Correction of optical aberrations in charged particle beam microscopy

Abstract

SOLUTION: A charged particle beam microscope system produces a plurality of images for a plurality of areas of a sample. Respective first sets of one or more aberration predictor values are acquired for each of the images. During the image acquisition, an aberration measurement and a corresponding second set of aberration predictor values are periodically acquired. An aberration model is obtained using the aberration measurements the corresponding second sets of aberration predictor values, where the model takes a set of aberration predictor values as input and outputs predicted aberration data. The model is applied to the first sets of aberration predictor values to obtain respective aberration data, which is used to reduce or at least partially correct aberration in the charged particle microscope images.EFFECT: A sample reconstruction is obtained using the acquired charged particle microscope images.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to correcting aberrations in charged particle beam microscopy, particularly, but not exclusively, in electron microscopy. [Background technology]

[0002] Transmission electron microscopy TEM (transmission electron microscopy) is a technique in which a beam of electrons is transmitted through a thin sample to form an image. The image is formed by the interaction of the electrons with the atoms of the sample as they pass through the sample. The image can be captured by a camera. TEM is capable of producing high resolution images due to the short wavelength of the transmitted electrons. Images with a sufficiently high resolution can be used to study and analyze structural details of the sample on an atomic scale. However, optical aberrations in the microscope can limit the resolution of the image. [Brief description of the drawings]

[0003] [Figure 1] FIG. 1 is a block diagram of a system for measuring and correcting optical aberrations in a charged particle beam microscope system operating in transmission imaging mode according to various implementations. [Diagram 2] FIG. 1 is a flow diagram of a particular exemplary method for obtaining aberration measurements that may optionally be used to facilitate various implementations described below. [Diagram 3] FIG. 1 is a flow diagram of a workflow for acquisition of aberration data according to various implementations. [Figure 4a] FIG. 1 is a flow diagram of a workflow for processing acquired aberration data to correct charged particle microscope images, according to various implementations. [Figure 4b] FIG. 1 is a flow diagram of a workflow for processing acquired aberration data to correct charged particle microscope images, according to various implementations. [Diagram 5]FIG. 1 is a flow diagram of a workflow for processing acquired aberration data to fine-tune a charged particle beam microscope system, according to various implementations. [Figure 6] FIG. 1 is a generalized computing system block diagram of a suitable computing environment that may be used in a method for implementing monitoring and correction of optical aberrations in a charged particle beam microscope system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0004] The subject matter disclosed herein relates to improved methods and systems for obtaining sample reconstructions. In particular, the subject matter disclosed herein relates to methods and systems for imaging in a charged particle beam microscope system (e.g., operating in a transmission imaging mode or a scanning imaging mode) while obtaining a model for the aberrations in the system, which is used to reduce or at least partially correct the aberrations. Examples of optical aberrations that can be corrected include, but are not limited to, defocus, two-fold astigmatism, three-fold astigmatism, four-fold astigmatism, five-fold astigmatism, six-fold astigmatism, axial coma, fifth-order axial coma, spherical aberration, sixth-order spherical aberration, stellate aberration, sixth-order stellate aberration, three-lobe aberration, rosette aberration, linear distortion, pincushion distortion, barrel distortion, helical distortion, and elliptical distortion. A particular embodiment of the charged particle beam microscope system relates to an electron microscope system. Thus, the present disclosure includes the above-mentioned aspects and embodiments in which the charged particle is an electron.

[0005] Sample reconstruction can refer to a method of extracting information from multiple acquired images and performing a reconstruction. An example of such a reconstruction is a (3D) structural reconstruction, a 3D model, an exit wave reconstruction, a 3D atomic resolution reconstruction, or a surface image (as in the case of a scanning imaging mode). The images can be acquired through any imaging workflow. Some such workflows acquire many images at different sample positions, such as, but not limited to, single particle analysis or electron tomography. It will be understood that the sample position refers to one or both of the axial position of the sample (e.g., x / y / z coordinates) and the rotation / tilt of the sample.

