Abstract: Techniques for adapting an adaptive specimen image acquisition system using an artificial neural network (ANN) are disclosed. An adaptive specimen image acquisition system is configurable to scan a specimen to produce images of varying qualities. An adaptive specimen image acquisition system first scans a specimen to produce a low-quality image. An ANN identifies objects of interest within the specimen image. A scan mask indicates regions of the image corresponding to the objects of interest. The adaptive specimen image acquisition system scans only the regions of the image corresponding to the objects of interest, as indicated by the scan mask, to produce a high-quality image. The low-quality image and the high-quality image are merged in a final image. The final image shows the objects of interest at a higher quality, and the rest of the specimen at a lower quality.

Abstract: Methods and systems for quantum computing based sample analysis include computing cross-correlations of two images using a quantum processing system, and computing less noisy image based of two or more images using a quantum processing system. Specifically, the disclosure includes methods and systems for utilizing a quantum computing system to compute and store cross correlation values for two sets of data, which was previously believed to be physically impossible. Additionally, the disclosure also includes methods and systems for utilizing a quantum computing system to generate less noisy data sets using a quantum expectation maximization maximum likelihood (EMML). Specifically, the disclosed systems and methods allow for the generation of less noisy data sets by utilizing the special traits of quantum computers, the systems and methods disclosed herein represent a drastic improvement in efficiency over current systems and methods that rely on traditional computing systems.

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.

Abstract: Various methods and systems are provided for generating an energy resolved chroma image of a sample. Upon irradiated by a charged particle beam, scattered charged particles from the sample are directed to form a first image before entering a spectrometer. The scattered charged particles are then dispersed based on their energy when passing through the spectrometer. The dispersed particles form a second image on a detector. The scattered particles at each location of the first image is spread along a corresponding energy spread vector in the second image.

Abstract: Various methods and devices are provided for searching the optimum sample condition of a sample for cryogenic electron microscopy. Multiple samples with different sample conditions may be screened using a sample inspection device. The sample inspection device includes at least a chamber formed between a top electron transparent layer and a bottom electron transparent layer for holding the sample. Multiple pillars are arranged within the chamber. The sample inspection device includes a window covering at least one of the multiple pillars.

Abstract: A focused ion beam (FIB) is used to mill beam spots into a substrate at a variety of ion beam column settings to form a set of training images that are used to train a convolutional neural network. After the neural network is trained, an ion beam can be adjusted by obtaining spot image which is processed with the neural network. The neural network can provide a magnitude and direction of defocus, aperture position, lens adjustments, or other ion beam or ion beam column settings. In some cases, adjustments are not made by the neural network, but serve to indicate that the ion beam and associated ion column continue to operate stably, and additional adjustment is not required.

Abstract: Disclosed herein are radio frequency (RF) cavities and systems including such RF cavities. The RF cavities are characterized as having an insert with at least one sidewall coated with a material to prevent charge build up without affecting RF input power and that is heat and vacuum compatible. One example RF cavity includes a dielectric insert, the dielectric insert having an opening extending from one side of the dielectric insert to another to form a via, and a coating layer disposed on an inner surface of the dielectric insert, the inner surface facing the via, wherein the coating layer has a thickness and a resistivity, the thickness less than a thickness threshold, and the resistivity greater than a resistivity threshold, wherein the thickness and resistivity thresholds are based partly on operating parameters of the RF cavity.

Abstract: A charged-particle beam (CPB) is aligned to a primary axis of a CPB microscope by determining a first beam deflection drive to a beam deflector for directing the CPB passing a reference location displaced from the primary axis. The beam deflector is provided with a second beam deflection drive during the working mode of the CPB microscope to propagate the beam along the primary axis. The second beam deflection drive is determined based on the first beam deflection drive.

Abstract: The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample; scanning said charged particle beam over said sample; and detecting, using a first detector, emissions of a first type from the sample in response to the beam scanned over the sample. Spectral information of detected emissions of the first type is used for assigning a plurality of mutually different phases to said sample. In a further step, a corresponding plurality of different color hues—with reference to an HSV color space—are associated to said plurality of mutually different phases. Using a second detector, emissions of a second type from the sample in response to the beam scanned over the sample are detected. Finally an image representation of said sample is provided.

Abstract: Methods and apparatuses disclosed herein provide beam hardening correction to tomographic reconstruction using a simplification to the Alvarez-Macovski attenuation model. An example method includes simplifying a forward projection model, the forward projection model based on an Alvarez-Macovski (AM) attenuation model, wherein the simplification of the forward projection model simplifies the AM attenuation model for one of photoelectric effect only, constant density, constant atomic number, and density proportional to atomic number, and performing an iterative reconstruction of a sample using the simplified forward projection model, the iterative reconstruction weighted by a first spectrum, wherein measured image data of the sample used in the iterative reconstruction is obtained at a first energy, and wherein a reverse operation of the iterative reconstruction is a non-adjoint to the simplified forward projection model.

Abstract: The present invention relates to a method of training a network for reconstructing and/or segmenting microscopic images comprising the step of training the network in the cloud. Further, for training the network in the cloud training data comprising microscopic images can be uploaded into the cloud and a network is trained by the microscopic images. Moreover, for training the network the network can be benchmarked after the reconstructing and/or segmenting of the microscopic images. Wherein for benchmarking the network the quality of the image(s) having undergone the reconstructing and/or segmenting by the network can be compared with the quality of the image(s) having undergone reconstructing and/or segmenting by already known algorithm and/or a second network.

