Hitachi-high-technologies Corporation

Hitachi High-tech Corporation

Sankyo Company, Limited

Daiichi Sankyo Company, Limited

Hitachi, Ltd.

The University of Tokyo

Takehiro Hirai

Minoru Harada

Yuji Takagi

Kenji Obara

Ryo Nakagaki

Yohei Minekawa

Naoaki Kondo

Kohei Yamaguchi

Toshifumi Honda

Kazuo Aoki

Atsushi Miyamoto

Kenji Nakahira

Keisuke Fukuchi

Toshinori Agatsuma

Junko Konishi

Naoma Ban

Shu Takahashi

Kozo Miyake

Makoto Ono

Kensuke Nakamura

Hideki Nakayama

Naoki Hosoya

Masashi Sakamoto

Yuko Otani

Kenji Iida

Fumihiko Fukunaga

Tomoko Terauchi

Yutaka Tandai

Yuya Isomae

Katsuhiro Kitahashi

Kumi Kaneko

Kimihasa Ichikawa

Shunji Maeda

Yuta Urano

Yasutaka Toyoda

Satoru Takahashi

Akira Ito

Kazuhisa Machida

Hirohito Okuda

Katsuhiko Ichinose

Hitoshi Komuro

Tomohiro Funakoshi

Kimihisa Ichikawa

Hirohiko Kitsuki

Kazuhisa Hasumi

Tsunehiro Sakai

Koichi Hayakawa

Go Kotaki

Kazunori Nemoto

Kiyoshi Takamasu

Nobuhiko Kanzaki

Miyuki Fukuda

Tamao Ishikawa

Takashi Nobuhara

Takafumi Chida

Akira Yoshikawa

Kaori Yaeshima

Masakazu Kanezawa

Kenichi Nishigata

Provided is a wafer observation apparatus includes: a scanning electron microscope; a control unit which includes a wafer observation unit that observes a wafer of a semiconductor device, and an image acquisition unit that acquires a wafer image; a storage unit which includes an image storage unit that stores the wafer image and a template image, and a recipe storage unit that stores a wafer alignment recipe including a matching success and failure determination threshold value, an image processing parameter set, and a use priority associated with the template image; and a calculation unit which includes a recipe reading unit that reads the template image and the wafer alignment recipe, a recipe update necessity determination unit that determines update necessity of the wafer alignment recipe, and a recipe updating unit that updates the wafer alignment recipe based on a determination result in the recipe update necessity determination unit.

Wafer observation apparatus and wafer observation method

It is an object of the present invention to provide an antibody specifically binding to GPR20-positive tumor cells such as GIST, a pharmaceutical product comprising the antibody and having therapeutic effects on a tumor, a method for treating a tumor using the aforementioned pharmaceutical product, and the like. It is another object of the present invention to provide an anti-GPR20 antibody having internalization activity, an antibody-drug conjugate containing the antibody, and the like.

Anti-GPR20 antibody and anti-GPR20 antibody-drug conjugate

A defect observation device is configured to include a charged particle microscope and a controller including a control unit that controls the charged particle microscope, a storage unit, and an arithmetic unit, in which the control unit controls the charged particle microscope under a first condition to acquire a first image of an observation target region of the sample, the arithmetic unit extracts first position information of the observation target region from the obtained first image, the control unit controls the charged particle microscope under a second condition to acquire a second image of the observation target region of the sample, and the arithmetic unit performs an image quality conversion process to match the image quality of the acquired second image with the image quality of the first image using the image quality conversion process parameters to process the second image subjected to the image quality conversion process.

Defect observation device

A sample observation device images a sample placed on a movable table by irradiating and scanning the sample with a charged particle beam of a microscope. A degraded image having poor image quality and a high quality image having satisfactory image quality which are acquired at the same location of the sample by causing the charged particle microscope to change an imaging condition for imaging the sample are stored. An estimation process parameter is calculated for estimating the high quality image from the degraded image by using the stored degraded image and high quality image. A high quality image estimation unit processes the degraded image obtained by causing the charged particle microscope to image the desired site of the sample by using the calculated estimation process parameter. Thereby, the high quality image obtained at the desired site is estimated, and then the estimated high quality image is output.

