Abstract: An object of the present invention is to achieve both suppression of data amount of an image processing system that learns a collation image to be used for image identification using a discriminator and improvement of identification performance of the discriminator. In order to achieve the above object, there is proposed an image processing system including a discriminator that identifies an image using a collation image, the image processing system further including a machine learning engine that performs machine learning of collation image data required for image identification. The machine learning engine searches for a successfully identified image using an image for which identification has been failed, and adds information, obtained based on a partial image of the image for which identification has been failed and which has been selected by an input device to the successfully identified image obtained by the search to generate corrected collation image data.

Abstract: An image quality improvement system includes: an image quality improvement unit that improves the image quality of a low quality image; a deformation prediction unit that predicts a deformation amount that has occurred between a first low quality image and a different second low quality image, included in a series of input low quality images; and a deformation correction unit that corrects, based on the deformation amount predicted by the deformation prediction unit, one of a first prediction image obtained by applying processing by the image quality improvement unit to the first low quality image, the second low quality image, or a second prediction image obtained by applying processing by the image quality improvement unit to the second low quality image. The image quality improvement system learns to reduce the evaluation of a loss function between the first prediction and the second low quality image or the second prediction image.

Abstract: An image processing method whereby data pertaining to an estimated captured image obtained from reference data of a sample is acquired using an input acceptance unit, an estimation unit, and an output unit. The data is used when comparing the estimated image and an actual image of the sample, wherein the method includes: an input acceptance unit accepting input of the reference data, process information pertaining to the sample, and trained model data; the estimation unit using the reference data, the process information, and the model data to calculate captured image statistics representing a probabilistic distribution of values attained by the data of the captured image; and the output unit outputting the captured image statistics, and generating the estimated captured image from the captured image statistics. This permits reducing the time required for estimation and to perform comparison in real time.

Abstract: The disclosure relates to a dimension measurement apparatus that reduces time required for dimension measurement and eliminates errors caused by an operator. Therefore, the dimension measurement apparatus uses a first image recognition model that extracts a boundary line between a processed structure and a background over the entire cross-sectional image and/or a boundary line of an interface between different kinds of materials, and a second image recognition that output information for dividing the boundary line extending over the entire cross-sectional image obtained from the first image recognition model for each unit pattern constituting a repetitive pattern, obtains coordinates of a plurality of feature points defined in advance for each unit pattern, and measures a dimension defined as a distance between two predetermined points of the plurality of feature points.

Abstract: A pattern inspection system inspects an image of an inspection target pattern of an electronic device using an identifier constituted by machine learning, based on the image of the inspection target pattern of the electronic device and data used to manufacture the inspection target pattern. The system includes a storage unit which stores a plurality of pattern images of the electronic device and pattern data used to manufacture a pattern of the electronic device, and an image selection unit which selects a learning pattern image used in the machine learning from the plurality of pattern images, based on the pattern data and the pattern image stored in the storage unit.

Abstract: A method, an apparatus, and a program for more appropriately determining a condition for appropriately recognizing a semiconductor pattern are provided. A method for determining a condition related to a captured image of a charged particle beam apparatus including: acquiring, by a processor, a plurality of captured images, each of the captured images being an image generated by irradiating a pattern formed on a wafer with a charged particle beam, and detecting electrons emitted from the pattern, each of the captured images being an image captured according to one or more imaging conditions, the method further including: acquiring teaching information for each of the captured images; acquiring, by the processor, one or more feature determination conditions; calculating, by the processor, a feature for each of the captured images based on each of the feature determination conditions, at least one of the imaging condition and the feature determination condition being plural.

Abstract: A learning processing device and method achieves learning of a lightweight model that is completed in a short amount of time. The learning processing device obtains a new, second learning model from an existing first learning model. An input unit acquires a first learning model generated in advance by learning a first learning data set, and an unpruned neural network (hereinafter, NN). An important parameter identification unit uses the first learning model and the NN to initialize a NN to be learned, and uses a second learning data set and the initialized NN to identify a degree of importance of parameters in a recognition process of the initialized NN. A new model generation unit carries out a pruning process for deleting parameters which are not important from the initialized NN, thereby generating a second NN; and a learning unit uses the second learning data set to learn the second NN.

Abstract: This disclosure relates to a system for performing efficient learning of a specific portion. To achieve this purpose, there is proposed a system configured to generate a converted image on the basis of input of an input image, the system comprising a learning model in which parameters are adjusted so as to suppress an error between the input image and a second image converted upon input of the input image, the learning model being subjected to different learning at least between a first area in the image and a second area different from the first area.

Abstract: According to the present invention, an image recognition system calculates importance of a feature for each target shape recognized in an image and for each type of feature, and determines correctness of a recognition result by comparing the importance with a statistic for each type of feature, for each target shape.

Abstract: The purpose of the present invention is to provide a computer program for achieving die-to-database inspection at high speed and with few false reports, and a semiconductor inspection device using the same. To achieve this purpose, the present invention proposes: a computer program comprising an encoder layer that is configured to determine the features of a design data image, and a decoder layer that is configured to generate, on the basis of a variation in an image (inspection target image) obtained by photographing an inspection target pattern, a statistic pertaining to the brightness values of pixels from feature values output by the encoder layer, wherein die-to-database inspection with few false reports can be achieved by comparing the inspection target image and the statistic obtained from the decoder layer and pertaining to the brightness values, and thereby detecting a defect region in the image; and a semiconductor inspection device using the same.