Abstract: The invention relates to a method of manufacturing a charged particle detector, comprising the steps of providing a sensor device, such as an Active Pixel Sensor (APS). Said sensor device at least comprises a substrate layer and a sensitive layer. The method further comprises the step of providing a mechanical supporting layer and connecting said mechanical supporting layer to said sensor device. After connection, the sensitive layer is situated in between said substrate layer and said mechanical supporting layer. By connecting the mechanical supporting layer, it is possible to thin said substrate layer for forming said charged particle detector. The mechanical supporting layer forms part of the manufactured detector. The detector can be used in a charged particle microscope, such as a Transmission Electron Microscope for direct electron detection.

Abstract: Disclosed herein are example embodiments for performing microscopy using microscope systems that combine both scanning-electron-microscope-cathodoluminescence (SEM-CL) microscopy and focused-ion-beam ion-induced optical emission (FIB-IOE) microscopy. Certain embodiments comprise operating a microscopy system in a first microscopy mode in which an electron beam interacts with a sample at a sample location and causes first-mode photons and electrons to be emitted, the first-mode photons including photons generated through a cathodoluminescence process; and operating a microscopy system in a second microscopy mode in which an ion beam interacts with a sample at the sample location and causes second-mode photons to be emitted, the second-mode photons including photons generated through an ion-induced luminescence process and photons generated through an atomic de-excitation process.

Abstract: Various methods and systems are provided for acquiring electron backscatter diffraction patterns. In one example, a first scan is performed by directing a charged particle beam towards multiple impact points within a ROI and detecting particles scattered from the multiple impact points. A signal quality of each impact point of the multiple impact points is calculated based on the detected particles. A signal quality of the ROI is calculated based on the signal quality of each impact point. Responsive to the signal quality of the ROI lower than a threshold signal quality, a second scan of the ROI is performed. A structural image of the sample may be formed based on detected particles from both the first scan and the second scan.

Abstract: Systems and method for the preparation and delivery of biological samples for charged particle analysis are disclosed herein. An example system at least includes an ion filter coupled to select a sample ion from an ionized sample supply, the ion filter including a quadrupole filter to select the sample ion from the sample supply, an energy reduction cell coupled to receive the selected sample ion and reduce a kinetic energy of the sample ion, a validation unit coupled to receive the sample ion and determine whether the sample ion is a target sample ion, a substrate coupled to receive the sample, wherein the substrate is electron transparent, an ion transport module coupled to receive the sample ion from the ion filter and transport the sample ion to the substrate, and an imaging system arranged to image, with a low energy charged particle beam, the sample located on the substrate, wherein the substrate is arranged in an analysis location.

Abstract: Methods of aligning specimen images of specimen sections situated on a substrate include obtaining an optical or SEM image of the substrate and locating and aligning optical or SEM images of each specimen section. The specimen sections are then imaged with an SEM to obtain preview images, and a region of interest (ROI) in at least one of the preview images is selected. The preview images are processed so that at least portions of the preview images proximate the ROI are aligned. Based on the alignment of the preview images, final SEM image of selected specimen sections are obtained so that a set of images aligned in three dimensions is available. Image alignment can use cross-correlation with a fixed or variable reference that can be updated as specimen section images are processed.

Abstract: Apparatus and methods are disclosed for sample preparation, suitable for online or offline use with multilayer samples. Ion beam technology is leveraged to provide rapid, accurate delayering with etch stops at a succession of target layers. In one aspect, a trench is milled around a region of interest (ROI), and a conductive coating is developed on an inner sidewall. Thereby, reliable conducting paths are formed between intermediate layers within the ROI and a base layer, and stray current paths extending outside the ROI are eliminated, providing better quality etch progress monitoring, during subsequent etching, from body or scattered currents. Ion beam assisted gas etching provides rapid delayering with etch stops at target polysilicon layers. Uniform etching at deep layers can be achieved. Variations and results are disclosed.

Abstract: Molecular structure may be determined based on structure factors solved from the diffraction pattern and the electron microscopy image of the sample. In particular, the amplitudes of the structure factors may be determined based on intensities of diffraction peaks in the multiple diffraction patterns. The phases of the structure factors may be determined based on electron microscopy images and the intensities of the diffraction peaks.

Abstract: The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample; scanning said charged particle beam over said sample at a plurality of sample locations; and detecting, using a first detector, emissions of a first type from the sample in response to the beam scanned over the plurality of sample locations. Spectral information of detected emissions of the first type is used to assign a plurality of mutually different phases to said sample at said plurality of sample locations. Information relating to at least one previously assigned phase and its respective sample location is used for establishing an estimated phase for at least one other of the plurality of sample locations. Said estimated phase is assigned to said other sample location. A control unit is used to provide a data representation of said sample containing at least information on said plurality of sample locations and said phases.

Abstract: The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample; scanning said charged particle beam over said sample at a plurality of sample positions; and acquiring an EELS spectrum for each of said plurality of sample positions. According to the method, it comprises the further steps of scanning, once more, said charged particle beam over said sample at said plurality of sample positions; acquiring a further EELS spectrum for each of said plurality of sample positions; and combining, for each of said plurality of sample positions, said EELS spectrum with said further EELS spectrum. With this, it is possible to acquire rapid information on the sample being investigated, allowing for faster processing of samples.