Hitachi, Ltd.

Hitachi-High-Technologies Corporation

Sony Corporation

Renesas Technology Corp.

Hitachi Computer Products (Europe) S.a.s.

Hitachi Power Solutions Co., Ltd.

The University of Tokyo

Hitachi High-Tech Electronics Engineering Co., Ltd.

Tsuru Educational Foundation

Shunji Maeda

Minoru Yoshida

Kaoru Sakai

Yukihiro Shibata

Takafumi Okabe

Sachio Uto

Akira Hamamatsu

Hisae Shibuya

Toshihiko Nakata

Hitoshi Kubota

Kenji Oka

Yuta Urano

Hidetoshi Nishiyama

Takashi Hiroi

Chie Shishido

Atsushi Yoshida

Maki Tanaka

Masahiro Watanabe

Hiroyuki Nakano

Takanori Ninomiya

Yasuhiko Nakayama

Hiroshi Makihira

Hiroaki Shishido

Minori Noguchi

Yuji Takagi

Mari Nozoe

Haruo Yoda

Yasutsugu Usami

Aritoshi Sugimoto

Atsuko Takafuji

Atsushi Shimoda

Kazuo Yamaguchi

Yasuhiro Yoshitake

Yoshimasa Oshima

Yutaka Kaneko

Hiroyuki Shinada

Yusuke Yajima

Katsuhiro Kuroda

Katsuya Sugiyama

Kimiaki Ando

Kazushi Yoshimura

Hiroshi Tooyama

Tadao Ino

Asahiro Kuni

Shunichi Sukegawa

Junichi Ishibashi

Toshiyuki Nakao

Taketo Ueno

Hideaki Doi

Joaquim Da Silva

Shigenobu Maruyama

Motoshige Okada

Claude Launay

Florent Voisin

Tomoaki Hirai

Hiroyuki Shinoda

Kenji Watanabe

Hiroshi Goto

Makoto Ono

Yukio Kembo

Takenori Hirose

Hirofumi Tsuchiyama

Yukio Kenbo

Naoya Takeuchi

Masayuki Kuwabara

Mitsunobu Isobe

Satoru Fushimi

Takahiko Suzuki

Masahiko Nakada

Toshifumi Honda

Takehiro Hirai

Yoshinori Nakayama

Satoru Takahashi

Atsushi Taniguchi

Yukio Honda

Sumio Hosaka

Akira Shimase

Masataka Shiba

Tsuyoshi Hasegawa

Tetsuya Matsui

Mikio Ichihashi

Shigeyuki Hosoki

Yasuo Nakagawa

Fumikazu Itoh

Satoshi Haraichi

Yuko Otani

Hirohito Okuda

Junzou Azuma

Yukio Matsuyama

Yoichi Takahara

Masakazu Ichikawa

Yoshimasa Ooshima

Taku Yamagata

Toujirou Noda

Kiyoshi Takamasu

Hironobu Matsui

Shouzou Miyabe

Tadashi Suzuki

A solid state image sensor of the present disclosure includes: a first semiconductor substrate provided with at least a pixel array unit in which pixels that perform photoelectric conversion are arranged in a matrix form; and a second semiconductor substrate provided with at least a control circuit unit that drives the pixels. The first semiconductor substrate and the second semiconductor substrate are stacked, with first surfaces on which wiring layers are formed facing each other, the pixel array unit is composed of a plurality of divided array units, the control circuit unit is provided corresponding to each of the plurality of divided array units, and electrical connection is established in each of the divided array units, through an electrode located on each of the first surfaces of the first semiconductor substrate and the second semiconductor substrate, between the pixel array unit and the control circuit unit.

