Hitachi-High-Technologies Corporation

Hitachi High-Tech Corporation

Hitachi, Ltd.

Glory Ltd.

Ricoh Printing Systems, Ltd.

Yasutaka Toyoda

Ryoichi Matsuoka

Shinichi Shinoda

Hiroyuki Shindo

Akiyuki Sugiyama

Takumichi Sutani

Mitsuji Ikeda

Hitoshi Sugahara

Hidetoshi Morokuma

Yuichi Abe

Masayoshi Ishikawa

Tatsuki Inuzuka

Hidemitsu Naya

Hideo Sakai

Norio Hasegawa

Ikuo Hiyama

Yuji Takagi

Tsunenori Yamamoto

Makoto Tsumura

Tsuyoshi Minakawa

Hiroyuki Ushiba

Shuyang Dou

Takeshi Kato

Takuma Yamamoto

Shigetoshi Sakimura

Tatsuki Inuduka

Yoshimichi Sato

Ryou Yumiba

Yutaka Hojo

Yasunari Souda

Takefumi Kakinuma

Akihiro Onizawa

Koji Arai

Masanori Ouchi

Daisuke Hibino

Maki Tanaka

Yoshiyuki Kaneko

Takashi Hiroi

Chie Shishido

Yoshiro Mikami

Hideyuki Kazumi

Atsushi Miyamoto

Takehiro Hirai

Hiroya Ohta

Atsushi Takane

Akitoyo Konno

Takeshi Ohmori

Zhaohui Cheng

Shinji Matsuura

Taku Ninomiya

Atsuko Yamaguchi

Masaki Inoue

Tomohiro Funakoshi

Hideto Dohi

Toshio Numata

Yasunori Goto

Tomoyasu Sato

Fumihiko Fukunaga

Masaki Hasegawa

Yutaka Okuyama

Teruo Sudo

Yoshihiro Ota

Takahiro Nishihata

Keita Ito

Takeyuki Yoshida

Hiroyuki Sindo

Kentaro Ohira

Fumihiro Bekku

Yoshimichi Satou

Toshinori Yamauchi

Atsushi Takayama

According to the present invention, an image recognition system calculates importance of a feature for each target shape recognized in an image and for each type of feature, and determines correctness of a recognition result by comparing the importance with a statistic for each type of feature, for each target shape.

Image recognition system

This disclosure relates to a system for performing efficient learning of a specific portion. To achieve this purpose, there is proposed a system configured to generate a converted image on the basis of input of an input image, the system comprising a learning model in which parameters are adjusted so as to suppress an error between the input image and a second image converted upon input of the input image, the learning model being subjected to different learning at least between a first area in the image and a second area different from the first area.

System for generating image, and non-transitory computer-readable medium

A training method that performs learning using appropriate low-quality image and high-quality image is provided. The invention is directed to a training method including executing learning by inputting a first image generated under a first image generation condition and a second image generated under a second image generation condition different from the first image generation condition to a learning apparatus that adjusts parameters so as to suppress an error between an input image and a converted image, in which the second image is selected such that an index value extracted from the second image is the same as or has a predetermined relationship with an index value extracted from the first image, or the second image is output from a second image generation tool different from a first image generation tool for generating the first image.

Training method for learning apparatus, and image generation system

The purpose of the present invention is to provide a computer program for achieving die-to-database inspection at high speed and with few false reports, and a semiconductor inspection device using the same. To achieve this purpose, the present invention proposes: a computer program comprising an encoder layer that is configured to determine the features of a design data image, and a decoder layer that is configured to generate, on the basis of a variation in an image (inspection target image) obtained by photographing an inspection target pattern, a statistic pertaining to the brightness values of pixels from feature values output by the encoder layer, wherein die-to-database inspection with few false reports can be achieved by comparing the inspection target image and the statistic obtained from the decoder layer and pertaining to the brightness values, and thereby detecting a defect region in the image; and a semiconductor inspection device using the same.

Image processing program, image processing device, and image processing method

A method, an apparatus, and a program for more appropriately determining a condition for appropriately recognizing a semiconductor pattern are provided. A method for determining a condition related to a captured image of a charged particle beam apparatus including: acquiring, by a processor, a plurality of captured images, each of the captured images being an image generated by irradiating a pattern formed on a wafer with a charged particle beam, and detecting electrons emitted from the pattern, each of the captured images being an image captured according to one or more imaging conditions, the method further including: acquiring teaching information for each of the captured images; acquiring, by the processor, one or more feature determination conditions; calculating, by the processor, a feature for each of the captured images based on each of the feature determination conditions, at least one of the imaging condition and the feature determination condition being plural.