[0006] In one or more implementations, a method of acquiring a plurality of charged particle microscope images for sample reconstruction includes acquiring a plurality of charged particle microscope images for a plurality of regions of a sample using a charged particle beam microscope system, e.g., an electron microscope system. The method includes acquiring a respective first set of aberration predictor values ​​for each acquisition of the plurality of charged particle microscope images, e.g., a temperature of an optical system of the charged particle microscope, a position and / or a configuration of the optical system of the charged particle microscope, etc. A corresponding second set of aberration measurements and aberration predictor values ​​is periodically acquired while acquiring the plurality of charged particle microscope images. The method further includes obtaining an aberration model using the second set of aberration predictor values ​​and aberration measurements, e.g., by training or updating a model with the second set of aberration predictor values ​​and aberration measurements or by reading the model from a memory. The model takes the set of aberration predictor values ​​as input and outputs predicted aberration data. The model is applied to the first set of aberration predictor values ​​to obtain respective predicted aberration data, which is used to reduce or at least partially correct aberrations. The aberration data may be used to (at least partially) correct for aberrations in the charged particle microscope image data and / or may be used to reduce aberrations by fine-tuning the charged particle beam microscope system during image acquisition (thereby reducing aberrations in subsequently acquired images). In either case, a sample reconstruction is obtained using the acquired charged particle microscope images, the first set of aberration predictor values, and the aberration model.

[0007] The aberration predictor values ​​may include any system parameters on which aberrations are known to depend, or parameters on which aberration determinations may otherwise be predicted. For example, the aberration predictor values ​​may include two-dimensional position readings of the electron beam, timestamps, temperatures, sensor measurements from the surface of the imaging system, sample position readings (e.g., x / y / z positions and / or sample tilt), or any other environmental or operational variables that may affect the imaging system or the optical state of the system.

[0008] The optical state may include parameters that define the optical elements of the system, which may drift or vary due to, for example, the variables discussed above. Such parameters that define the optical elements of the system may include, for example, the two-dimensional or three-dimensional position of the beam, the de(focus) or magnification of the beam, the state of the magnetic or electrostatic deflectors, the state of one or more magnetic or electrostatic lenses. The optical state of the system may change by an unknown amount during a data acquisition session. However, the optical state can be fine-tuned by adjusting the settings of the magnetic or electrostatic deflectors and the settings of the magnetic or electrostatic lenses, for example by changing the supply current or voltage. Furthermore, the images acquired from the microscope column can be corrected to take into account the changing optical state of the system. By obtaining an aberration model, the workflow of the imaging or data acquisition session is made more robust against less than perfect situations, such as the changes in the parameters defined above. In particular, the parameter changes and their resulting aberrations can be more closely "tracked" over a data acquisition session without the need for additional aberration measurements, resulting in, for example, a higher resolution and / or a more accurate 3D model of the sample.

[0009] The aberration model may be any of a variety of suitable classes of models. In some examples, the aberration model may be a parametric model that has key system parameters as inputs. In some examples, the model may be a physical model, a statistical model, a linear model, a polynomial model, a decision tree, a random forest, a neural network, or a machine learning model.

[0010] The aberration model can also be described with reference to its inputs and outputs. In particular, a set of aberration predictor values ​​is input to the aberration model. More particularly, the aberration model can take any system parameters on which aberrations are known to depend as inputs, the system parameters being obtained for each of a plurality of charged particle microscope images. Predicted aberration data is then output for the aberration model. It will be appreciated that the predicted aberration data may define predicted aberrations or may define potential aberration corrections or system adjustments. In particular, the predicted aberration data may include parameters that specify the type and numerical characteristics of one or more aberrations that may be present. The characteristics may include at least one of the magnitude or direction of one or more aberrations, such as coma, or a set of values ​​indicative of the aberration characteristics of a set of identified aberrations, such as coma and spherical aberration, or any other form of data capable of characterizing one or more aberrations, so that the characterized aberrations can be reduced or corrected, for example, by applying a correction transform to the respective data acquisition and / or by applying setting adjustments to the microscope system based on the predicted aberration data in a subsequent step. In particular, the model's output predicted aberration data includes parameters that specify image corrections or setting adjustments for the microscope system, and thus the output of the aberration model may be directly used to specify actions that reduce or at least partially correct the predicted distorting effect of the aberrations. Corrections or system adjustments (e.g., corrections to the respective charged particle microscope images or adjustments to the microscope system) may be applied as appropriate to reduce the aberrations prior to obtaining the sample reconstruction.

[0011] In one or more examples, each predicted aberration data includes one or more aberration corrections for application to the charged particle microscope images, in such examples, each predicted aberration data is used to correct aberrations in the charged particle microscope images of each acquisition, and the corrected charged particle microscope images are used in the sample reconstruction.

[0012] In some examples, each predicted aberration data can characterize one or more aberrations, such as, for example, defocus, astigmatism, coma, spherical, star, three-lobe, rosette, linear distortion, pincushion distortion, barrel distortion, helical distortion, elliptical distortion, or other aberrations. In such examples, correcting the charged particle microscope image can include calculating an aberration correction for the charged particle microscope image from each predicted aberration data and correcting the charged particle microscope image using the calculated aberration correction.

