Abstract: The invention relates to a method of, and apparatus for, examining a sample using a charged particle beam apparatus. The method as defined herein comprises the step of detecting, using a first detector, emissions of a first type from the sample in response to the charged particle beam illuminating the sample. The method further comprises the step of acquiring spectral information on emissions of a second type from the sample in response to the charged particle beam illuminating the sample. As defined herein, said step of acquiring spectral information comprises the steps of providing a spectral information prediction algorithm and using said algorithm for predicting said spectral information based on detected emissions of the first type as an input parameter of said algorithm. With this it is possible to gather EDS data using only a BSE detector.

Abstract: Method and system are disclosed for determining sample composition from spectral data acquired by a charged particle microscopy system. Chemical elements in a sample are identified by processing the spectral data with a trained neural network (NN). If the identified chemical elements not matching with a known elemental composition of the sample, the trained NN is retrained with the spectral data and the known elemental composition of the sample. The retrained NN can then be used to identify chemical elements within other samples.

Abstract: Techniques for training an artificial neural network (ANN) using simulated specimen images are described. Simulated specimen images are generated based on data models. The data models describe characteristics of a crystalline material and characteristics of one or more defect types. The data models do not include any image data. Simulated specimen images are input as training data into a training algorithm to generate an artificial neural network (ANN) for identifying defects in crystalline materials. After the ANN is trained, the ANN analyzes captured specimen images to identify defects shown therein.