Abstract: An apparatus includes: a first pixel for acquiring an image, the first pixel including a first conversion element and a first thin-film transistor and connected to a first signal line; and a second pixel for correcting an output of the first pixel, the second pixel including an element and a second thin-film transistor and connected to a second signal line, in which a ratio between a plurality of capacitances related to the first signal line and a ratio between a plurality of capacitances related to the second signal line are approximately equivalent.

Abstract: In one aspect, a process operation is conducted at a first pressure in a process chamber, and an epitaxial deposition operation is conducted at an atmospheric pressure in an epitaxial deposition chamber. The atmospheric pressure is greater than the first pressure. The process chamber is mounted to a first mainframe that operates at the first pressure (a reduced pressure), and the epitaxial deposition chamber is mounted to a second mainframe that operates at the atmospheric chamber. In one aspect, the process chamber is a cleaning chamber (such as a pre-clean chamber) and the process operation is a cleaning operation. In one aspect, the process chamber is an atmospheric pressure epitaxial deposition chamber and the process operation is an atmospheric pressure epitaxial deposition operation.

Abstract: A method of moving a susceptor in a processing system, suitable for use in semiconductor processing, is provided. The method includes: moving a first susceptor from an interior volume of a first enclosure to an interior volume of a process chamber during a first time period; and positioning, during a second time period, a first substrate on the first susceptor when the first susceptor is in the process chamber, wherein the interior volume of the first enclosure and interior volume of the process chamber are maintained at a non-atmospheric pressure from the beginning of the first time period until the end of the second time period.

Abstract: A delivery server includes a processor including hardware, the processor being configured to: transmit arrival information of a product to a user terminal carried by a user who has ordered the product; transmit, in a case where unattended delivery is instructed by the user terminal, unattended delivery information to a home delivery terminal of a delivery person who delivers the product, and request a signature of the user; transmit the signature information to the home delivery terminal in response to receiving signature information regarding the signature of the user from the user terminal; and transmit delivery completion information of the product to the user terminal.

Abstract: A susceptor for use in a processing chamber for supporting a wafer includes a susceptor substrate having a susceptor ledge on an outer circumferential edge of a front side of the susceptor substrate, wherein a pocket within the susceptor ledge is configured to hold a wafer to be processed in a processing chamber, and a coating layer deposited on the susceptor substrate, wherein a surface of the susceptor ledge is textured with a plurality of venting groove lines, a surface of the pocket is textured with a first pattern, and a surface of a back side of the susceptor substrate opposite the front side is textured with a second pattern.

Abstract: A substrate processing system is disclosed which includes a processing chamber comprising a susceptor having a first surface and a second surface opposite to the first surface, a groove formed in the first surface adjacent to a perimeter thereof, and a substrate support structure including a plurality of carrier lift pins, each of the plurality of carrier lift pins movably disposed in an opening formed from the second surface to the first surface, wherein the opening is recessed from the groove.

Abstract: A plasma processing system for cleaning a substrate is provided. The plasma processing system includes a process chamber that includes: a chamber body enclosing an interior volume; and a substrate support disposed in the interior volume. The plasma processing system includes a vacuum pump; a first exhaust line fluidly coupled between the interior volume of the process chamber and the vacuum pump; and a second exhaust line fluidly coupled between the interior volume of the process chamber and the vacuum pump. The first exhaust line and the second exhaust line are arranged to provide alternative paths for the exhaust between the interior volume and the vacuum pump, and the first exhaust line has an internal diameter that is at least 50% smaller than the internal diameter of the second exhaust line.

Abstract: The information processing apparatus includes: a management unit that stores a correspondence relationship between a training method for a model and task information of the model; and a selection unit that selects an optimum training method for task information input from a predetermined device and outputs the optimum training method to the device. The management unit associates pieces of specification information necessary for implementing training methods with the training methods, respectively, and stores the pieces of specification information and the training methods. The selection unit selects an optimum training method within a range of a specification available for training in the device.

Abstract: A substrate processing system is disclosed which includes a processing chamber comprising a susceptor having a first surface and a second surface opposite to the first surface, a groove formed in the first surface adjacent to a perimeter thereof, and a substrate support structure including a plurality of carrier lift pins, each of the plurality of carrier lift pins movably disposed in an opening formed from the second surface to the first surface, wherein the opening is recessed from the groove.

Abstract: In one aspect, a process operation is conducted at a first pressure in a process chamber, and an epitaxial deposition operation is conducted at an atmospheric pressure in an epitaxial deposition chamber. The atmospheric pressure is greater than the first pressure. The process chamber is mounted to a first mainframe that operates at the first pressure (a reduced pressure), and the epitaxial deposition chamber is mounted to a second mainframe that operates at the atmospheric chamber. In one aspect, the process chamber is a cleaning chamber (such as a pre-clean chamber) and the process operation is a cleaning operation. In one aspect, the process chamber is an atmospheric pressure epitaxial deposition chamber and the process operation is an atmospheric pressure epitaxial deposition operation.

Abstract: A large amount of training data is typically required to perform deep network leaning, making it difficult to achieve using a few pieces of data. In order to solve this problem, the neural network device according to the present invention is provided with: a feature extraction unit which extracts features from training data using a learning neural network; an adversarial feature generation unit which generates an adversarial feature from the extracted features using the learning neural network; a pattern recognition unit which calculates a neural network recognition result using the training data and the adversarial feature; and a network learning unit which performs neural network learning so that the recognition result approaches a desired output.

