Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

Abstract: Methods and systems for implementing artificial intelligence enabled metrology are disclosed. An example method includes segmenting a first image of structure into one or more classes to form an at least partially segmented image, associating at least one class of the at least partially segmented image with a second image, and performing metrology on the second image based on the association with at least one class of the at least partially segmented image.

Abstract: Methods and apparatuses are disclosed herein for parameter estimation for metrology. An example method at least includes optimizing, using a parameter estimation network, a parameter set to fit a feature in an image based on one or more models of the feature, the parameter set defining the one or more models, and providing metrology data of the feature in the image based on the optimized parameter set.

Abstract: Apparatuses and methods directed toward endpoint detection are disclosed herein. An example method at least includes forming a plurality of lines on a top surface of a sample; removing, a plurality of times, material from a working surface of the sample, the working surface different than the top surface; imaging, a plurality of times, the sample to at least capture the plurality of lines; and determining an endpoint based on a relative spatial characteristic between two or more lines of the plurality of lines.

Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

Abstract: Apparatuses and methods directed toward endpoint detection are disclosed herein. An example method at least includes forming a plurality of lines on a top surface of a sample; removing, a plurality of times, material from a working surface of the sample, the working surface different than the top surface; imaging, a plurality of times, the sample to at least capture the plurality of lines; and determining an endpoint based on a relative spatial characteristic between two or more lines of the plurality of lines.

Abstract: Methods and systems for allowing users operators to quickly and easily (i) review the products of machine learning algorithm(s) to evaluate their accuracy, (ii) make corrections to such products, and (iii) compile feedback for retraining the algorithm(s) are disclosed. An example method includes acquiring a plurality of correlated images of a sample, determining one or more features in each image of the plurality of correlated images, and then determining a relationship between at least a first feature in a first image of the plurality of correlated images and at least a second feature in a second image of the plurality of images.

Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

Abstract: An autoclaving microplate washing system for cells and non-adhering three-dimensional (3D) cell cultures includes one or more peristaltic pumps for controlling the dispensing of washing fluid and the evacuation of fluid from microwells to gently wash the cells.

Abstract: An autoclaving microplate washing system for cells and non-adhering three-dimensional (3D) cell cultures includes one or more peristaltic pumps for controlling the dispensing of washing fluid and the evacuation of fluid from microwells to gently wash the cells.

Abstract: Methods and systems for implementing artificial intelligence to determine the pose of a sample within a microscope system and aligning said sample are disclosed. An example method includes receiving an image of the sample in the microscope apparatus, accessing a template associated with the sample. The template describes a plurality of template key points of the template version of the sample. A plurality of key points on the sample are then determined, where each of the key points on the sample corresponds to a corresponding template key point of a sample template, and the key points are subsequently used to determine a transformation between the sample as depicted in the image and the template version of the sample as described in the template. The transformation can then be used to automate the alignment of the sample within the microscope.

Abstract: Methods and systems for implementing artificial intelligence enabled preparation end-pointing are disclosed. An example method at least includes obtaining an image of a surface of a sample, the sample including a plurality of features, analyzing the image to determine whether an end point has been reached, the end point based on a feature of interest out of the plurality of features observable in the image, and based on the end point not being reached, removing a layer of material from the surface of the sample.

Abstract: Methods and apparatuses are disclosed herein for parameter estimation for metrology. An example method at least includes optimizing, using a parameter estimation network, a parameter set to fit a feature in an image based on one or more models of the feature, the parameter set defining the one or more models, and providing metrology data of the feature in the image based on the optimized parameter set.

Abstract: Methods and systems for implementing artificial intelligence enabled metrology are disclosed. An example method includes segmenting a first image of structure into one or more classes to form an at least partially segmented image, associating at least one class of the at least partially segmented image with a second image, and performing metrology on the second image based on the association with at least one class of the at least partially segmented image.

Abstract: Charging areas in electron microscopy are identified by comparing images obtained in different frames. A difference image or one or more optical flow parameters can be used for the comparison. If charging is detected, electron dose is adjusted, typically just in specimen areas associated with charging. Dose is conveniently adjusted by adjusting electron beam dwell time. Upon adjustment, a final image is obtained, with charging effects eliminated or reduced.

Abstract: An electrophotographic imager includes a photoconductive element, a charger, and a light source to expose areas of a charged surface of the photoconductive element to form a latent image. A development element is coupled relative to the photoconductive element to develop, via charged marking agent, the latent image on the photoconductive element. An exposure adjustment factor is selectively applied, prior to the exposing, to a first printable area of the latent image.

Abstract: Charging areas in electron microscopy are identified by comparing images obtained in different frames. A difference image or one or more optical flow parameters can be used for the comparison. If charging is detected, electron dose is adjusted, typically just in specimen areas associated with charging. Dose is conveniently adjusted by adjusting electron beam dwell time. Upon adjustment, a final image is obtained, with charging effects eliminated or reduced.

Abstract: An electrophotographic imager includes a photoconductive element, a charger, and a light source to expose areas of a charged surface of the photoconductive element to form a latent image. A development element is coupled relative to the photoconductive element to develop, via charged marking agent, the latent image on the photoconductive element. An exposure adjustment factor is selectively applied, prior to the exposing, to a first printable area of the latent image.

Abstract: An autoclaving microplate washing system for cells and non-adhering three-dimensional (3D) cell cultures includes one or more peristaltic pumps for controlling the dispensing of washing fluid and the evacuation of fluid from microwells, and means for independently controlling the relative positions between microwells of the microplate and cleaning dispense pipes and evacuation aspiration pipes.