Abstract: A method for acquiring a 3D image of a sample structure includes acquiring a first raw 2D set of 2D images of a sample structure at a limited number of raw sample planes; calculating a 3D image of the sample structure represented by a 3D volumetric image data set; and extracting a measurement parameter from the 3D volumetric image data set. A further number of interleaving 2D image acquisitions are recorded at a further number of interleaved sample planes which do not coincide with previous acquisition sample planes. The steps “calculating,” “extracting” and “assigning” are repeated for the further interleaving 2D set until convergence or a maximum number of 2D image acquisitions is recorded. A projection system used for such method comprises a projection light source, a rotatable sample structure holder and a spatially resolving detector. Such method can also be used to acquire virtual tomographic images of a sample.

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 for measuring a sample with a microscope, the method comprising the steps of: measuring a tilt of the sample, correcting an orientation of the sample based on the tilt, and scanning the sample.

Abstract: Semiconductor structures can be investigated, e.g., in an in-line quality check. An x-ray scattering measurement, e.g., CD-SAXS, can be used for wafer metrology. The x-ray scattering measurement can be configured based on a slice-and-imaging tomographic measurement using a dual-beam device, e.g., including a focused ion beam device and a scanning electron microscope.

Abstract: The present disclosure provides a method of transferring alignment information from a first set of images to a second set of images, a respective computer program product and a respective inspection device. A first set of cross-section images in a first imaging mode is obtained, the first cross-section images being taken at times Tai. A second set of cross-section images in a second imaging mode is obtained, the second cross-section images being taken at times Tbj, the times Tbj differing from the times Tai. Obtaining the first and second sets of cross-section images comprises subsequently removing a cross-section surface layer of a sample to make a new cross-section accessible for imaging, and imaging the new cross-section of the sample in the first imaging mode or in the second imaging mode. Switching is performed between the first and second imaging modes while obtaining the first and second sets of cross-section images.

Abstract: A method of a defect detection of a plurality of semiconductor structures arranged on a wafer includes obtaining a microscopic image of the wafer. The microscopic image depicts the plurality of semiconductor structures. The method also includes obtaining, from a database, fingerprint data for each base pattern class of a set of base pattern classes associated with respective one or more semiconductor structures of the plurality of semiconductor structures. The method further includes performing the defect detection based on the fingerprint data and the microscopic image.

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: 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: Methods and evaluation devices for evaluating 3D data of a device under inspection are provided. A first machine learning logic detects target objects, and a second machine learning logic provides a voxel segmentation for the target objects. Based on the segmented voxels, a transformation to feature space is performed to obtain measurement results.

Abstract: In a method to acquire a 3D image of a sample structure initially a first raw 2D set of 2D images of a sample structure is acquired at a limited number of raw sample planes. From this first raw 2D set a 3D image of the sample structure being represented by a 3D volumetric image data set is calculated and a measurement parameter is extracted from the 3D volumetric image data set. Such measurement parameter is assigned to the number of 2D image acquisitions recorded during the acquisition step. Then, a further interleaving 2D set of 2D images of the sample structure is required by recording a further number of interleaving 2D image acquisitions at a further number of interleaved sample planes which do not coincide with the previous acquisition sample planes. The steps “calculating,” “extracting” and “assigning” are repeated for the further interleaving 2D set. The actual and the last extracted measurement parameters are compared to check whether a convergence criterion is met.

Abstract: The present invention relates to a method for measuring a sample with a microscope, the method comprising the steps of: measuring a tilt of the sample, correcting an orientation of the sample based on the tilt, and scanning the sample.

Abstract: The present disclosure relates to dual beam device and three-dimensional circuit pattern inspection techniques by cross sectioning of inspection volumes with large depth extension exceeding 1 ?m below the surface of a semiconductor wafer, as well as methods, computer program products and apparatuses for generating 3D volume image data of a deep inspection volume inside a wafer without removal of a sample from the wafer. The disclosure further relates to 3D volume image generation and cross section image alignment methods utilizing a dual beam device for three-dimensional circuit pattern inspection.

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: A three-dimensional circuit pattern inspection technique includes cross sectioning integrated circuits for obtaining a 3D volume image of an integrated semiconductor sample. The method employs a feature based alignment of cross section images based on features of an integrated semiconductor sample. A computer program product and apparatus are provided.

Abstract: A method includes detecting a plurality of anomalies in an imaging dataset of a wafer. The wafer includes a plurality of semiconductor structures. The method also includes executing multiple iterations. At least some of the iterations include determining a current classification of the plurality of anomalies using a machine-learned classification algorithm and tiles of the imaging dataset associated with the plurality of anomalies. The current classification includes a current set of classes into which the anomalies of the plurality of anomalies are binned. The method further includes, based on at least one decision criterion, selecting at least one anomaly of the plurality of anomalies for a presentation to a user. In addition, the method includes, based on an annotation of the at least one anomaly provided by the user with respect to the current classification, re-training the classification algorithm.

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: 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 includes controlling a multi-scanning electron microscope, mSEM, to capture a first image of a wafer attached to a motorized handling stage while the motorized handling stage is in a first position. The first image includes at least a part of a notch of the wafer. The method also includes determining a radial axis of the wafer based on the first image, and controlling the motorized handling stage to shift the wafer along the radial axis by half a diameter of the wafer so that the motorized handling stage is in a second position. The method further includes controlling the mSEM to capture a second image of the wafer while the motorized handling stage is in the second position. The second image includes wafer structures.

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.