Sample observation device and sample observation method

A defect observation device comprising: a defect determination coordinate creation unit by which the coordinates of a plurality of second defect candidates are determined as overlapping defect candidate coordinates, the plurality of second defect candidates respectively having, in a plurality of second imaging visual field regions overlapping a first imaging visual field region, a circuit pattern which partly overlaps a circuit pattern in the first imaging visual field region, in which a first defect candidate for defect determination among a plurality of defect candidates of a sample is present; a pseudo-reference image generation unit which generates a pseudo-reference image including a circuit pattern of the first defect candidate by superimposing a plurality of images respectively captured at the plurality of overlapping defect candidate coordinates; and a defect determination unit which compares an image for defect determination captured at the coordinates of the first defect candidate with the pseudo-reference image to determine the presence or absence of a defect in the image for defect determination.

Defect observation device and defect observation method

A versatile template generation unit cuts a first region in which a similarity level to a template image is a first similarity level and a second region in which the similarity level to the template image is a second similarity level different from the first similarity level, from an input image including an alignment mark, to generate a versatile template image.

Wafer observation device

In a device for observing a semiconductor wafer, a positional relationship between an in-wafer region and a background region in an imaging field of view is not constant when an outer peripheral portion of the wafer is imaged, which results in an increase in the quantity of calculation in defect detection and image classification processing and makes it difficult to efficiently perform defect observation and analysis. There is provided a defect observation system for a semiconductor wafer, and the system includes: a stage on which the semiconductor wafer is placed and which is movable in an XY direction, an imaging unit that is configured to image a portion including an edge of the semiconductor wafer, and an image output unit that is configured to, with respect to a plurality of images obtained by imaging, output images in which edges of the wafer are substantially in parallel among the plurality of images.

Defect observation system and defect observation method for semiconductor wafer

Provided is a defect classification apparatus classifying images of defects of a sample included in images obtained by capturing the sample, the apparatus including an image storage unit for storing the images of the sample acquired by an external image acquisition unit, a defect class storage unit for storing types of defects included in the images of the sample, an image processing unit for extracting images of defects from the images from the sample, processing the extracted images of defects and generating a plurality of defect images, a classifier learning unit for learning a defect classifier using the images of defects of the sample extracted by the image processing unit and data of the plurality of generated defect images, and a defect classification unit for processing the images of the sample by using the classifier learned by the classifier learning unit, to classify the images of defects of the sample.

Defect classification apparatus and defect classification method

A defect observation device detects a defect with high accuracy regardless of a defect size. One imaging configuration for observing an observation target on a sample is selected from an optical microscope, an optical microscope, and an electron microscope, and an imaging condition of the selected imaging configuration is controlled.

To quantify the degree of a defect, and provide information useful for yield management. Disclosed is a defect quantification method wherein: a defect image is classified; a measurement region and a measurement area are set to each of the defect image and a reference image on the basis of defect image classification results, said reference image corresponding to the defect image; and an evaluation value of a defect is calculated using each of the measurement values obtained from each of the measurement areas of the defect image and the reference image, and the defect is quantified.

Defect quantification method, defect quantification device, and defect evaluation value display device

An inspection method uses a charged particle microscope to observe a sample and view a defect site or a circuit pattern. A plurality of images is detected by a plurality of detectors and a mixed image is generated by automatically adjusting and mixing weighting factors required when the plurality of images are synthesized with each other. The sample is irradiated and scanned with a charged particle beam so that the plurality of detectors arranged at different positions from the sample detects a secondary electron or a reflected electron generated from the sample. The mixed image is generated by mixing the plurality of images of the sample with each other for each of the plurality of detectors, which are obtained by causing each of the plurality of detectors arranged at the different positions to detect the secondary electron or the reflected electron. The generated mixed image is displayed on a screen.

Sample observation method and sample observation device

Provided is a GUI including: an unadded pane region that hierarchically displays folders which are sets of images having no class information added thereto; an image pane region that displays the images displayed in the unadded pane region, the displayed images having no classification added thereto; and a class pane region that displays images having classification added thereto, wherein by externally inputting class information for one image having the class information added thereto, the input class information is displayed.

In a scheme for analyzing low magnification defect images and determining whether or not a defect detection method using cell comparison is applicable, if a defect detection method using cell comparison cannot be applied and the proportion transitioning to a defect detection method using die comparison increases, throughput may decrease even more than starting out with defect detection by a defect detection method using die comparison. The purpose of present invention is to carry out high precision defect detection with a stable throughput. In the present invention, the defect detection processing mode applied for detecting defects from the defect image is determined using a reference image, and defects are detected from the defect image by the defect detection processing mode that has been determined.

Growing community of inventors

Tokyo, Japan

Takehiro Hirai