Method, apparatus, and program for determining condition related to captured image of charged particle beam apparatus

The purpose of the present invention is to provide an image evaluation device and method which can detect unknown defects and which can prevent misrecognition by a machine learning model. This image evaluation device, which uses a machine learning classifier to classify defect information in a defect image of an electronic device, is characterized by being provided with: an image storage unit which stores a defect image of an electronic device; a defect region storage unit which stores defect region information that is in the defect image; a classifier which classifies the defect information with machine learning; an image extraction unit which, in the course of the defect image classification processing, extracts image-of-interest information which the classifier will focus on; and an evaluation unit which compares the image-of-interest information and the defect region information to evaluate the classifiability of the defect image.

Image evaluation apparatus and image evaluation method

In order to select an optimal learning model for an image when inference is carried out in the extraction of a profile line using machine learning, without requiring a correct value or degree of certainty, a feature extraction learning model group containing a plurality of learning models is used for feature extraction. A recall learning model group containing recall learning models is paired with the feature extraction learning models. A feature amount extraction unit for referencing a feature extraction learning model and extracting a feature amount from input data; a data-to-data recall unit for referencing a recall learning model and outputting a recall result with the feature amount subjected to dimensional compression; and a learning model selection unit for selecting a feature extraction learning model from the feature extraction learning model group under the condition that the difference between the feature amount and the recall result is minimized are provided.

Image recognition device and method

An object of the present invention is to achieve both suppression of data amount of an image processing system that learns a collation image to be used for image identification using a discriminator and improvement of identification performance of the discriminator. In order to achieve the above object, there is proposed an image processing system including a discriminator that identifies an image using a collation image, the image processing system further including a machine learning engine that performs machine learning of collation image data required for image identification. The machine learning engine searches for a successfully identified image using an image for which identification has been failed, and adds information, obtained based on a partial image of the image for which identification has been failed and which has been selected by an input device to the successfully identified image obtained by the search to generate corrected collation image data.

Image processing system and computer program for performing image processing

The disclosure relates to a dimension measurement apparatus that reduces time required for dimension measurement and eliminates errors caused by an operator. Therefore, the dimension measurement apparatus uses a first image recognition model that extracts a boundary line between a processed structure and a background over the entire cross-sectional image and/or a boundary line of an interface between different kinds of materials, and a second image recognition that output information for dividing the boundary line extending over the entire cross-sectional image obtained from the first image recognition model for each unit pattern constituting a repetitive pattern, obtains coordinates of a plurality of feature points defined in advance for each unit pattern, and measures a dimension defined as a distance between two predetermined points of the plurality of feature points.

Dimension measurement apparatus, dimension measurement program, and semiconductor manufacturing system

A pattern inspection system inspects an image of an inspection target pattern of an electronic device using an identifier constituted by machine learning, based on the image of the inspection target pattern of the electronic device and data used to manufacture the inspection target pattern. The system includes a storage unit which stores a plurality of pattern images of the electronic device and pattern data used to manufacture a pattern of the electronic device, and an image selection unit which selects a learning pattern image used in the machine learning from the plurality of pattern images, based on the pattern data and the pattern image stored in the storage unit.

Pattern inspection system

A money handling machine includes: a storage assembly including a storage unit for storing money and feeding out the stored money; a collection unit configured to store the money fed out from the storage unit; a replenishing unit configured to store money with which an external device is replenished; and a control unit configured to manage the money stored in the storage unit of the storage assembly under a first management authority, and manage money stored in the collection unit and at least a part of money stored in the replenishing unit under a second management authority.

Money handling machine and money handling system

The image evaluation device includes a design data image generation unit that images design data; a machine learning unit that creates a model for generating a design data image from an inspection target image, using the design data image as a teacher and using the inspection target image corresponding to the design data image; a design data prediction image generation unit that predicts the design data image from the inspection target image, using the model created by the machine learning unit; a design data image generation unit that images the design data corresponding to the inspection target image; and a comparison unit that compares a design data prediction image generated by the design data prediction image generation unit and the design data image. As a result, it is possible to detect a systematic defect without using a defect image and generating misinformation frequently.

Image evaluation method and image evaluation device

One of the objects of the present invention is to provide a semiconductor measurement apparatus capable of obtaining a measurement result that appropriately reflects the deformation of a pattern even if plural causes for the deformation of the pattern exist together. In order to attain the above object, the semiconductor measurement apparatus is proposed in the following way. The semiconductor measurement apparatus is capable of measuring the dimensions between plural measurement points of different positions of the edge of a reference pattern and plural corresponding points of the circuit pattern of an electronic device, in which the corresponding points correspond to the plural measurement points. In addition, the semiconductor measurement apparatus is capable of measuring the spacings between the circuit pattern and the reference pattern, both of which exist in a predefined measurement area, selecting a second measured value aggregation from a first measured value aggregation comprised of measured values at plural points in the measurement area based on a predefined sampling condition, and calculating the measurement value based on the second measured value aggregation.

Semiconductor measurement apparatus and computer program

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Hitachi, Japan

Yasutaka Toyoda