Abstract: A neural network learning device 20 is equipped with: a determination module 22 that determines the size of a local region in learning information 200 which is to be learned by a neural network 21 containing multiple layers, said determination being made for each layer, on the basis of the structure of the neural network 21; and a control module 25 that, on the basis of size of the local region as determined by the determination module 22, extracts the local region from the learning information 200, and performs control such that the learning of the learning information represented by the extracted local region by the neural network 200 is carried out repeatedly while changing the size of the extracted local region, and thus, a reduction in the generalization performance of the neural network can be avoided even when there is little learning data.

Abstract: A workpiece gripping device (10) for securing a workpiece (90) on a table 114 of a machine tool (100) is provided with a workpiece gripping unit (20) having at least three claws (12) which are disposed at equal intervals around a central axis (Oc) to secure an outer peripheral part of the workpiece, a bar gripping member (38) which is mounted on the central axis (Oc), and grips a bar (260) inserted into the workpiece (90) along the central axis (Oc), and a retraction mechanism (64) for retracting the bar gripping member (38) along a central axis (O) toward a table side.

Abstract: A substrate processing system is disclosed which includes a processing chamber comprising a susceptor having a first surface and a second surface opposite to the first surface, a groove formed in the first surface adjacent to a perimeter thereof, and a substrate support structure including a plurality of carrier lift pins, each of the plurality of carrier lift pins movably disposed in an opening formed from the second surface to the first surface, wherein the opening is recessed from the groove.

Abstract: A substrate processing system is disclosed which includes a processing chamber comprising a susceptor having a first surface and a second surface opposite to the first surface, a groove formed in the first surface adjacent to a perimeter thereof, and a substrate support structure including a plurality of carrier lift pins, each of the plurality of carrier lift pins movably disposed in an opening formed from the second surface to the first surface, wherein the opening is recessed from the groove.

Abstract: A data transformation apparatus (1) includes: data transformation means (11) for performing data transformation on each of a plurality of data sets so that data distributions of the plurality of data sets are brought close to each other; first calculation means (12) for calculating a class classification loss from a result of class classification performed by class classification means on at least some of a plurality of first transformed data sets obtained after the data transformation; second calculation means (13) for calculating an upper bound and a lower bound of a domain classification loss from a result of domain classification performed by domain classification means on each of the plurality of first transformed data sets; and first learning means (14) for performing first learning by updating a parameter of the domain classification means so that the upper bound is reduced and updating a parameter of the data transformation means so that the class classification loss is reduced and the lower bound is

Abstract: Provided is a search system which is configured to search for a registered vector being similar to an input vector among a plurality of registered vectors, on the basis of a degree of similarity between an input vector and a registered vector. The search system includes a partial similarity calculation unit that calculates a degree of partial similarity which is the degree of similarity concerning some of one or more dimensions of the input vector and the registered vector, a limit calculation unit that calculates, on the basis of the degree of partial similarity, an upper limit of the degree of similarity that is expected when the degree of similarity is calculated, and a rejection decision unit that decides, on the basis of the upper limit of the degree of similarity, whether or not to reject the registered vector from a candidate for a search result.

Abstract: Provided is a search system which is configured to search for a registered vector being similar to an input vector among a plurality of registered vectors, on the basis of a degree of similarity between an input vector and a registered vector. The search system includes a partial similarity calculation unit that calculates a degree of partial similarity which is the degree of similarity concerning some of one or more dimensions of the input vector and the registered vector, a limit calculation unit that calculates, on the basis of the degree of partial similarity, an upper limit of the degree of similarity that is expected when the degree of similarity is calculated, and a rejection decision unit that decides, on the basis of the upper limit of the degree of similarity, whether or not to reject the registered vector from a candidate for a search result.

Abstract: A data conversion learning apparatus includes a data conversion unit that performs data conversion of source data and target data, a first deduction unit that deduces data of a non-appearing class on the basis of a domain certainty factor acquired by a domain identification using converted data, a second deduction unit that deduces data of a non-appearing class on the basis of a class certainty factor acquired by a class identification using converted data, a class identification learning unit that performs machine learning for class identification using the data of the non-appearing class deduced by the first deduction unit and the source data and the target data which are inputs, and a domain identification learning unit that performs machine learning for domain identification using the data of the non-appearing class deduced by the second deduction unit and the source data and the target data which are inputs.

Abstract: The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a clustering unit (2040), a transformation unit (2060) and modeling unit (2080). Until a predetermined termination condition is determined, the clustering unit (2040) repeatedly preforms: 1) optimizing the posterior parameters for clustering assignment for each data streams; 2) optimizes the posterior parameters for each determined cluster and for each time frame; 3) optimizes the posterior parameters for individual responses for each data stream; 4) optimizes the posterior parameters for latent states, via approximating the observation model through non-conjugate inference. The transformation unit (2060) transforms the latent states into parameters of the observation model, through a transformation function. The modeling unit (2060) generates the model data, which including all the optimized parameters of all the model latent variables, optimized inside the clustering unit (2040).