Carl Zeiss Smt Gmbh

Carl Zeiss Microscopy Gmbh

Carl Zeiss Meditec Ag

Carl Zeiss Jena Gmbh

Alexander Freytag

Christoph Husemann

Dirk Seidel

Carsten Schmidt

Daniel Haase

Manuel Amthor

Christian Kungel

Christian Wojek

Stefan Saur

Marco Wilzbach

Lars Stoppe

Michael Budach

Thomas Scheruebl

Nicole Auth

Wibke Hellmich

Anna Alperovich

Volker Doering

Niklas Mevenkamp

Rebecca Elsaesser

Susanne Töpfer

Christian Rensing

Matthias Eibl

Anselm Brachmann

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.

Method and device for capturing microscopy objects in image data

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.

Multi-task learning of white light photographs for a surgical microscope

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.

Method and device for evaluating a statistically distributed measured value in the examination of an element of a photolithography process

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.

Digital contrast post-processing of microscopy images

A device for examining and/or processing an element for photolithography with a beam of charged particles, the device including (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. The trained machine learning model and/or the predictive filter use(s) at least the measurement data as input data.

Devices and methods for examining and/or processing an element for photolithography

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.

Method and apparatus for evaluating an unknown effect of defects of an element of a photolithography process

A method generates an image processing model to calculate a virtually stained image from a microscope image. The image processing model is trained using training data comprising microscope images as input data into the image processing model and target images that are formed via chemically stained images registered locally in relation to the microscope images. The image processing model is trained to calculate virtually stained images from the input microscope images by optimizing an objective function that captures a difference between the virtually stained images and the target images. After a number of training steps, at least one weighting mask is defined using one of the chemically stained images and an associated virtually stained image calculated after the number of training steps. In the weighting mask, one or more image regions are weighted based on differences between locally corresponding image regions in the virtually stained image and in the chemically stained image. Subsequent training considers the weighting mask in the objective function.

Microscopy system and method for generating a virtually stained image

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.

Apparatus and method for analyzing an element of a photolithography process with the aid of a transformation model

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.

Material testing of optical test pieces

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.

Growing community of inventors

Erfurt, Germany

Alexander Freytag