[0013] In some examples, each predicted aberration data can characterize one or more aberration corrections that can be used to correct aberrations in the charged particle microscope image for sample reconstruction. In such examples, the predicted aberration data can include parameters that specify a transformation that can be used to correct aberrations in the image, including, for example, scaling, rotating and / or skewing operations, or Fourier filters. For example, the transformation can be a distortion field that corrects the aberrations. It will be appreciated that determining the corrections (in any form) for known aberrations can be accomplished using numerical methods and / or algorithms known in the art, such as those reviewed by Amit Singer et al. [URL: https: / / arxiv.org / abs / 2003.13828], which is incorporated herein by reference. Thus, in this example, using the output of the aberration model, the distorting effects of the aberrations can be corrected following data acquisition in a post-processing step. By correcting the aberrations in post-processing, the need to closely monitor the imaging system to correct the aberrations during data acquisition is reduced. Nevertheless, the image corrections can be determined on the fly on the microscope during image acquisition. In particular, one or more model fitting or image correction steps as described herein may be performed while data acquisition is ongoing, for example to limit processing latency.

[0014] In one or more examples, once the predicted aberration data is obtained, it is determined whether each predicted aberration data satisfies a condition that indicates that the charged particle microscope system needs to be fine-tuned or that there are aberrations that need to be reduced. In some examples, if the condition is met, which may be a predetermined threshold value of the aberration or adjustment, the charged particle microscope system is fine-tuned to reduce the aberration.

[0015] In some examples, the predicted aberration data may include parameters that specify adjustments to the settings of the optical system, such as adjustments to currents applied to one or more components of the optical system to fine-tune the components of the optical system and mitigate one or more potential aberrations during image acquisition. In other examples, the predicted aberration data characterizes one or more aberrations, such as defocus, astigmatism, coma, spherical, star-like, three-lobe, rosette, linear distortion, pincushion distortion, barrel distortion, helical distortion, elliptical distortion, or other aberrations. In such examples, the charged particle beam microscope system may be fine-tuned by calculating one or more adjustments for the charged particle beam microscope system from the predicted aberration data and applying the adjustments to the necessary components of the charged particle beam microscope system. It will be appreciated that if the estimated aberration values ​​are large, it may be better to apply adjustments to the optical elements of the microscope to reduce the aberrations on-the-fly during acquisition. If the aberrations are small, corrections may be more easily applied to the image, for example, to correct the aberrations in post-processing. Therefore, these techniques may be used in combination, e.g., if a condition is not met indicating that the microscope system needs to be fine-tuned, the predicted aberration data may still indicate that (small) aberrations are present that can be corrected by corrections to the corresponding acquired images / data acquisition.

[0016] In some examples, the method includes periodically inferring the aberration data while acquiring images to determine whether the adjustment condition is met, and continuing to acquire images after fine-tuning the charged particle beam microscope system. For example, the periodic aberration data inference can be after every acquisition, after every n acquisitions, or according to a rule (e.g., the periodic acquisition can be triggered by a sensor on the microscope system). After the periodic acquisition, images may continue to be acquired, but now using the adjusted microscope system, i.e., with reduced aberrations after the adjustment.

[0017] In one or more examples, the predicted aberration data may characterize or correct coma or multiple aberrations, including coma, spherical, or other.

[0018] In some examples, the aberration model may include multiple models for various aberrations. For example, the aberration model may include multiple separate models (e.g., one model per aberration) for characterizing each of the multiple aberrations. In other examples, the aberration model may be a single model with one or more outputs for each one or more aberrations. In this manner, models for different aberrations may be calculated separately and applied one by one, for example, with sequential aberration correction and / or sample reconstruction, or a joint multi-output model for multiple aberrations may be calculated and applied for a wide range of aberration mitigation and / or sample reconstruction.

[0019] In some examples, the aberration predictor values ​​include one or more temperature values ​​corresponding to the respective one or more temperature sensors, a timestamp of acquisition, an optical state of the charged particle beam microscope system, a measurable physical state of the charged particle beam microscope system, a sensor measurement from the surface of the imaging system, a sample position reading, and / or a position reading of the optical system of the charged particle microscope. By training the model on periodically acquired aberration predictor values ​​including the above, the imaging process is made more robust against temperature or other system changes over time, thereby allowing higher resolution sample reconstructions to be consistently achieved. Furthermore, the need for strict monitoring and correction of the imaging system during data acquisition is reduced.

[0020] In one or more examples, periodically acquiring the aberration measurements can include obtaining an aberration measurement for each charged particle microscope image. For example, the aberration measurement acquisition can be performed following each exposure. In this way, a first portion of the exposure is used for the acquisition of the charged particle microscope image, which can be beneficial for samples that may be radiation sensitive.

