公开(公告)号：US20230298310A1

公开(公告)日：2023-09-21

申请号：US18009783

申请日：2020-07-09

Applicant: Hitachi High-Tech Corporation

Inventor： Liang LI ,  Wataru NAGATOMO ,  Yuichi ABE

IPC: G06V10/75 ,  G06T5/50 ,  G06V10/60 ,  G06V10/74 ,  G06V10/774

CPC classification number: G06V10/7515 ,  G06T5/50 ,  G06V10/60 ,  G06V10/761 ,  G06V10/774 ,  G06T2207/10061 ,  G06T2207/20081 ,  G06T2207/20221

Abstract: The present disclosure proposes a pattern matching device capable of achieving matching processing that is characterized by involving a learning function even for a semiconductor pattern including a repetitive pattern, in particular. The pattern matching device pertaining to the present disclosure is provided with a learning unit for estimating a first correlation image having, as pixel values thereof, numerical values representing a correlation between a first image and a second image. The pattern matching device calculates a second correlation image having, as pixel values thereof, numerical values representing the correlation between a derivative image generated from the first image and the first image, and the learning unit performs learning so as to reduce the difference between the first correlation image and the second correlation image (refer to FIG. 1).

公开(公告)号：US20230229965A1

公开(公告)日：2023-07-20

申请号：US18084920

申请日：2022-12-20

Applicant: Hitachi High-Tech Corporation

Inventor： Masayoshi ISHIKAWA ,  Daisuke ASAI ,  Yuichi ABE ,  Yohei MINEKAWA ,  Mitsuji IKEDA

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A machine learning system and a machine learning method capable of selecting a pretrained model to be used in transfer learning in a short time without actually executing the transfer learning includes a pretrained model acquisition unit which acquires a pretrained model from a pretrained model storage unit storing a plurality of pretrained models obtained by learning a transfer source task under respective conditions; a transfer learning dataset storage unit configured to store dataset related to a transfer target task; a pretrained model adaptability evaluation unit configured to evaluate adaptability of each pretrained model acquired by the pretrained model acquisition unit to the dataset related to the transfer target task; and a transfer learning unit configured to execute, based on an evaluation result of the pretrained model adaptability evaluation unit, transfer learning using a selected pretrained model and the dataset, and outputs a learning result as a trained model.

公开(公告)号：US20230071668A1

公开(公告)日：2023-03-09

申请号：US17800155

申请日：2020-02-20

Applicant: Hitachi High-Tech Corporation

Inventor： Liang LI ,  Yuichi ABE ,  Wataru NAGATOMO

IPC: G06T7/12 ,  G06V10/44 ,  G06V10/75 ,  G06V10/60

Abstract: A pattern matching apparatus includes a computer system configured to execute pattern matching processing between first pattern data based on design data 104 and second pattern data representing a captured image 102 of an electron microscope. The computer system acquires a first edge candidate group including one or more first edge candidates, acquires a selection-required number (the number of second edge candidates to be selected based on the second pattern data), acquires a second edge candidate group including the second edge candidates of the selection-required number, acquires an association evaluation value for each of different association combinations between the first edge candidate group and the second edge candidate group, selects one of the combinations based on the association evaluation value, and calculates a matching shift amount based on the selected combination.

公开(公告)号：US20240327775A1

公开(公告)日：2024-10-03

申请号：US18580132

申请日：2021-09-27

Applicant: HITACHI HIGH-TECH CORPORATION

Inventor： Mitsuji IKEDA ,  Makoto KATAGISHI ,  Yuichi ABE ,  Yohei MINEKAWA

IPC: C12M1/36

CPC classification number: C12M41/48

Abstract: A training method for a machine learning model can be trained for predicting characteristics after cell culture. The training method for machine learning models includes first performing a cell culture experiment at least three times, in which a plurality of cell images are captured during or after cell culture, and after the cell culture, a plurality of pieces of characteristic information of cells of the plurality of cell images are acquired, and second using some or all of pairs combining the plurality of captured cell images and the characteristic information as training data. In the second step, information indicating a magnitude relationship of the plurality of pieces of characteristic information of the plurality of cell images is classified into at least three classes including large, medium and small classes, and some of the cell images of the large and small classes are trained as the training data.

公开(公告)号：US20240177333A1

公开(公告)日：2024-05-30

申请号：US18282004

申请日：2021-03-25

Applicant: Hitachi High-Tech Corporation

Inventor： Mitsuji IKEDA ,  Yasutaka TOYODA ,  Yuichi ABE ,  Makoto SATO ,  Haruhiko HIGUCHI ,  Masayoshi ISHIKAWA

IPC: G06T7/70

CPC classification number: G06T7/70 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: In order to facilitate generation of teacher data and to detect position coordinates with high reliability, there is provided a localization apparatus including: a deep learning model trained by using training image data in which position coordinates desired to be detected are specified and teacher image data in which a pixel group representing a shape independent of a subject of the training image data is arranged at a position relative to the position coordinates desired to be detected; a position coordinate calculation unit calculating position coordinates by using inference image data output from the deep learning model, and a reliability calculation unit calculating reliability by using global information of the pixel group of the inference image data output from the deep learning model.

公开(公告)号：US20220374785A1

公开(公告)日：2022-11-24

申请号：US17734667

申请日：2022-05-02

Applicant: Hitachi High-Tech Corporation

Inventor： Masayoshi ISHIKAWA ,  Sota KOMATSU ,  Yuichi ABE ,  Mitsuji IKEDA

IPC: G06N20/20 ,  G06K9/62

Abstract: A machine learning system performs transfer learning to output a trained model by performing training using a parameter of a pre-trained model by using a given dataset and a given pre-trained model. The machine learning system includes a dataset storage unit that stores one or more datasets, and a first training unit that performs training using a dataset stored in the dataset storage unit to generate the pre-trained model, and stores the generated pre-trained model in a pre-trained model database. The dataset storage unit stores tag information including any one or more of domain information indicating a target object of data included in a dataset to be stored, class information indicating a class included in data, and data acquisition condition information related to an acquisition condition of data and a dataset in a manner that the tag information and the dataset are associated with each other.