Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of inspecting a substrate for a defect on a basis of a substrate image imaged by an imaging apparatus, includes: imaging a substrate being an imaging object under a predetermined imaging condition; extracting a mode of pixel values of the imaged substrate image; calculating a correction value for the imaging condition on a basis of the extracted pixel value and preset imaging condition correction data; then changing the imaging condition on a basis of the correction value, and imaging again the substrate being the imaging object under the changed imaging condition; and inspecting the substrate for a defect on a basis of a substrate image imaged under the changed imaging condition.