Tokyo Electron Limited

Shuji Iwanaga

Hiroshi Tomita

Tadashi Nishiyama

Nobuyuki Sata

Izumi Hasegawa

Shinobu Miyazaki

Satoshi Morita

Takeshi Tamura

Kazuya Hisano

Kouji Okamura

Motoi Okada

Kenji Nakamizo

Shinichiro Araki

Shinji Koga

Yasutoshi Umehara

Shuhei Matsumoto

Kanzou Katou

Akinori Miyahara

A substrate inspection apparatus for inspecting a substrate, includes: an acquisition part configured to acquire an estimated image of an inspection target substrate after a process by a substrate processing apparatus, based on an image estimation model created by a machine learning by using a captured image before the process by the substrate processing apparatus and a captured image after the process by the substrate processing apparatus for each of a plurality of substrates, and a captured image of the inspection target substrate before the process by the substrate processing apparatus; and a determination part configured to determine the presence or absence of a defect in the inspection target substrate, based on a captured image of the inspection target substrate and the estimated image of the inspection target substrate after the process by the substrate processing apparatus.

Substrate inspection device, substrate inspection system, and substrate inspection method

An apparatus for classifying a defect generated in a substrate, includes: a first storage part for storing a first image data for defect classification determination, which includes a defect region in which the defect is generated and a surrounding region of the defect region; a first estimation part for estimating a first type of defect by using a deep learning system, based on the first image data; a second storage part for storing a second image data for defect classification estimation, which is obtained by expressing the defect region and the surrounding region by a binarized data; a second estimation part for estimating a second type of defect by using a rule-based system, based on an attribute of the defect region extracted from the second image data; and a comprehensive determination part for comprehensively determining a type of defect based on the first and second types of defects.

Substrate defect inspection apparatus, substrate defect inspection method, and storage medium

Film thickness measured values obtained by measurement in advance at a plurality of points on a measurement preparation substrate and coordinates corresponding to the film thickness measured values are acquired. A pixel value at each coordinates is extracted from a preparation imaged image obtained by imaging the measurement preparation substrate in advance by an imaging device. Correlation data between the pixel value extracted at each coordinates and the film thickness measured value at each coordinates is generated. A substrate being a film thickness measurement object is imaged by the imaging device to acquire an imaged image, and a film thickness of a film formed on the substrate being the film thickness measurement object is calculated on the basis of a pixel value of the imaged image and the correlation data.

Film thickness measurement apparatus, film thickness measurement method, and non-transitory computer storage medium

A potential trouble can be in advance suppressed by analyzing a defect of a wafer. A defect analyzing apparatus of analyzing a defect of a substrate includes an imaging unit configured to image target substrates; a defect feature value extracting unit configured to extract a defect feature value in a surface of the substrate based on the substrate image; a defect feature value accumulating unit configured to calculate an accumulated defect feature value with respect to the substrates to create an accumulation data AH; a defect determination unit configured to determine whether the accumulated defect feature value exceeds a preset critical value; and an output display unit configured to output a determination result from the defect determination unit.

Defect analyzing apparatus, substrate processing system, defect analyzing method and computer-readable storage medium

A method of setting an exposure apparatus to expose exposure sectors defined on a resist film formed on a surface of a substrate with proper values of an exposure amount and a focus value for forming a pattern having a predetermined dimension includes exposing and developing an exposure sector defined on a reference substrate by a first exposure apparatus having a first state, and imaging the same. The method exposes and develops exposure sectors defined on an inspection substrate by a second exposure apparatus having a second state where at least one of the exposure amount and the focus value is unknown, and forms and images a pattern on the inspection substrate. The method determines the proper values for the exposure amount and the focus value for the second state based on luminance of the exposure sector of reference data and luminances of the exposure sectors of inspection data.

Setting method of exposure apparatus, substrate imaging apparatus and non-transitory computer-readable storage medium

In the present invention, a planar distribution of pixel values in a picked-up substrate image is decomposed into a plurality of pixel value distribution components through use of a Zernike polynomial for each of substrate images; Zernike coefficients of the pixel value distribution components decomposed through use of the Zernike polynomial are calculated; a median value and values deviated from the median value by a predetermined value or more are extracted for every Zernike coefficients having a same couple of degrees from the calculated Zernike coefficients; substrate images having the extracted values are specified; and a substrate image being a defect inspection reference is created by combining the specified substrate images.

