Abstract: A method includes obtaining an image data set that depicts semiconductor components, and applying a hierarchical bricking to the image data set. In this case, the bricking includes a plurality of bricks on a plurality of hierarchical levels. The bricks on different hierarchical levels have different image element sizes of corresponding image elements.

Abstract: 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.

Abstract: A method includes obtaining at least one 2-D image dataset of semiconductor structures formed on a wafer including one or more defects during a wafer run of a wafer using a predefined fabrication process. The method also includes determining, based on at least one machine-learning algorithm trained on prior knowledge of the fabrication process and based on the at least one 2-D image dataset, one or more process deviations of the wafer run from the predefined fabrication process as a root cause of the one or more defects. A 3-D image dataset may be determined as a hidden variable.

Abstract: A method for creating a second series of individual images with a first series of individual images, the individual images of the first or the second series of individual images having been captured with an objective, includes determining the entrance pupil and the field of vision of the objective for the individual images of the first series and creating or adapting the individual images of the second series in accordance with the entrance pupil and the field of vision of the objective of the individual image in question of the first series.

Abstract: A method for creating a second series of individual images with a first series of individual images, the individual images of the first or the second series of individual images having been captured with an objective, includes determining the entrance pupil and the field of vision of the objective for the individual images of the first series and creating or adapting the individual images of the second series in accordance with the entrance pupil and the field of vision of the objective of the individual image in question of the first series.

Abstract: A method, including: recording plural images of an object by scanning plural particle beams across the object and detecting signals generated by the particle beams, wherein the plural particle beams are generated by a multi-beam particle microscope; determining plural regions of interest; determining plural image regions in each of the recorded images; determining plural displacement vectors; and determining image distortions based on image data of the recorded images and the determined displacement vectors.

Abstract: Methods for determining metrology sites for products includes detecting corresponding objects in measurement data of one or more product samples, and aligning the detected objects are aligned. The methods also include analyzing the aligned objects, and determining metrology sites based on the analysis. Devices use such methods to determine metrology sites for products.

Abstract: Methods and apparatuses for designing optical systems are provided. In this case, on a plurality of known optical systems, machine learning is carried out in order to train a computing device. After this training, the computing device can generate a design for an optical system on the basis of parameters describing desired properties of an optical system.

Abstract: A method includes obtaining an image data set that depicts semiconductor components, and applying a hierarchical bricking to the image data set. In this case, the bricking includes a plurality of bricks on a plurality of hierarchical levels. The bricks on different hierarchical levels have different image element sizes of corresponding image elements.

Abstract: A method, including: recording plural images of an object by scanning plural particle beams across the object and detecting signals generated by the particle beams, wherein the plural particle beams are generated by a multi-beam particle microscope; determining plural regions of interest; determining plural image regions in each of the recorded images; determining plural displacement vectors; and determining image distortions based on image data of the recorded images and the determined displacement vectors.

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: A method for creating a second series of individual images with a first series of individual images, the individual images of the first or the second series of individual images having been captured with an objective, includes determining the entrance pupil and the field of vision of the objective for the individual images of the first series and creating or adapting the individual images of the second series in accordance with the entrance pupil and the field of vision of the objective of the individual image in question of the first series.

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: A method includes obtaining at least one 2-D image dataset of semiconductor structures formed on a wafer including one or more defects during a wafer run of a wafer using a predefined fabrication process. The method also includes determining, based on at least one machine-learning algorithm trained on prior knowledge of the fabrication process and based on the at least one 2-D image dataset, one or more process deviations of the wafer run from the predefined fabrication process as a root cause of the one or more defects. A 3-D image dataset may be determined as a hidden variable.

Abstract: An event classification is trained by machine learning. An anomaly detection for detecting events in an image data set is thereby performed. Based on the performance of the anomaly detection, a model assumption of the event classification is determined. An image data set may include a plurality of images, and each image may include an array of pixels. Further, an image data set may include volume data and/or a time sequence of images and in this way represent a video sequence.

Abstract: Methods for determining metrology sites for products includes detecting corresponding objects in measurement data of one or more product samples, and aligning the detected objects are aligned. The methods also include analyzing the aligned objects, and determining metrology sites based on the analysis. Devices use such methods to determine metrology sites for products.

Abstract: Methods for determining one or more quality or size parameters of a structure in a semiconductor product, on the basis of an image of the semiconductor product which was generated with the aid of charged particles which have been radiated onto the semiconductor product, include: providing the image of the semiconductor product; applying the provided image to a machine-learning-based method such as, e.g., an artificial neural network which has been trained with training images of semiconductor products and which is configured to generate an output parameter from the provided image; and determining the size parameter of the structure on the basis of the output parameter.

Abstract: A computer-implemented method for determining a representation of a rim of a spectacles frame or a representation of the edges of the spectacle lenses is disclosed, wherein at least two calibrated images taken from different viewing angles of a head a subject wearing the spectacles frame or the spectacles are provided, and wherein data for at least portions of the rims of the spectacles frame or the edges of the lenses are detected in each image. Further, a three-dimensional model of the spectacles frame or the spectacles is provided, based on geometric parameters, and the geometric parameters are optimised to adapt the model to the detected edges.

Abstract: A computer-implemented method for determining a representation of a rim of a spectacles frame or a representation of the edges of the spectacle lenses is disclosed, wherein at least two calibrated images taken from different viewing angles of a head a subject wearing the spectacles frame or the spectacles are provided, and wherein data for at least portions of the rims of the spectacles frame or the edges of the lenses are detected in each image. Further, a three-dimensional model of the spectacles frame or the spectacles is provided, based on geometric parameters, and the geometric parameters are optimised to adapt the model to the detected edges.

Abstract: Methods and apparatuses for designing optical systems are provided. In this case, on a plurality of known optical systems, machine learning is carried out in order to train a computing device. After this training, the computing device can generate a design for an optical system on the basis of parameters describing desired properties of an optical system.