[0021] Obtaining aberration measurements for each charged particle microscope image may include obtaining one or more aberration measurements with low precision (e.g., with significant noise in the estimates of the aberration values). By fitting and applying a model when making the low precision measurements at each exposure, the noise in the measurements can be effectively averaged out, and thus the low precision measurements can be "de-noised".

[0022] In one or more examples, aberration measurements may be obtained periodically during image acquisition, for example, between 1 and 200 image acquisitions per aberration measurement, such as an aberration measurement every 20 image acquisitions.

[0023] The aberration measurements used to train the model may include one or more of one or more images acquired while varying one or more microscope parameters, one or more images that may be processed to obtain one or more aberration parameters, and / or parameters characterizing the aberrations. For example, one or more images may be acquired while varying the beam tilt. In such an example, one or more images (or a shift between them) may indicate the presence of one or more aberrations and thus may be processed to obtain one or more aberration parameters.

[0024] Parameters for characterizing aberrations are known in the art and can include, for example, Thon ring analysis. Such parameters and methods for characterizing aberrations are discussed, for example, in Barthel J, Thust A, "Aberration measurement in HRTEM: implementation and diagnostic use of numerical procedures for the highly precision recognition of diffractogram patterns", Ultramicroscopy, Volume 111, Issue 1, 2010, pages 27-46, which is incorporated herein by reference.

[0025] The parameters characterizing the aberrations may also include image shifts across the acquired images. In a particular example, a test pattern is applied to a beam tilt coil in a microscope column, and the resulting beam tilt leads to an aberration-induced image shift across the acquired images, as described, for example, in EP 4235731 A1. In this particular example, a charged particle beam microscope system is operated in a transmission imaging mode, and while the charged particle beam is directed at a sample, a time series of beam tilts is applied to the charged particle beam in a pattern to generate a series of images. At least some of the images in the series of images are captured while the charged particle beam transitions between one beam tilt in the time series of beam tilts and sequentially adjacent beam tilts in the time series of beam tilts. The series of images may be analyzed according to the methods disclosed herein to obtain a sample reconstruction. In this particular example, the images of the test pattern refer to a pattern in which the beam tilt is configured to induce an image change (e.g., image shift) in the series of images that can be correlated to optical aberrations in the microscope system. For example, the image change can be determined from a series of images and used to estimate one or more optical aberration values ​​for the microscope system, and / or the one or more optical aberration values ​​can be determined by a trained neural network that receives as input data the series of images or information derived from the series of images (e.g., the power spectrum of the images). In this particular example of aberration measurement, the beam tilt pattern is a periodic pattern, such as a Lissajous figure. The use of a periodic pattern can allow for the determination of an unknown time delay and an unknown deviation in the frequency of the applied beam tilt pattern in relation to the measured image change over time. The periodic pattern also allows for the system to be robust against other effects that may induce image changes (e.g., specimen drift and mechanical vibrations), and allows signal processing to be used to separate different signal sources.

[0026] In some instances, the sample reconstruction may be performed using single particle analysis. The sample reconstruction may include a charged particle microscopy acquisition, in which many images are acquired at different sample locations. For example, the sample reconstruction may be for an electron tomography reconstruction.

[0027] In some implementations, a charged particle beam microscope system can include one or more memories storing computer-readable instructions configured to cause one or more processors to perform operations including the methods described above.

[0028] In some implementations, the computer-readable medium includes instructions that are executed by one or more data processing devices, which, when executed, cause the respective one or more devices to perform operations including the methods described above.

[0029] In one or more implementations, a method of training an aberration model to reduce or at least partially correct aberrations in a sample reconstruction includes accessing previously acquired pairs of aberration predictor values ​​and corresponding sets of aberration measurements, and training the model using the pairs. The model takes the set of aberration predictor values ​​as inputs and outputs predicted aberration data. In some examples, the aberration model can be used to obtain a reconstruction or improved resolution of an imaged sample to reduce or at least partially correct aberrations in a charged particle microscope image. Training the model includes providing the acquired set of predictor values ​​as inputs to the aberration model, obtaining an output of the aberration model in response to the set of predictor values, and comparing the output of the model to a corresponding known aberration measurement for each input set of predictor values. The input aberration predictor values ​​include one or more temperature values ​​(each temperature value corresponding to a respective one or more sensors or different acquisition times), a time to obtain the aberration measurement, a sample position, an optical state of the charged particle beam microscope system, a measurable physical state of the charged particle beam microscope system, and / or a position of the optical system of the charged particle beam microscope system. The parameters of the model are then updated and / or fine-tuned to reduce the error between the output values ​​and the known aberration data. The training can include using the aberration measurements to train the model such that the predicted aberration data characterizes the aberrations, or alternatively, the training can include determining parameters that define corrections or adjustments to the charged particle beam microscope system that at least partially correct the aberrations in the image to reduce the aberrations corresponding to the aberration measurements, and training the model using the determined parameters such that the predicted aberration data characterizes the corrections and / or adjustments. Various methods of training a model are known in the art and are suitable for the methods disclosed herein. For example, the model can be a deep learning model, such as an artificial neural network (ANN).The ANN can be trained using gradient descent, stochastic gradient descent, or other optimization algorithms such as ADAM, as are well known in the field of ANNs. The model may also be any other parametric model, such as a physical model of aberrations, a polynomial model, etc., and the parameters are updated to reduce the error using any suitable optimization routine, including those described above.

