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 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 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 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 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 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, 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: Wafer inspection methods and systems include converting an acquired image to a polygonal chain representation. The polygonal chain representation is converted to feature vectors. The feature vectors are compared to further feature vectors obtained based on design data.

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.