Stacked substrate solid state image sensor

Solid state image sensor comprising stacked substrates, semiconductor device, and electronic device

Solid state image sensor, semiconductor device, and electronic device

An objective is to identify the health state of mechanical equipment and provide information usable for determining maintenance work timing or the like. A health management system includes a time-series data acquisition unit configured to acquire multi-dimensional sensor data and environmental data from mechanical equipment; a first discrimination unit configured to quantify the equipment state of the mechanical equipment by a statistical method using normal data as learning data; a second discrimination unit configured to quantify the health state indicating the performance or quality of the mechanical equipment by a statistical method using normal data; and an output unit configured to display and/or output to the outside the quantified equipment state and health state.

Health management system, fault diagnosis system, health management method, and fault diagnosis method

To sensing an anomaly on the basis of a multi-dimensional time series sensor signal, in order to determine the next action for a countermeasure, survey, or the like, the present invention is configured such that a multi-dimensional feature vector for each time is extracted on the basis of a sensor signal, a reference feature vector for each time is extracted on the basis of a set of characteristic vectors for a predetermined learning period and the characteristic vector of each time, an anomaly measure is calculated on the basis of the difference between the feature vectors for the times and the reference feature vectors, an anomaly is detected by comparing the anomaly measure and a predetermined threshold value, and the anomaly-related sensor for the time the anomaly is detected is identified on the basis of a 2-dimensional distribution density of feature values.

Anomaly diagnosis method and apparatus

A method for reviewing a defect including a light capturing step that illuminates a sample with light under plural optical conditions, while varying only at least one of illumination conditions, sample conditions, or detection conditions, and detects plural lights scattering from the sample; a signal obtaining step that obtains plural signals based on the lights detected; and a processing step that discriminates a defect from noise according to a waveform characteristic quantity, an image characteristic quantity, or a value characteristic quantity created using the signals and derives the coordinates of defect.

Method for reviewing a defect and apparatus

In case-based anomaly indication detection in a facility, there are problems such as error generation due to insufficient learning data or execution difficulty due to increased memory capacity and calculation time when the learning data period has been increased to obtain the learning data sufficiently. Provided is a method for monitoring facility state on the basis of a time series signal outputted from the facility, wherein an operation pattern label for each fixed interval is assigned on the basis of the time series signal, learning data is selected on the basis of the operation pattern label for each fixed interval, a normal model is created on the basis of the selected learning data, an anomaly measure is calculated on the basis of the time series signal and the normal model, and the facility state is determined to be anomaly or normal on the basis of the calculated anomaly measure.

Facility state monitoring method and device for same

Provided is an anomaly detection method and system capable of constructing determination condition rules of anomaly detection from case-based anomaly detection by way of multivariate analysis of a multi-dimensional sensor signal, applying the rules to design-based anomaly detection of individual sensor signals, and also appropriately executing setting and control of threshold values for highly sensitive, early, and clearly visible detection of anomalies. Anomaly detection on the basis of a case base by way of multivariate analysis controls design-based anomaly detection. That is to say, (1) anomaly detection on the basis of a case base performs selection of sensor signals and anomaly detection according to various types of anomalies. Specifically, anomaly detection (characteristic conversion), evaluation of level of effect of each signal, construction of determination conditions (rules), and display and selection of sensor signals corresponding to the anomaly are performed. (2) Design-based anomaly detection for individual sensor signals performs anomaly detection after the above have been performed. Specifically, setting and control of thresholds, display of thresholds, and anomaly detection and display are performed.

Anomaly detection and diagnosis/prognosis method, anomaly detection and diagnosis/prognosis system, and anomaly detection and diagnosis/prognosis program

The pattern inspection apparatus of the present invention performs comparison between images of regions corresponding to patterns formed to be same patterns, thereby determining mismatch portions across the images to be defects. The apparatus includes multiple sensors that synchronously acquire images of shiftable multiple detection systems different from one another, and an image comparator section corresponding thereto. In addition, the apparatus includes a means for detecting a statistical offset value from the feature amount to be a defect, thereby properly detecting the defect even when a brightness difference is occurring in association with film a thickness difference in a wafer.

