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    SYSTEMS AND METHODS FOR SAMPLE GENERATION FOR IDENTIFYING MANUFACTURING DEFECTS
    2.
    发明申请
    SYSTEMS AND METHODS FOR SAMPLE GENERATION FOR IDENTIFYING MANUFACTURING DEFECTS 有权

    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

    IPC: G06N3/08 ,  G06K9/62 ,  G06K9/74 ,  G06N3/063

    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.

    SYSTEMS AND METHODS FOR IDENTIFYING MANUFACTURING DEFECTS
    5.
    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.

    IMAGE-BASED DEFECTS IDENTIFICATION AND SEMI-SUPERVISED LOCALIZATION
    6.
    发明申请
    IMAGE-BASED DEFECTS IDENTIFICATION AND SEMI-SUPERVISED LOCALIZATION 有权

    公开(公告)号：US20210319546A1

    公开(公告)日：2021-10-14

    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 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20

    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.

    FUSION MODEL TRAINING USING DISTANCE METRICS
    7.
    发明申请
    FUSION MODEL TRAINING USING DISTANCE METRICS 有权

    公开(公告)号：US20210319270A1

    公开(公告)日：2021-10-14

    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: G06K9/62 ,  G06N3/08 ,  G06N3/04

    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.

    SYSTEMS AND METHODS FOR IDENTIFYING MANUFACTURING DEFECTS
    10.
    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.

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