[0030] In one or more implementations, a method of acquiring a plurality of charged particle microscope images for a sample reconstruction includes acquiring a plurality of charged particle microscope images for a plurality of regions of a sample. The method includes acquiring a respective first set of aberration predictor values ​​for each of the plurality of charged particle microscope images, such as, for example, a temperature of an optical system of a charged particle beam microscope system, a position and / or a configuration of an optical system of a charged particle beam microscope system. The sample reconstruction is obtained using the acquired charged particle microscope images. In these implementations, a model is pre-trained and / or a pre-trained model is used for image reconstruction and / or charged particle microscope system adjustment without necessarily obtaining or updating the model using the images acquired for the reconstruction. In this manner, obtaining the aberration model may only include retrieving the trained aberration model from a memory. The examples described above are equally applicable to these implementations in a similar manner.

[0031] The subject matter is described with implementations and examples. In some cases, as will be recognized by those skilled in the art, the disclosed implementations and examples may be practiced without requiring one or more of the specific details disclosed, or may be practiced using other methods, structures, and materials not specifically disclosed herein. All implementations and examples described herein and shown in the drawings may be combined without any restriction to form any number of combinations, unless the context clearly dictates otherwise, such as when the proposed combination includes elements that are incompatible or mutually exclusive. The sequential order of acts in any process described herein may be rearranged, unless the context clearly dictates otherwise, such as when one act requires the result of another act as an input.

[0032] For brevity and continuity of description, the same or similar reference numbers may be used for the same or similar elements in different figures, and the description of an element in one figure is considered to be carried forward when that element appears in another figure with the same or similar reference number. In some cases, the term "corresponding to" may be used to describe the correspondence between elements in different figures. In exemplary usage, when an element in a first figure is described as corresponding to another element in a second figure, the element in the first figure is considered to have the characteristics of the other element in the second figure, and vice versa, unless otherwise noted.

[0033] The word "comprise" and its derivatives such as "comprises" and "comprising" should be interpreted in an open and inclusive sense, i.e., "including but not limited to." Similarly, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. The term "and / or," when used between the last two elements of a list of elements, means any one or more of the listed elements. The term "or" is generally used in its broadest sense, i.e., to mean "and / or" unless the context clearly dictates otherwise. When used to describe a range of dimensions, the phrase "between X and Y" represents a range that includes X and Y. As used herein, "apparatus" may refer to any individual device, a collection of devices, a portion of a device, or a collection of portions of a device.

[0034] 1 illustrates a system 100 according to one implementation that can measure and reduce or partially correct optical aberrations in a charged particle beam microscope system while the charged particle beam microscope system is operating in a transmission imaging mode. The system 100 is illustrated in the context of an exemplary charged particle beam microscope system 104. However, the system 100 is not limited to the particular configuration of the charged particle beam microscope system 104 shown in FIG. 1 and can be used with any charged particle beam microscope system, such as a system operating in a scanning imaging mode.

[0035] In the illustrated example, the charged particle beam microscope system 104 includes a charged particle beam microscope column 120 (hereinafter microscope column 120) enclosed within a vacuum chamber 106. The microscope column 120 defines an optical axis 116 of the charged particle beam microscope system 104.

[0036] The sample 124 to be interrogated can be positioned along the optical axis 116. The sample 124 can be supported by a sample holder 140, which in some examples can have the ability to translate, rotate, and / or tilt the sample 124 relative to the optical axis 116. The sample holder 140 can allow different regions of the sample 124 to be positioned relative to the optical axis 116 for interrogation.

[0037] At the top of the microscope column 120 is a charged particle source 108 (e.g., a thermionic electron source, a Schottky emission source, a field emission source, etc.) that emits a charged particle beam 112 (e.g., an electron beam). The charged particle beam 112 is directed along an optical axis 116 so that it can impinge on and interact with a sample 124. The charged particle beam 112 passes through the sample 124 to generate an image.

