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: A method comprises: providing FIB and CPB columns with FIB and CPB optical axes coinciding at a wafer surface; in the coincidence arrangement, removing a cross section surface layer of a measurement site of a wafer using the FIB column to make a new cross section accessible for imaging; reducing a working distance between the CPB imaging column and the wafer surface in a direction along the axis of the CPB imaging column; imaging the new cross section at the measurement site of the wafer with the CPB imaging column at the reduced working distance and thus not in the coincidence arrangement; and increasing the working distance between the CPB imaging column and the wafer surface in the direction along the axis of the CPB imaging column until the coincidence arrangement is reached.

Abstract: A system and a method for volume inspection of semiconductor wafers are configured for milling and fast image acquisition of cross-sections surfaces in an inspection volume. High quality images can be obtained by restriction of the imaging to regions of interest or by averaging over several fast image scans. The method and device can be utilized for quantitative metrology, defect detection, process monitoring, defect review, and inspection of integrated circuits within semiconductor wafers.

Abstract: A computer implemented method for the detection of anomalies comprises: selecting an imaging dataset of a wafer and a hyperparameter value defining a machine learning model for anomaly detection; training and evaluating the machine learning model by computing an objective function value; and selecting one of the trained machine learning models and applying it to detect anomalies. A computer implemented method for the detection of anomalies in an imaging dataset of a wafer comprises: providing samples of a distribution of anomaly detection image values for each defect class; calibrating the anomaly detection image by training a machine learning model for anomaly localization; and applying a threshold to the calibrated anomaly detection image to detect anomalies.

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 computer implemented method detects and classifies anomalies in an imaging dataset of a wafer comprising a plurality of semiconductor structures. The method comprises determining a current detection of a plurality of anomalies in the imaging dataset, and obtaining an unsupervised or semi-supervised clustering of the current detection of the plurality of anomalies. Based on at least one decision criterion at least one cluster of the clustering is selected for presentation and annotation to a user via a user interface. An anomaly classification algorithm is re-trained based on the annotated anomalies. A system for controlling the quality of wafers and a system for controlling the production of wafers are also disclosed.

Abstract: A method includes preparing an initial layer of a semiconductor sample., and aligning a surface area of a region of interest volume of the prepared layer with an object field of an SEM. An electron energy of an electron beam of the SEM is adjusted. The region of interest volume is probed with the SEM within the object field. X-rays emanating from the aligned region of interest volume are detected. A detection signal is post-processed to deconvolute the detection signal into structured data attributed to the sample structure within the region of interest volume. A next layer to be investigated is prepared by FIB etching and the steps “preparing” to “post-processing” are repeated until the layer by layer investigation of a superimposed volume of interest of the sample is completed.

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: A system and a method for volume inspection of semiconductor wafers are configured for milling and imaging of reduced number or areas of appropriate cross-sections surfaces in an inspection volume and determining inspection parameters of the 3D objects from the cross-section surface images. The system and method can be utilized for quantitative metrology, defect detection, process monitoring, defect review, and inspection of integrated circuits within semiconductor wafers.

Abstract: A system and a method for volume inspection of semiconductor wafers with increased throughput are configured for milling and imaging a reduced number or areas of appropriate cross-sections surfaces in an inspection volume and determining inspection parameters of the 3D objects from the cross-section surface images. The method and device can be utilized for quantitative metrology, defect detection, process monitoring, defect review, and inspection of integrated circuits within semiconductor wafers.

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 3D-inspection of a semiconductor object inside of an inspection volume of a wafer or wafer sample comprises a 3D data processing and a step for acquiring a plurality of two-dimensional images. The acquiring step comprises a monitoring step for determining whether a two-dimensional image is in conformity with a desired property of the 3D data processing. The disclosure further comprises a method of configuring the method of 3D-inspection and a system configured to execute the method of 3D-inspection as well as the method of configuring the method of 3D-inspection.

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 defect detection in a sample, such as in a semiconductor sample, includes the following steps: providing a reference image of the sample; providing a sample image generated via a particle beam inspection system, wherein the sample image comprises a rotation with respect to the reference image; dividing the sample image into sample image regions; dividing the reference image into reference image regions, wherein each sample image region is assigned one reference image region to form an image region pair; identifying in each image region pair a structure that is present both in the sample image region and also in the associated reference image region of the image region pair; registering the sample image regions by correcting a lateral offset of the identified structure in each sample image region on the basis of the location of the identified structure in the respectively associated reference image region, as a result of which corrected sample image regions are formed; and comparing each corrected

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.