Abstract: The disclosure relates to a dimension measurement apparatus that reduces time required for dimension measurement and eliminates errors caused by an operator. Therefore, the dimension measurement apparatus uses a first image recognition model that extracts a boundary line between a processed structure and a background over the entire cross-sectional image and/or a boundary line of an interface between different kinds of materials, and a second image recognition that output information for dividing the boundary line extending over the entire cross-sectional image obtained from the first image recognition model for each unit pattern constituting a repetitive pattern, obtains coordinates of a plurality of feature points defined in advance for each unit pattern, and measures a dimension defined as a distance between two predetermined points of the plurality of feature points.

Abstract: The present disclosure relates to a system and a non-transitory computer-readable medium for estimating the height of foreign matter, etc. adhering to a sample. In order to achieve the abovementioned purpose, proposed is a system, etc. in which data acquired by a charged particle beam device or features extracted from the data are input to a learning model, which is provided with, in an intermediate layer thereof, a parameter learned using teacher data having data acquired by the charged particle beam device or features extracted from the data as inputs and having the heights or depths of the structures of samples or of foreign matter on the samples as outputs, and height or depth information is output.

Abstract: The purpose of the present invention is to provide an image evaluation device and method which can detect unknown defects and which can prevent misrecognition by a machine learning model. This image evaluation device, which uses a machine learning classifier to classify defect information in a defect image of an electronic device, is characterized by being provided with: an image storage unit which stores a defect image of an electronic device; a defect region storage unit which stores defect region information that is in the defect image; a classifier which classifies the defect information with machine learning; an image extraction unit which, in the course of the defect image classification processing, extracts image-of-interest information which the classifier will focus on; and an evaluation unit which compares the image-of-interest information and the defect region information to evaluate the classifiability of the defect image.

Abstract: An object of the present invention is to achieve both suppression of data amount of an image processing system that learns a collation image to be used for image identification using a discriminator and improvement of identification performance of the discriminator. In order to achieve the above object, there is proposed an image processing system including a discriminator that identifies an image using a collation image, the image processing system further including a machine learning engine that performs machine learning of collation image data required for image identification. The machine learning engine searches for a successfully identified image using an image for which identification has been failed, and adds information, obtained based on a partial image of the image for which identification has been failed and which has been selected by an input device to the successfully identified image obtained by the search to generate corrected collation image data.

Abstract: A pattern inspection system inspects an image of an inspection target pattern of an electronic device using an identifier constituted by machine learning, based on the image of the inspection target pattern of the electronic device and data used to manufacture the inspection target pattern. The system includes a storage unit which stores a plurality of pattern images of the electronic device and pattern data used to manufacture a pattern of the electronic device, and an image selection unit which selects a learning pattern image used in the machine learning from the plurality of pattern images, based on the pattern data and the pattern image stored in the storage unit.

Abstract: In order to select an optimal learning model for an image when inference is carried out in the extraction of a profile line using machine learning, without requiring a correct value or degree of certainty, a feature extraction learning model group containing a plurality of learning models is used for feature extraction. A recall learning model group containing recall learning models is paired with the feature extraction learning models. A feature amount extraction unit for referencing a feature extraction learning model and extracting a feature amount from input data; a data-to-data recall unit for referencing a recall learning model and outputting a recall result with the feature amount subjected to dimensional compression; and a learning model selection unit for selecting a feature extraction learning model from the feature extraction learning model group under the condition that the difference between the feature amount and the recall result is minimized are provided.

Abstract: An object of the present invention is to achieve both suppression of data amount of an image processing system that learns a collation image to be used for image identification using a discriminator and improvement of identification performance of the discriminator. In order to achieve the above object, there is proposed an image processing system including a discriminator that identifies an image using a collation image, the image processing system further including a machine learning engine that performs machine learning of collation image data required for image identification. The machine learning engine searches for a successfully identified image using an image for which identification has been failed, and adds information, obtained based on a partial image of the image for which identification has been failed and which has been selected by an input device to the successfully identified image obtained by the search to generate corrected collation image data.

Abstract: A pattern inspection system inspects an image of an inspection target pattern of an electronic device using an identifier constituted by machine learning, based on the image of the inspection target pattern of the electronic device and data used to manufacture the inspection target pattern. The system includes a storage unit which stores a plurality of pattern images of the electronic device and pattern data used to manufacture a pattern of the electronic device, and an image selection unit which selects a learning pattern image used in the machine learning from the plurality of pattern images, based on the pattern data and the pattern image stored in the storage unit.

Abstract: A money handling machine includes: a storage assembly including a storage unit for storing money and feeding out the stored money; a collection unit configured to store the money fed out from the storage unit; a replenishing unit configured to store money with which an external device is replenished; and a control unit configured to manage the money stored in the storage unit of the storage assembly under a first management authority, and manage money stored in the collection unit and at least a part of money stored in the replenishing unit under a second management authority.

Abstract: 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.