Abstract: This application discloses an image encoder determination method performed by a computer device. The method includes: inputting, for a first sample image and a second sample image of a first object under different lighting parameters, the first sample image into an image encoder in an initial reconstruction model for image encoding, and outputting first image patch codes respectively corresponding to a plurality of first image patches; inputting the plurality of first image patch codes into a reconstruction network in the initial reconstruction model, and performing code prediction on a plurality of second image patches in the second sample image to output a plurality of first predicted codes; and performing model training on the initial reconstruction model with reference to a plurality of second image patch codes obtained by inputting the plurality of second image patches into a pre-trained encoder and a loss function, to obtain a first reconstruction model.

Abstract: A data detection method includes obtaining an image sample, extracting sample features from the image sample, determining a target sample feature with a maximum nearest-neighbor feature distance, determining, based on the nearest-neighbor feature distances of the sample features, (N+1) sample feature categories corresponding to the sample features, N being a positive integer, and training an initial feature classification model based on the (N+1) sample feature categories and the sample features to obtain a trained feature classification model. The nearest-neighbor feature distance of one sample feature is a minimum value of feature distances between the one sample feature and other ones of the plurality of sample features except the one sample feature. The (N+1) sample feature categories include N first feature categories indicating detected data being normal data and a second feature category indicating to perform secondary detection on the detected data.

Abstract: This application discloses an image processing method and apparatus, and a storage medium. The method includes receiving a line scan image in line scan data, the line scan image comprising a plurality of image blocks; obtaining a first sampled image and a second sampled image corresponding to each image block, inputting the first sampled image and the second sampled image into a preset backbone network, to obtain a first image feature corresponding to the first sampled image and a second image feature corresponding to the second sampled image, mapping the first image feature to a feature space of the second image feature to obtain a mapping feature; performing self-supervised training on the preset backbone network, and determining a to-be-processed image feature of a to-be-processed image through the target backbone network.

Abstract: A reaction vessel pickup mechanism, an automatic loading device, a sample analyzer, and a loading method are disclosed. Driven by a driving structure, each of the pickup blocks moves in a first preset time or a preset distance and then stops or shakes, so that a reaction vessel is dropped from the pickup blocks more easily.

Abstract: A reaction vessel pickup mechanism, an automatic loading device, a sample analyzer, and a loading method are disclosed. Driven by a driving structure, each of the pickup blocks moves in a first preset time or a preset distance and then stops or shakes, so that a reaction vessel is dropped from the pickup blocks more easily.

Abstract: According to one embodiment of the invention, a method for enhancing multiple feature lithography is provided. The method includes generating a plurality of maps each associated with a particular one of a plurality of circuit features. Each map maps an illumination field comprising a plurality of point sources and indicates, in terms of a process metric and for each point source, a level of feature quality that will result from using the each point source to image the each circuit feature. The method also includes identifying, based on the maps, a group of one or more of the point sources that, if used to image the circuit features onto a target surface, will result in an overall feature quality level equal to or greater than a predetermined quality threshold.

Abstract: According to one embodiment of the invention, a method for enhancing multiple feature lithography is provided. The method includes generating a plurality of maps each associated with a particular one of a plurality of circuit features. Each map maps an illumination field comprising a plurality of point sources and indicates, in terms of a process metric and for each point source, a level of feature quality that will result from using the each point source to image the each circuit feature. The method also includes identifying, based on the maps, a group of one or more of the point sources that, if used to image the circuit features onto a target surface, will result in an overall feature quality level equal to or greater than a predetermined quality threshold.

Abstract: The invention provides a machine for washing a mop, which includes a housing, a driving mechanism, a controlling mechanism, a pulsator, a water tank, a water removal barrel, a draining mechanism, and a mop supporting means, wherein an integrated device of the pulsator and the water removal barrel consists of the water removal barrel which is a cylinder without bottoms and the pulsator which is fixed together with the lower end of the barrel. The mop supporting means is a protrusion supporting element which is on the upper end of the center of the pulsator.

Abstract: Characterizing an exposure tool involves receiving data describing a pattern formed at a wafer. The pattern is formed by illuminating the wafer using an exposure tool, and the data has a scan direction and slit direction. An image field is mapped according to the data to determine an image field error of the data, and the image field error is separated from the data to reduce variation of the data in the scan direction. The data is reduced to the slit direction. Errors associated with the exposure tool are determined from the data in order to characterize the exposure tool.

Abstract: Characterizing an exposure tool involves receiving data describing a pattern formed at a wafer. The pattern is formed by illuminating the wafer using an exposure tool, and the data has a scan direction and slit direction. An image field is mapped according to the data to determine an image field error of the data, and the image field error is separated from the data to reduce variation of the data in the scan direction. The data is reduced to the slit direction. Errors associated with the exposure tool are determined from the data in order to characterize the exposure tool.