Fei Comapny

Ondrej Machek

Pavel Potocek

Tomás Vystavel

Oleksii Kaplenko

Mykola Kaplenko

Jan Klusácek

Libor Strakos

Remco Schoenmakers

Maurice Peemen

Tomás Tůma

Kristýna Bukvisová

Remco Johannes Petrus Geurts

Tereza Konečná

Disclosed herein are charged particle microscopy (CPM) support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a CPM support apparatus may include: first logic to cause a CPM to generate a single image of a first portion of a specimen; second logic to generate a first mask based on one or more regions-of-interest provided by user annotation of the single image; and third logic to train a machine-learning model using the single image and the one or more regions-of-interest. The first logic may cause the CPM to generate multiple images of corresponding multiple additional portions of the specimen, and the second logic may, after the machine-learning model is trained using the single image and the one or more regions-of-interest, generate multiple masks based on the corresponding images of the additional portions of the specimen using the machine-learning model without retraining.

Data acquisition in charged particle microscopy

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.

Training an artificial neural network using simulated specimen images

The disclosure relates to a method of aligning a charged particle beam apparatus, comprising the steps of providing a charged particle beam apparatus in a first alignment state; using an alignment algorithm, by a processing unit, for effecting an alignment transition from said first alignment state towards a second alignment state of said charged particle beam apparatus; and providing data related to said alignment transition to a modification algorithm for modifying said alignment algorithm in order to effect a modified alignment transition.

Method of aligning a charged particle beam apparatus

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.

Method of examining a sample using a charged particle beam apparatus

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.

Method and system for determining sample composition from spectral data

Methods and systems for creating TEM lamella using image restoration algorithms for low keV FIB images are disclosed. An example method includes irradiating a sample with an ion beam at low keV settings, generating a low keV ion beam image of the sample based on emissions resultant from irradiation by the ion beam, and then applying an image restoration model to the low keV ion beam image of the sample to generate a restored image. The sample is then localized within the restored image, and a low keV milling of the sample is performed with the ion beam based on the localized sample within the restored image.

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Brno, Czechia

Ondrej Machek