公开(公告)号：US20230290125A1

公开(公告)日：2023-09-14

申请号：US17942815

申请日：2022-09-12

Applicant: Kioxia Corporation

Inventor： Bo WANG ,  Youyang NG ,  Yuchieh LIN ,  Kengo NAKATA ,  Takeshi FUJIWARA

IPC: G06V10/774 ,  G06V10/40 ,  G06V10/82 ,  G06V10/764 ,  G06V20/70 ,  G06V10/776

CPC classification number: G06V10/7747 ,  G06V10/40 ,  G06V10/82 ,  G06V10/764 ,  G06V20/70 ,  G06V10/776

Abstract: An image processing apparatus has a first image acquisitor that acquires a source image, a second image acquisitor that acquires a first target image, a label acquisitor that acquires a label, a feature extractor including a first neural network that extracts a feature of the source image and a feature of the first target image, a class classifier including a second neural network that performs a class classification of the source image and the first target image, a domain classifier including a third neural network that performs a domain classification of the source image and the first target image, a processor that assigns a pseudo label to the first target image, a self-learner that performs a self-learning of the first neural network, the second neural network, and the third neural network, and a learner that learns the first, second and third neural networks, by performing a back propagation process.

公开(公告)号：US20220076398A1

公开(公告)日：2022-03-10

申请号：US17191063

申请日：2021-03-03

Applicant: Kioxia Corporation

Inventor： Youyang NG ,  Bo WANG ,  Takuji OHASHI ,  Osamu YAMANE ,  Takeshi FUJIWARA

IPC: G06T7/00 ,  G06K9/62

Abstract: An information processing apparatus has an acquisitor configured to acquire an entire area image obtained by capturing an entire area of a processing surface of a wafer including at least one defect, a training image selector configured to select, as a training image, a partial image including at least one defect from the entire area image, a model constructor configured to construct a calculation model of generating a label image obtained by extracting and binarizing the defect included in the partial image, and a learner configured to update a parameter of the calculation model based on a difference between the label image generated by inputting the training image to the calculation model and a reference label image obtained by extracting and binarizing the defect of the training image.