Method and apparatus for inspecting patterns formed on a substrate

The invention provides a defect inspection method and a defect inspection device which enable a defect to be inspected regardless of optical conditions. The invention comprises the steps of setting a target local region and a plurality of corresponding local regions in the image signals, the target local region including a target pixel and an area surrounding the target pixel, the corresponding local regions including pixels corresponding to the target pixel and areas surrounding the corresponding pixels; searching similarities between the image signal of the target local region and the image signals of the plurality of corresponding local regions; determining a plurality of image signals that represent corresponding local regions and are similar to the image signal of the target local region; and comparing the image signal of the target local region with the image signals that represent the corresponding local regions.

Defect inspection method and device thereof

A pattern inspection apparatus is provided to compare images of regions, corresponding to each other, of patterns that are formed so as to be identical and judge that non-coincident portions in the images are defects. The pattern inspection apparatus is equipped with an image comparing section which plots individual pixels of an inspection subject image in a feature space and detects excessively deviated points in the feature space as defects. Defects can be detected correctly even when the same patterns in images have a brightness difference due to a difference in the thickness of a film formed on a wafer.

Defect inspection method and apparatus

When using a CCD sensor as a photo-detector in a device for inspecting foreign matters and defects, it has a problem of causing electric noise while converting the signal charge, produced inside by photoelectric conversion, into voltage and reading it. Therefore, the weak detected signal obtained by detecting reflected and scattered light from small foreign matters and defects is buried in the electric noise, which has been an obstacle in detecting small foreign matters and defects. In order to solve the above problem, according to the present invention, an electron multiplying CCD sensor is used as a photo-detector. The electron multiplying CCD sensor is capable of enlarging signals brought about by inputted light relatively to the electric noise by multiplying the electrons produced through photoelectric conversion and reading them. Accordingly, compared to a conventional CCD sensor, it can detect weaker light and, therefore, smaller foreign matters and defects.

Method for inspecting pattern defect and device for realizing the same

By including an illumination system and a detection system, an information collecting function of monitoring an environment, such as temperature and atmospheric pressure, and an apparatus state managing function having a feedback function of comparing the monitoring result and a design value, a theoretical calculation value or an ideal value derived from simulation results and calibrating an apparatus so that the monitoring result is brought close to the ideal value, a unit for keeping the apparatus state and apparatus sensitivity constant is provided. A control unitis configured to include a recording unit, a comparing unit, a sensitivity predicting unit, and a feedback control unit. In the comparing unit, the monitoring result transmitted from the recording unitand an ideal value stored in a databaseare compared with each other. When a difference between the ideal value and the monitoring result exceeds a predetermined threshold, the feedback control unitcorrects the illumination system and the detection system.

Dark-field defect inspecting method, dark-field defect inspecting apparatus, aberration analyzing method, and aberration analyzing apparatus

This invention provides method for detecting advance signs of anomalies, event signals outputted from the facility are used to create a separate mode for each operating state, a normal model is created for each mode, the sufficiency of learning data for each mode is checked, a threshold is set according to the results of said check, and anomaly identification is performed using said threshold. Also, for diagnosis, a frequency matrix is created in advance, with result events on the horizontal axis and cause events on the vertical axis, and the frequency matrix is used to predict malfunctions. Malfunction events are inputted as result events, and quantized sensor signals having anomaly measures over the threshold are inputted as cause events.

Method and device for monitoring the state of a facility

A defect classification method to classify defects by using a classifier having a binary tree structure based on features of defects extracted from detected signals acquired from a defect inspection apparatus includes a classifier construction process for constructing the classifier by setting a branch condition including defect classes respectively belonging to groups located on both sides of the branch point, a feature to be used for branching, and a discriminant reference, for each branch point in the structure based on instruction of defect classes and feature data respectively associated therewith beforehand. The process includes a priority order specification process for previously specifying target classification performance of purity and accuracy for each defect class, whole and in worst case, with priority order, and an evaluation process for evaluating whether the specified target classification performance under the branching condition is satisfied and displaying a result of evaluation, every item.

Defect classification method and apparatus, and defect inspection apparatus

Growing community of inventors

Yokohama, Japan

Shunji Maeda