSTERING FOR DEFECT VISIBILITY REGRESSION 有权

    公开(公告)号：US20220343210A1

    公开(公告)日：2022-10-27

    申请号：US17327618

    申请日：2021-05-21

    Applicant: Samsung Display Co., Ltd.

    Inventor： Janghwan Lee ,  Steven Munn

    IPC: G06N20/00 ,  G06K9/62 ,  G06Q50/04

    Abstract: A method of training a system for making predictions relating to products manufactured via a manufacturing process includes receiving a plurality of input vectors and a plurality of defect values corresponding to the plurality of input vectors, identifying a plurality of first cluster labels corresponding to the plurality of input vectors based on the defect values, training a cluster classifier based on the input vectors and the corresponding first cluster labels, reassigning the input vectors to a plurality of second cluster labels based on outputs of the cluster classifier, retraining the cluster classifier based on the input vectors and the second cluster labels, and training a plurality of machine learning models corresponding to the second cluster labels.

    SYSTEMS AND METHODS FOR IDENTIFYING MANUFACTURING DEFECTS
    30.
    发明申请
    SYSTEMS AND METHODS FOR IDENTIFYING MANUFACTURING DEFECTS 有权

    公开(公告)号：US20220343140A1

    公开(公告)日：2022-10-27

    申请号：US17317806

    申请日：2021-05-11

    Applicant: Samsung Display Co., Ltd.

    Inventor： Shuhui Qu ,  Janghwan Lee ,  Yan Kang

    IPC: G06N3/04 ,  G06N3/08

    Abstract: Systems and method for classifying manufacturing defects are disclosed. A first machine learning model is trained with a training dataset, and a data sample that satisfies a criterion is identified from the training dataset. A second machine learning model is trained to learn features of the data sample. When an input dataset that includes first and second product data is received, the second machine learning model is invoked for predicting confidence of the first and second product data based on the learned features of the data sample. In response to predicting the confidence of the first and second product data, the first product data is removed from the dataset, and the first machine learning model is invoked for generating a classification based the second product data.

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