Abstract: Methods and systems for using dynamic data-driven detector tuning to investigate a sample with a charged particle microscopy system are disclosed herein. Methods and systems according to the present disclosure include acquiring sample data for a region of interest on the sample, and then determining one or more materials present in the region of interest. Once the materials are identified, a differentiation detector window is identified for the one or more materials, and the detector settings of a detector are adjusted such that the detector obtains information within the differentiation detector window. Thus, as the sample is subsequently scanned, the detector obtains an optimal range of information that is allows for efficient differentiation among the one or more materials.

Abstract: The invention relates to a method implemented by a data processing apparatus, comprising the steps of receiving an image; providing a set-point for a desired image quality parameter of said image; and processing said image using an image analysis technique for determining a current image quality parameter of said image. In the method, the current image quality parameter is compared with said desired set-point. Based on said comparison, a modified image is generated by using an image modification technique. The generating comprises a step of deteriorating said image in terms of said image quality parameter in case said current image quality parameter exceeds said set-point. The modified image is then output and may be further analysed.

Abstract: Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, an example method may comprise receiving, by a first device located at a premises and from a second device located external to the premises, and via a network, configuration data for a charged particle microscope located at the premises. The method may comprise updating, by the first device and based on the configuration data, one or more configuration settings associated with the charged particle microscope. The method may comprise causing, based on the updated one or more configuration settings, one or more operations associated with the charged particle microscope to be performed.

Abstract: The invention relates to a method implemented by a data processing apparatus, comprising the steps of receiving an image; providing a set-point for a desired image quality parameter of said image; and processing said image using an image analysis technique for determining a current image quality parameter of said image. In the method, the current image quality parameter is compared with said desired set-point. Based on said comparison, a modified image is generated by using an image modification technique. The generating comprises the steps of improving said image in terms of said image quality parameter in case said current image quality parameter is lower than said set-point; and deteriorating said image in terms of said image quality parameter in case said current image quality parameter exceeds said set-point. The modified image is then output.

Abstract: A method of imaging a sample includes acquiring one or more first images of a region of the sample at a first imaging condition with a charged particle microscope system. The one or more first images are applied to an input of a trained machine learning model to obtain a predicted image indicating atom structure probability in the region of the sample. An enhanced image indicating atom locations in the region of the sample based on the atom structure probability in the predicted image is caused to be displayed in response to obtaining the predicted image.

Abstract: Disclosed herein are CPM support systems, as well as related apparatuses, methods, computing devices, and computer-readable media. For example, in some embodiments, a charged particle microscope computational support apparatus may include: first logic to, for each angle of a plurality of angles, receive an associated image of a specimen at the angle, and generate an associated scan mask based on one or more regions-of-interest in the associated image; second logic to, for each angle of the plurality of angles, generate an associated data set of the specimen by processing data from a scan, in accordance with the associated scan mask, by a charged particle microscope of the specimen at the angle; and third logic to provide, for each angle of the plurality of angles, the associated data set of the specimen to reconstruction logic to generate a three-dimensional reconstruction of the specimen.

Abstract: Methods for drift corrected, fast, low dose, adaptive sample imaging with a charged particle microscopy system include scanning a surface region of a sample with a charged particle beam to obtain a first image of the surface region with a first detector modality, and then determining a scan strategy for the surface region. The scan strategy comprises a charged particle beam path, a first beam dwell time associated with at least one region of interest in the first image, the first beam dwell time being sufficient to obtain statistically significant data from a second detector modality, and at least a second beam dwell time associated with other regions of the first image, wherein the first beam dwell time is different than the second beam dwell time. The surface region of the sample is then scanned with the determined scan strategy to obtain data from the first and second detector.

Abstract: Methods and apparatuses are disclosed herein for parameter estimation for metrology. An example method at least includes optimizing, using a parameter estimation network, a parameter set to fit a feature in an image based on one or more models of the feature, the parameter set defining the one or more models, and providing metrology data of the feature in the image based on the optimized parameter set.

Abstract: Disclosed herein are apparatuses, systems, methods, and computer-readable media relating to area selection in charged particle microscope (CPM) imaging. For example, in some embodiments, a CPM support apparatus may include: first logic to generate a first data set associated with an area of a specimen by processing data from a first imaging round of the area by a CPM; second logic to generate predicted parameters of the area; and third logic to determine whether a second imaging round of the area is to be performed by the CPM based on the predicted parameters of the area; wherein the first logic is to, in response to a determination by the third logic that a second imaging round of the area is to be performed, generate a second data set, including measured parameters, associated with the area by processing data from a second imaging round of the area by the CPM.

Abstract: Disclosed herein are CPM support systems, as well as related apparatuses, methods, computing devices, and computer-readable media. For example, in some embodiments, a charged particle microscope computational support apparatus may include: first logic to, for each angle of a plurality of angles, receive an associated image of a specimen at the angle, and generate an associated scan mask based on one or more regions-of-interest in the associated image; second logic to, for each angle of the plurality of angles, generate an associated data set of the specimen by processing data from a scan, in accordance with the associated scan mask, by a charged particle microscope of the specimen at the angle; and third logic to provide, for each angle of the plurality of angles, the associated data set of the specimen to reconstruction logic to generate a three-dimensional reconstruction of the specimen.