[0038] A camera 136 at the bottom of the microscope column 120 can be operated to capture images of the sample 124. The camera 136 can include, for example, a CCD (charge-coupled device) imaging sensor, a CMOS (complementary metal-oxide semiconductor) imaging sensor, or more generally, an array of photodetectors. In one example, the camera 136 can be operable in a "movie" mode to capture a series of images of the sample.

[0039] The microscope column 120 may include various charged particle beam lens systems (hereinafter lens systems) that direct the charged particle beam 112 along an optical axis 116 and configure the charged particle beam 112 to provide constant illumination covering a desired field of view on the sample 124. In some examples, the various lens systems may also project an image generated by the interaction of the charged particle beam 112 with the sample 124 into the field of view of the camera 136.

[0040] In some examples, the various lens systems may include a condenser lens system 122, an objective lens system 126, and a projector lens system 130, which may have any suitable configuration for modifying the charged particle beam 112 to provide a desired field of view. The condenser lens system 122 may be configured to focus the charged particle beam 112 into a substantially parallel beam. The objective lens system 126 may be configured to focus an image formed by the transmission of the charged particle beam 112 through the sample 124. The projector lens system 130 may be configured to project the image coming from the objective lens system 126 into a field of view of the camera 136.

[0041] In some examples, the condenser lens system 122 can include condenser lenses 122a, 122b and other components such as a condenser stigmator 122c (i.e., a device that corrects axial astigmatism) and a condenser aperture 122d.

[0042] In some examples, the objective lens system 126 can include an objective lens 126a and other components, such as a beam deflector 126b (which can be used to center the beam), an objective stigmator 126c (i.e., a device that corrects axial astigmatism), and an objective aperture 126d. In the illustrated example, the sample 124 is positioned above the objective lens 126a (in other examples, the sample 124 can be inserted into the objective lens 126a).

[0043] In some examples, projector lens system 130 can include, for example, intermediate lens 130a (e.g., for magnifying the image) and projector lenses 130b, 130c. Projector lens system 130 can include other components such as a stigmator, an image deflector, and an aperture.

[0044] The components of the lens system may be electro-optical components that are fine-tuned by varying the current applied to the components. For example, the various lenses of the lens systems 122, 126, 130 may be electromagnetic lenses. Electromagnetic lenses modify the charged particle beam by a magnetic field generated by a solenoid. By varying the current to the solenoid, the magnetic field can be changed, which correspondingly changes the focal length and magnification of the lens. Other components of the lens systems 122, 126, 130, such as the stigmators and beam deflectors, may be electromagnetic devices that can be fine-tuned in the same manner as described for the electromagnetic lenses. The ability to fine-tune the components of the lens systems 122, 126, 130 by varying the current supplied to the components may allow aberration corrections to be applied to the microscope column 120 on the fly (e.g., while the charged particle beam microscope system 104 is operating in a transmission imaging mode).

[0045] The lens systems 122, 126, 130 may include other optical components or different configurations of optical components not shown in Figure 1. The microscope column 120 may include other optical components near the output of the charged particle source 108. For example, the microscope column 120 may include an accelerator stack that can be used to increase the acceleration of the charged particle beam 112 (e.g., prior to focusing the charged particle beam 112).

[0046] In some specific examples relating to measuring aberrations using images of a test pattern, the system 100 can include a pattern generator 144 that can generate various patterns. The pattern generator 144 can be, for example, a programmable function generator. In some examples, the pattern generator 144 can be configured to generate a periodic pattern. In one example, the periodic pattern can be a Lissajous figure. The Lissajous figure is given by the following parametric equation:

[0047]

number

[0048] A Lissajous figure can be as simple as a circle (e.g., the parametric equations (1) and (2) give a circle when a / b=1, A=B, and d=p radians) or it can be more complex (e.g., the ratio a / b does not equal 1).

[0049] In such an example including a test pattern, the system 100 may include a beam deflector 134 inserted into the microscope column 120. The beam deflector 134 may be controlled according to the pattern to tilt the charged particle beam 112 in the X and Y directions over time. The beam deflector 134 may apply a tilt motion to the charged particle beam 112 before it reaches the sample 124 (e.g., before focusing the charged particle beam 112). The beam deflector 134 may be an electro-optical deflector (e.g., an electrostatic or magnetic beam deflector) that allows changing the tilt angle applied to the charged particle beam 112 by changing the current to the beam deflector.

[0050] The beam deflector 134, operating according to a pattern, applies a time sequence of beam tilts to the charged particle beam 112. Each beam tilt generates an image. Thus, the time sequence of beam tilts results in a sequence of images. Image shifts between successive (or adjacent) images in the sequence can indicate optical aberrations in the microscope system.