Substrate reference image creation method, substrate defect inspection method, substrate reference image creation apparatus, substrate defect inspection unit and non-transitory computer storage medium

According to an embodiment of the present disclosure, an apparatus of inspecting an overlapped substrate obtained by bonding substrates together is provided. The apparatus includes a first holding unit configured to hold and rotate the overlapped substrate, and a displacement gauge configured to measure displacements of peripheral sides of a first substrate and a second substrate constituting the overlapped substrate while rotating the overlapped substrate held by the first holding unit.

Inspection device, bonding system and inspection method

Disclosed is a liquid processing apparatus capable of accurately determining a holding state of a substrate without being influenced by, for example, material or surface condition of a substrate. The liquid processing apparatus includes a substrate holding unit that holds a substrate, a camera that photographs a region where a peripheral edge portion of substrate is present when substrate is properly held by the substrate holding unit, and a control unit that determines a holding state of the substrate held by the substrate holding unit based on an image photographed by the camera.

Substrate processing apparatus, substrate processing method, and computer readable storage medium storing substrate processing program

Provide is a process monitoring device in a semiconductor manufacturing apparatus that can readily and reliably monitor the process in the semiconductor manufacturing apparatus. The process monitoring device includes a storage unit that stores a normal state moving image data indicating a normal state of the process; an image capturing unit that captures an image of a state of the process to be monitored to acquire a moving image data; an abnormality level calculation unit configured to extract a feature amount for each frame of the moving image data and the normal state moving image data, and calculate an abnormality level based on the extracted feature amount; and a display unit that displays the abnormality level calculated by the abnormality level calculation unit in association with a frame position of the moving image data.

Process monitoring device and process monitoring method in semiconductor manufacturing apparatus and semiconductor manufacturing apparatus

An image processing method of picking up an image of a substrate and converting pixel values of the picked-up substrate image makes the pixel values of the picked-up substrate image into a histogram, and creates a tone curve T composed of a periodic function of a predetermined amplitude and a predetermined period based on a distribution of the pixel values in the histogram. The pixel values of the picked-up substrate image are converted using the tone curve T to obtain a substrate image with a high contrast.

Image processing method, image display method, image processing apparatus and a non-transitory computer-readable recording medium

An object of the present invention is to perform temperature setting of a heating plate so that a wafer is uniformly heated in an actual heat processing time. The temperature of a wafer is measured during a heat processing period from immediately after a temperature measuring wafer is mounted on the heating plate to the time when the actual heat processing time elapses. Whether the uniformity in temperature within the wafer is allowable or not is determined from the temperature of the wafer in the heat processing period, and if the determination result is negative, a correction value for a temperature setting parameter of the heating plate is calculated using a correction value calculation model from the measurement result, and the temperature setting parameter is changed.

Temperature setting method of heat processing plate, temperature setting apparatus of heat processing plate, program, and computer-readable recording medium recording program thereon

A defect classification apparatus of the present invention includes a design unit and a diagnosis unit. In the design unit, a model creation unit combines a defect template in a template storage unit with a teaching image to create a defect model, and a classification class setting unit calculates feature amounts of a defect in the defect model and sets a classification class of the defect. The relation between the feature amounts of the defect and the classification class is stored in a storage unit. In the diagnosis unit, a feature amount calculation unit calculates feature amounts of defects from a captured inspection object image of the substrate, and a classification unit classifies the defects of the substrate into classification classes from the relations between the feature amounts of the defects and the classification classes in the storage unit based on the calculated feature amounts of the defects.

Defect classification method, computer storage medium, and defect classification apparatus

There is provided a database storing reference data including a plurality of reference image data, which are obtained by imaging reference substrates, respectively, wherein each of the reference substrates lacks only one of the films of different kinds but includes remainder of the films of different kinds, and wherein in the reference substrates the lacking films are different from each other, and wherein the plurality of reference image data is classified into categories according to the kinds of the films. Difference degrees between color information of a defect area extracted from an image data of an inspection target substrate and color information of corresponding areas of the reference substrates are calculated. Based on the difference degree, the defective film is identified.

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Koshi, Japan

Shuji Iwanaga