Abstract: Tomographic images are obtained by processing a tilt series of 2D images by aligning and combining images withing a group of neighbor images. The tilt series generally includes sparsely sampled images. Images of the tilt series at tilt angles associated with the sparsely sample images are selected as reference frames, grouped with neighbor images, and the group of images aligned. The aligned images are combined to produce replacement frames and a replacement frame tilt series that can be used for tomographic reconstruction.

Abstract: Methods for drift corrected, fast, low dose, adaptive sample imaging with a charged particle microscopy system include scanning a surface region of a sample with a charged particle beam to obtain a first image of the surface region with a first detector modality, and then determining a scan strategy for the surface region. The scan strategy comprises a charged particle beam path, a first beam dwell time associated with at least one region of interest in the first image, the first beam dwell time being sufficient to obtain statistically significant data from a second detector modality, and at least a second beam dwell time associated with other regions of the first image, wherein the first beam dwell time is different than the second beam dwell time. The surface region of the sample is then scanned with the determined scan strategy to obtain data from the first and second detector.

Abstract: Methods for drift corrected, fast, low dose, adaptive sample imaging with a charged particle microscopy system include scanning a surface region of a sample with a charged particle beam to obtain a first image of the surface region with a first detector modality, and then determining a scan strategy for the surface region. The scan strategy comprises a charged particle beam path, a first beam dwell time associated with at least one region of interest in the first image, the first beam dwell time being sufficient to obtain statistically significant data from a second detector modality, and at least a second beam dwell time associated with other regions of the first image, wherein the first beam dwell time is different than the second beam dwell time. The surface region of the sample is then scanned with the determined scan strategy to obtain data from the first and second detector.

Abstract: Methods and systems for generating high resolution reconstructions of 3D samples imaged using slice and view processes where the electron interaction depth of the imaging beam is greater than slice thicknesses. Data obtained via such slice and view processes is enhanced with a depth blur reducing algorithm, that is configured to reduce depth blur caused by portions of the first data and second data that are resultant from electron interactions outside the first layer and second layer, respectively, to create enhanced first data and second enhanced data. A high-resolution 3D reconstruction of the sample is then generated using the enhanced first data and the enhanced second data. In some embodiments, the depth blur reducing algorithm may be selected from a set of such algorithms that have been individually configured for certain microscope conditions, sample conditions, or a combination thereof.

Abstract: 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.

Abstract: Methods and systems for generating high resolution reconstructions of 3D samples imaged using slice and view processes where the electron interaction depth of the imaging beam is greater than slice thicknesses. Data obtained via such slice and view processes is enhanced with a depth blur reducing algorithm, that is configured to reduce depth blur caused by portions of the first data and second data that are resultant from electron interactions outside the first layer and second layer, respectively, to create enhanced first data and second enhanced data. A high-resolution 3D reconstruction of the sample is then generated using the enhanced first data and the enhanced second data. In some embodiments, the depth blur reducing algorithm may be selected from a set of such algorithms that have been individually configured for certain microscope conditions, sample conditions, or a combination thereof.

Abstract: The invention relates to a method implemented by a data processing apparatus, comprising the steps of receiving an image; providing a set-point for a desired image quality parameter of said image; and processing said image using an image analysis technique for determining a current image quality parameter of said image. In the method, the current image quality parameter is compared with said desired set-point. Based on said comparison, a modified image is generated by using an image modification technique. The generating comprises the steps of improving said image in terms of said image quality parameter in case said current image quality parameter is lower than said set-point; and deteriorating said image in terms of said image quality parameter in case said current image quality parameter exceeds said set-point. The modified image is then output.

Abstract: Convolutional neural networks (CNNs) of a set of CNNs are evaluated using a test set of images (electron micrographs) associated with a selected particle type. A preferred CNN is selected based on the evaluation and used for processing electron micrographs of test samples. The test set of images can be obtained by manual selection or generated using a model of the selected particle type. Upon selection of images using the preferred CNN in processing additional electron micrographs, the selected images can be added to a training set or used as an additional training set to retrain the preferred CNN. In some examples, only selected layers of the preferred CNN are retrained. In other examples, two dimensional projections of based on particles of similar structure are used for CNN training or retraining.

Abstract: Methods and systems for neural network based image restoration are disclosed herein. An example method at least includes acquiring a plurality of training image pairs of a sample, where each training image of each of the plurality of training image pairs are images of a same location of a sample, and where each image of the plurality of training image pairs are acquired using same acquisition parameters, updating an artificial neural network based on the plurality of training image pairs, and denoising a plurality of sample images using the updated artificial neural network, where the plurality of sample images are acquired using the same acquisition parameters as used to acquire the plurality of training image pairs.

Abstract: 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.