[0051] The system 100 may include a frame grabber 148 in communication with the camera 136. The frame grabber 148 may, for example, form an interface between the camera 136 and a computing system 154 (e.g., a server). The frame grabber 148 is an electronic device that may capture individual image frames from the stream of image data generated by the camera 136.

[0052] In such an example of an image shift method for obtaining aberration measurements, the system 100 may include an image shift tracker 152 in communication with the frame grabber 148. The image shift tracker 152 may receive image frames from the frame grabber 148 and measure (or calculate) the image shift between successive image frames over time. The image shift tracker 152 may use any suitable image processing technique to measure the image shift. One exemplary technique is cross-correlation. In one example, the image shift tracker 152 may be executed by a processor (e.g., a GPU, a CPU, or an FPGA) of the computing device 154.

[0053] The computing system 154 also obtains or otherwise receives aberration predictor values ​​corresponding to the image frames received from the frame grabber 148. The aberration predictor values ​​are preferably received with each of the multiple images acquired by the frame grabber and may refer to any parameter on which the aberrations may depend or on which the aberration determination may otherwise be predicted. For example, the aberration predictor values ​​may refer to two-dimensional position readings of the image beam shift, a timestamp, temperature, sensor measurements from the surface of the imaging system, or any other environmental or operational variables that may affect the imaging system or optical conditions. Any of these key parameters are input to the computing system 154 with each received image frame.

[0054] The system 100 can include an aberration calculator 156 that determines values ​​of optical aberrations in a charged particle beam microscope system. The optical aberrations can be determined, for example, from Thon ring analysis, or from image shifts measured from a series of images acquired while varying the beam tilt using a pattern, from one or more parameters that characterize the aberrations, or from one or more images that can be processed to obtain the aberration parameters. Examples of optical aberrations that can be seen and measured in a charged particle beam microscope include defocus, two-fold astigmatism, three-fold astigmatism, four-fold astigmatism, five-fold astigmatism, six-fold astigmatism, axial coma, fifth-order axial coma, spherical aberration, sixth-order spherical aberration, stellate aberration, sixth-order stellate aberration, three-lobe aberration, rosette aberration, linear distortion, pincushion distortion, barrel distortion, helical distortion, and elliptical distortion. The aberration calculator 156 can be configured to determine values ​​of any combination of these optical aberrations as well as other optical aberrations not specifically described.

[0055] In one example, the aberration calculator 156 can receive data acquisition or charged particle microscope images from the computing system 154 and measure the optical aberrations in the microscope column 120 based thereon. In one example, the aberration calculator 156 can estimate the optical aberrations from an image shift tracker 152 that fits the image shift to an optical aberration function that provides a relationship between parameters of the image shift, e.g., test pattern image shift, beam tilt, and the optical aberrations in the microscope column 120. In some examples, the optical aberration function can be extended with one or more auxiliary parameters that are also fitted.

[0056] In one example, the aberration calculator 156 can receive image data directly from the camera 136 or image frames from the frame grabber 148. The aberration calculator can process the image data to measure aberrations using any of a number of techniques known in the art, for example, J. Barthel, A. Thust, Aberration measurement in HRTEM: Implementation and diagnostic use of numerical procedures for the highly precise recognition of diffractogram patterns, Ultramicroscopy, Volume 111, Issue 1, 2010, pages 27-46.

[0057] The aberration calculator 156 fits an aberration model to predict aberration data according to the methods described herein. With reference to FIG. 1, the aberration calculator 156 uses a set of aberration predictor values ​​from the computing system 154 obtained during data acquisition and known corresponding aberration measurements determined by the aberration calculator 156. Specifically, the aberration calculator 156 fits a model (e.g., a parametric model) that takes as input the set of aberration predictor values, as obtained, and outputs predicted aberration data. The predicted aberration data may be values ​​indicative of the corresponding aberrations or may be values ​​that parameterize the corrections applied to the acquired image.

[0058] The system 100 may include an aberration corrector 168 communicatively coupled to the aberration calculator 156. The aberration corrector 168 takes the predicted aberration data and corrects the received image and / or adjusts the instruments in the optical system to obtain a sample reconstruction from the (corrected) image. For example, the predicted aberration data may specify a correction (e.g., image data correction and / or system adjustment), and the aberration corrector 168 applies the correction and / or system adjustment. Alternatively, the predicted aberration data may indicate or characterize one or more predicted aberrations, and the aberration corrector 168 receives the predicted aberration data from the aberration calculator 156 and determines therefrom a correction or adjustment for the associated one or more aberrations. Once a correction or adjustment is determined, the aberration corrector 168 applies the correction to the respective image data or optical system (i.e., microscope column). For example, the aberration corrector 168 may apply adjustments to components of the microscope column 120 (e.g., components of a lens system) to reduce aberrations based on the predicted aberration data determined by the aberration calculator 156. Specifically, if the components are electro-optical components, the aberration corrector 168 may modify currents applied to the components to change how the components modify the charged particle beam 112. In some examples, the aberration corrector 168 may apply adjustments to components of the microscope column 120 based on a predetermined condition (e.g., if the optical aberrations exceed a predetermined threshold or if the confidence in the estimated aberration values ​​exceeds a predetermined threshold (e.g., if the absolute value of the estimated aberration values ​​is less than a confidence interval for that optical aberration)).

