Abstract: A method identifies ring structures in pillars of high aspect ratio (HAR) structures. For segmentation of rings, a machine learning-logic is used. A two-step training method for the machine learning logic is described.

Abstract: The present invention relates to a method and an apparatus for determining at least one unknown effect of defects of an element of a photolithography process. The method comprises the steps of: (a) providing a model of machine learning for a relationship between an image, design data associated with the image and at least one effect of the defects of the element of the photolithography process arising from the image; (b) training the model of machine learning using a multiplicity of images used for training purposes, design data associated with the images used for training purposes and corresponding effects of the defects; and (c) determining the at least one unknown effect of the defects by applying the trained model to a measured image and the design data associated with the measured image.

Abstract: The invention relates to a method for registering structures on microlithographic masks comprising the comparison of a recorded measurement image of a mask and the target design underlying the mask, wherein the target design underlying the mask is converted into a simulated reference image that is directly comparable with the measurement image with the aid of an optical simulation, wherein the optical simulation is fully automatically differentiable in such a manner that a metric that is determined from the recorded measurement image and the reference image simulated in the forward mode and represents the differences allows in the backward mode a representation of the actual design of the mask that is directly comparable with the target design for the purpose of determining possible defects of the mask. The invention furthermore relates to a corresponding computer program product and to the use of the above method in the course of a microlithographic process.

Abstract: A method for training and using a machine learning system for a differentiation between healthy and diseased tissue during a microsurgical intervention is described. In this case, the method comprises: receiving training data and associated annotation data for training a machine learning system, training the machine learning system, which after training is configured for a prediction of a probability value and a prediction of a trustworthiness value, from which a control signal for a surgery assistance system is derivable, which is usable during a later application during a microsurgical operation, and storing parameter values of the trained machine learning model.

Abstract: The invention relates to techniques for material testing of optical test pieces, for example of lenses. Angle-variable illumination, using a suitable illumination module, and/or angle-variable detection are carried out in order to create a digital contrast. The digital contrast can be, for example, a digital phase contrast. A defect detection algorithm for automated material testing based on a result image with digital contrast can be used. For example, an artificial neural network can be used.

Abstract: The present invention relates to a method and an apparatus for determining at least one unknown effect of defects of an element of a photolithography process. The method comprises the steps of: (a) providing a model of machine learning for a relationship between an image, design data associated with the image and at least one effect of the defects of the element of the photolithography process arising from the image; (b) training the model of machine learning using a multiplicity of images used for training purposes, design data associated with the images used for training purposes and corresponding effects of the defects; and (c) determining the at least one unknown effect of the defects by applying the trained model to a measured image and the design data associated with the measured image.

Abstract: A method for additive manufacture of a workpiece includes obtaining a dataset defining the workpiece in multiple workpiece layers. The method includes producing a respective layer and capturing an image of the layer. The method includes feeding the image to a statistical learning model to determine a defect vector of defect probabilities each indicating whether a respective layer defect is present. The method includes, in response to no layer defects being present, selectively solidifying the layer. The method includes, in response to at least one defect being present, reworking or reproducing the layer and repeating the recording and the feeding to determine the defect vector again. The method includes repeating the producing, the recording, the feeding, and the selectively solidifying such that further layers are produced one on top of the other. The respective material layers each are inspected using the previously trained statistical learning model.

Abstract: A system and a method for measuring of parameter values of semiconductor objects within wafers with increased throughput include using a modified machine learning algorithm to extract measurement results from instances of semiconductor objects. A training method for training the modified machine learning algorithm includes reducing a user interaction. The method can be more flexible and robust and can involve less user interaction than conventional methods. The system and method can be used for quantitative metrology of integrated circuits within semiconductor wafers.

Abstract: A method of virtual staining of a tissue sample includes obtaining imaging data depicting the tissue sample. The method also includes processing the imaging data in at least one machine-learning logic, the at least one machine-learning logic being configured to provide multiple output images all comprising a given virtual stain of the tissue sample, the multiple output images depicting the tissue sample comprising the given virtual stain at different colorings associated with different staining laboratory processes. The method further includes obtaining, from the at least one machine-learning logic, at least one output image of the multiple output images.

