Sony Corporation

Hitachi-high-technologies Corporation

Tokyo Electron Limited

Hitachi, Ltd.

Hitachi High-tech Corporation

Kabushiki Kaisha Toshiba

Nec Corporation

Thomson Licensing

Tokyo Electron Yamanashi Limited

Octec, Inc.

Yuichi Abe

Yoichiro Sako

Takatoshi Nakamura

Mitsuru Takehara

Toshiro Terauchi

Toru Sasaki

Mitsuji Ikeda

Itaru Kawakami

Norikazu Hiraki

Motoyuki Takai

Wataru Nagatomo

Ryoichi Imaizumi

Masamichi Asukai

Hiroyuki Segawa

Yasutaka Toyoda

Katsuya Shirai

Ryo Fujita

Kenichiro Nakamura

Masao Yamaguchi

Katsunori Suzuki

Shinya Ishii

Kazuyoshi Koga

Kazuhisa Takami

Mitsuru Soga

Yoshihiro Manabe

Kazunori Oniki

Masahiro Kitazawa

Yasushi Miyajima

Kosei Yamashita

Koyo Katsura

Akihiro Komori

Hiroyuki Shindo

Hideki Noma

Hiroyuki Shioya

Jun'ichi Taguchi

Yoshimichi Sato

Junichi Taguchi

Akifumi Kashiwagi

Tsugutomo Enami

Hiroyuki Ushiba

Hideki Fujii

Taketoshi Itoyama

Yoshimichi Satou

Katsuya Okumura

Takashi Shibata

Hiromichi Yamada

Takashi Totsuka

Yasuhiro Nakatsuka

Masanori Kato

Akiyuki Sugiyama

Takanaga Yamazaki

Masayoshi Ishikawa

Shuji Akiyama

Shigekazu Komatsu

Osamu Inoue

Shinichi Shinoda

Kunihiro Furuya

Yoichiro Suzuki

Tsukasa Matoba

Hisatomi Hosaka

Hideaki Tanaka

Takahiro Kawasaki

Akio Yoshioka

Kyoungmo Yang

Shohei Ikeda

Kenichi Kubo

Kentarou Kudou

Vincent Vezin

Tsuyoshi Aruga

Takao Shimada

Munetosi Nagasaka

Wataru Mochizuki

Liang Li

Motoki Masaka

The learning device learns the target detection model used in the radar device. The learning device includes an acquisition unit, a learning data generation unit, and a learning processing unit. The acquisition unit acquires a reception signal generated based on the received wave and a tracking signal generated based on the reception signal from the radar device. The learning data generation unit generates learning data using the reception signal and the tracking signal. The learning processing unit learns a target detection model that detects a target from the reception signal, using the learning data.

Learning device, learning method, recording medium, and radar device

A pattern matching apparatus includes a computer system configured to execute pattern matching processing between first pattern data based on design data and second pattern data representing a captured image 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.

Pattern matching device, pattern measurement system, and non-transitory computer-readable medium

The purpose of the present invention is to provide a pattern matching device and computer program that carry out highly accurate positioning even if edge positions and numbers change. The present invention proposes a computer program and a pattern matching device wherein a plurality of edges included in first pattern data to be matched and a plurality of edges included in second pattern data to be matched with the first pattern data are associated, a plurality of different association combinations are prepared, the plurality of association combinations are evaluated using index values for the plurality of edges, and matching processing is carried out using the association combinations selected through the evaluation.

Pattern matching device and computer program for pattern matching

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

Provided are a liquid surface inspection device, an automated analysis device, and a liquid surface inspection method with which instances of contamination can be minimized and the accuracy of the manner in which the surface conditions, such as bubbles or the like, of a liquid substance are detected can be enhanced. The device has: a light illumination unit for illuminating a container holding a liquid substance, as well as the surface of the liquid substance, with light; an image capture unit for acquiring a video image having at least color information and brightness information of light from the container and the liquid substance which are illuminated by the light illumination unit; and a detection unit for using the color information and brightness information in the video image captured by the image capture unit to detect the condition of the liquid surface.

Liquid surface inspection device, automated analysis device, and processing device

It is an object of the present invention to provide a semiconductor inspection apparatus capable of well carrying out position alignment and correctly determining whether the position alignment has been carried out successfully or has ended in a failure without operator interventions even if an inspected image is an image having few characteristics as is the case with a repetitive pattern or the inspected image is an image having a complicated shape. The semiconductor inspection apparatus includes means for imaging a shape on a wafer or on an exposure mask; means for storing an image inspected by the imaging means; means for storing design data of the semiconductor circuit corresponding to a position on the wafer or on the exposure mask which are to be imaged by the imaging means; means for storing a design-data image obtained as a result of converting the design data into an image; means for generating a design-data ROI image by converting an interest drawing region found from a relative crude-density relation of a shape included in the design-data image into an image; and a position alignment section configured to carry out position alignment on the inspected image and the design-data image. The semiconductor inspection apparatus makes use of the design-data ROI image in order to identify a position at which the inspected image and the design-data image match each other or compute the degree of coincidence.