Abstract: A computer-implemented method for predicting digital images in the form of a digital fluorescence representation together with a further derived representation by means of a combined machine learning system is described. The method comprises providing a first digital image of a tissue sample that was recorded under white light by means of a microsurgical optical system with a digital image recording unit, and predicting a second digital image of the tissue sample in a fluorescence representation and a further representation, which has optical indications about diseased tissue elements. This is done by means of a previously trained combined machine learning system comprising a trained combined machine learning model for predicting the second digital image of the tissue sample in the fluorescence representation and the further representation.

Abstract: A prediction algorithm determines synthetic fluorescence images on the basis of measurement images. A validation of the synthetic fluorescence images can be effected on the basis of reference images which are captured after the measurement images or are captured for a separate sample. Alternatively or additionally, a training of the prediction algorithm can be effected on the basis of training images which are captured after the measurement images or are captured for a separate sample.

Abstract: In a computer-implemented method for generating an image processing model that generates output data defining a stylized contrast image from a microscope image, model parameters of the image processing model are adjusted by optimizing at least one objective function using training data. The training data comprises microscope images as input data and contrast images, wherein the microscope images and the contrast images are generated by different microscopy techniques. In order for the output data to define a stylized contrast image, the objective function forces a detail reduction or the contrast images are detail-reduced contrast images with a level of detail that is lower than in the microscope images and higher than in binary images.

Abstract: A method, a device, and a computer program product captures microscopy objects in image data that includes first images recorded with a first contrast and second images recorded with a second contrast, wherein in each case, one of the first and one of the second images can be correspondingly assigned to each other. The method includes capturing information indicating microscopy objects in at least one of the second images, transferring the captured information to those of the first images which correspond to the at least one of the second images, and capturing information indicating microscopy objects in the first images, to which the captured information of the second images was transferred by using the transferred information.

Abstract: In a computer-implemented method for generating an image processing model that generates output data defining a stylized contrast image from a microscope image, model parameters of the image processing model are adjusted by optimizing at least one objective function using training data. The training data comprises microscope images as input data and contrast images, wherein the microscope images and the contrast images are generated by different microscopy techniques. In order for the output data to define a stylized contrast image, the objective function forces a detail reduction or the contrast images are detail-reduced contrast images with a level of detail that is lower than in the microscope images and higher than in binary images.

Abstract: The invention relates to techniques for material testing of optical test pieces, for example of lenses. Angle-variable illumination, using a suitable illumination module, and/or angle-variable detection are carried out in order to create a digital contrast. The digital contrast can be, for example, a digital phase contrast. A defect detection algorithm for automated material testing based on a result image with digital contrast can be used. For example, an artificial neural network can be used.

Abstract: The present invention relates to a method for evaluating a statistically distributed measured value in the examination of an element for a photolithography process, comprising the following steps: (a) using a plurality of parameters in a trained machine learning model, wherein the parameters characterize a state of a measurement environment in a time period assigned to a measurement of the measured value; and (b) executing the trained machine learning model in order to evaluate the measured value.

Abstract: The invention relates to a device for examining and/or processing an element for photolithography with a beam of charged particles, wherein the device comprises: (a) means for acquiring measurement data while the element for photolithography is exposed to the beam of charged particles; and (b) means for predetermining a drift of the beam of charged particles relative to the element for photolithography with a trained machine learning model and/or a predictive filter, wherein the trained machine learning model and/or the predictive filter use(s) at least the measurement data as input data.

Abstract: The present invention relates to a method and an apparatus for determining at least one unknown effect of defects of an element of a photolithography process. The method comprises the steps of: (a) providing a model of machine learning for a relationship between an image, design data associated with the image and at least one effect of the defects of the element of the photolithography process arising from the image; (b) training the model of machine learning using a multiplicity of images used for training purposes, design data associated with the images used for training purposes and corresponding effects of the defects; and (c) determining the at least one unknown effect of the defects by applying the trained model to a measured image and the design data associated with the measured image.

Abstract: The present invention relates to an apparatus for analyzing an element of a photolithography process, said apparatus comprising: (a) a first measuring apparatus for recording first data of the element; and (b) means for transforming the first data into second, non-measured data, which correspond to measurement data of a measurement of the element with a second measuring apparatus; (c) wherein the means comprise a transformation model, which has been trained using a multiplicity of first data used for training purposes and second data corresponding therewith, which are linked to the second measuring apparatus.