Pattern matching apparatus and computer program

To provide means according to which template matching is performed successfully in an apparatus that performs inspection or measurement of a semiconductor pattern, which is formed on a wafer, even in a case in which a pattern for alignment, which is in design data, has been eliminated from an image in which an image of a practical pattern was captured using the apparatus, in which the brightness value contrast of a pattern is lower (more indistinct) than that of other locations, or in which a pattern is deformed and there is a discrepancy with the shape of a template (a pattern for alignment in the design data). An inspection apparatus according to the invention acquires a target retrieval image, and carries out template matching on the target retrieval image, and includes template input, means for inputting a plurality of templates, a plurality of matching candidate selection sections that select a matching candidate group by performing a matching process of the target retrieval image and the plurality of templates, a plurality of single template likelihood calculation process sections that calculate single template likelihoods for a plurality of matching candidate groups that are selected by the plurality of matching candidate selection sections, a multiple template assimilation likelihood calculation process section that calculates a multiple template assimilation likelihood for the matching candidate groups using a plurality of single template likelihoods that are calculated by the plurality of single template likelihood calculation process sections, and a highest assimilation likelihood matching candidate selection section that selects a matching candidate for which the multiple template assimilation on likelihood is the highest, from among the matching candidate groups, using the multiple template assimilation likelihoods that are calculated by the multiple template assimilation likelihood calculation process section.

Inspection apparatus and method using pattern matching

A method of using contents includes the steps of setting public time information and private time information, the public time information being associated with each content and defined universally for use by a large number of unspecified users, the private time being associated with the content and defined for use by a small number of particular persons, and searching for a content according to the public time information and/or the private time information and using the content detected in the searching.

Content using method, content using apparatus, content recording method, content recording apparatus, content providing system, content receiving method, content receiving apparatus, and content data format

When the lengths of FEM wafers are automatically measured, not only the sizes of targets, the lengths of which are to be measured, are often varied from those in registration, but also the patterns of the targets are often deformed. Therefore, it is difficult to automatically determine whether the length measurement is possible or not. Therefore, the following are executed with a semiconductor inspection system: (1) a process of identifying the position of the contour line of an inspected image using a distance image calculated from a reference image, (2) a process of calculating a defect size image based on the position of the contour line with respect to the identified distance image, and detecting a defect candidate from the defect size image, and (3-1) a process of, upon detection of the defect candidate, calculating the size of the detected defect candidate, or (3-2) a process of detecting a portion different between the first and second contour lines as the defect candidate.

Semiconductor inspection system

An inspection device that performs pattern matching on a searched image performs matching between a template image of an inspection object and the searched image by using: a feature region extraction process unit that extracts a feature quantity from the template image acquired for learning; a feature quantity extraction process unit that extracts a feature quantity from the searched image acquired for learning; a mutual feature quantity calculation process unit that calculates a mutual feature quantity of the template image and the searched image from the feature quantity extracted from the template image and the feature quantity extracted from the searched image; a learning process unit that calculates, using a plurality of the mutual feature quantities, a discrimination boundary surface that determines matching success or failure; a process unit that calculates a plurality of the mutual feature quantities from an image acquired from the inspection object; and the plurality of mutual feature quantities and the discrimination boundary surface. Thus, an inspection device can be provided that outputs an accurate matching position in template matching even when there is a large apparent image discrepancy between the template and the searched image.

Matching process device, matching process method, and inspection device employing same

An evaluation value indicative of the extent of lines in each direction is calculated for a pre-processed image in which 0s are filled in and extended in the lateral direction of the inputted image and which has been reduced &#x215b;th in the longitudinal direction. To obtain the angle of rotation of an image from the change in the evaluation value obtained while the angle relative to the lateral direction of the pre-processed image is modified in small steps, a parallel line is drawn for each direction, a projection is taken, and the sum of squares serves as the evaluation value of the direction. The direction having the highest evaluation value serves as the obtained direction of rotation from the normal position. The projection of each direction references the point of intersection between the parallel line drawn for each direction and the coordinate line of the horizontal axis.

Device and method for detecting angle of rotation from normal position of image

Image processing apparatus includes: interpolation process image acquisition means for acquiring an interpolation process image of prescribed size which includes an interpolation point of an inputted image; Fourier transform means for subjecting the interpolation process image which is acquired with the interpolation process image acquisition means to Fourier transform; phase change means for changing, a phase of each value of the transformed interpolation process image which has been subjected to Fourier transform by the Fourier transform means, such that the interpolation point migrates to a desired nearby integer coordinate position; inverse Fourier transform means for subjecting the interpolation process image whose phase has been changed by the phase change means, to inverse Fourier transform; interpolation value determination means for adopting an interpolation point, a value of a pixel at the integer coordinate position, from the transformed interpolation process image subjected to inverse Fourier transform by the inverse Fourier transform means.

Image processing apparatus, distortion-corrected map creation apparatus, and semiconductor measurement apparatus

A content list display method includes the step of displaying a list of private content owned by a user and public content not owned by the user on a screen by using a predetermined common attribute as a search key attribute for search and display such that a difference between the private content and the public content can be visually recognized.

Content list display method, content list display apparatus, content selecting and processing method, and content selecting and processing apparatus

An inspection apparatus and method are provided, wherein even when an image that cannot be processed by a current image processing algorithm is input to an image processing unit while a working line is in operation, the inspection can be continued by newly generating an image processing algorithm optimized in keeping with a particular image. The apparatus includes an erroneous recognition detector, a teacher data generator and a switching unit for switching the current image processing algorithm to a new image processing algorithm generated based on an updated teacher data group. As a result, the inspection can be continued without extremely decreasing the accuracy even when an unexpected image is input to the working line.

Inspection apparatus

Growing community of inventors

Tokyo, Japan

Yuichi Abe