[0059] In some examples, the aberration calculator 156 and the aberration corrector 168 can be programs or scripts running on the computing system 154. The aberration corrector 168 can be in communication with the aberration calculator 156. In some examples, the computing system 154 can be coupled to a display 164 (e.g., a console of the charged particle beam microscope system 104). In some examples, the computing system 154 can present current aberration values ​​and aberration trends on the display 164.

[0060] The aberration calculator 156 and the aberration corrector 168 can be implemented in software and / or hardware. The software components of the aberration calculator 156 and the aberration corrector 168 can be stored in one or more computer-readable storage media or devices and executed by one or more processors (e.g., processors in the computing system 154). Any of the computer-readable media herein can be non-transitory (e.g., volatile memory such as DRAM or SRAM, non-volatile memory such as magnetic storage, optical storage, etc.) and / or tangible. It will be understood that the system 100 can include one or more computing systems. For example, different programs or scripts of the methods disclosed herein (e.g., the aberration calculator 156 and the corrector 168 of the computing system 154) can be shared across multiple computing systems (or servers) communicatively coupled to each other to individually execute the different programs or scripts.

[0061] 2 is a flow diagram illustrating an exemplary method 200 of measuring optical aberrations in a charged particle beam microscope system (e.g., the charged particle beam microscope system 104 illustrated in FIG. 1) using a time series of beam tilts in a pattern and acquiring a corresponding series of microscope images. It will be understood that this is one way of measuring values ​​characterizing the aberrations, and that other measures for making such measurements may alternatively be used in the disclosed methods of aberration correction, and in particular in fitting or training an aberration model. Although the operations are illustrated in FIG. 2 once each and in a particular order, the operations may be suitably reordered and / or repeated as necessary (e.g., different operations performed may suitably be performed in parallel).

[0062] At 210, the method includes operating a charged particle beam microscope system in an imaging mode (e.g., transmission imaging). Operation 210 can include generating a charged particle beam (e.g., an electron beam) using a charged particle source at the top of a microscope column and directing the charged particle beam to a sample using optical components in the microscope column. The charged particle beam interacts with the sample to generate an image. Operation 210 can include projecting the image into a field of view of a camera at the bottom of the microscope column.

[0063] At 220, with the charged particle beam microscope system operating in a transmission imaging mode as described in operation 210, the method can include applying a time sequence of beam tilts of a pattern to the charged particle beam emerging from the charged particle source. Operation 220 can include generating a pattern. Operation 220 can include applying a time sequence of beam tilts by traversing the pattern. At each point on the traversal path, the x,y coordinates of the point determine an amount of tilt applied to the charged particle beam in the X and Y directions. The amount of tilt can be used to control a beam deflector inserted in the microscope column to tilt the beam. In some examples, the pattern can be a periodic pattern. In some examples, the periodic pattern can be a Lissajous figure (or Lissajous curve).

[0064] At 225, while applying beam tilt to the charged particle beam as described in operation 220, the method may include operating the camera in a video mode to capture images of the sample. For every beam tilt, the camera may obtain a complete image of the sample. Thus, the camera may capture a series of images corresponding to applying a time series of beam tilts to the charged particle beam. The time between the application of the beam tilt and the actual tilt of the beam may vary such that there is an unknown time delay between the beam tilt pattern and the captured series of images. In some examples, the optical aberration function may incorporate the unknown time delay as an auxiliary variable to be fitted. Other auxiliary variables, such as the beam tilt pattern or sample drift, may also be incorporated into the optical aberration function.

[0065] At 230, the method can include obtaining an aberration measurement for the optical aberration in the microscope column from the images captured in operation 225. In one example, the optical aberration can be estimated from an optical aberration function that relates the image shift to the beam tilt and optical aberration of the microscope column. In another example, the optical aberration can be obtained by optimizing a function that provides a relationship between the series of images and the optical aberration in the microscope column. In another example, the optical aberration can be obtained as an inference of a trained neural network. The inference can include the prediction of the neural network as well as other information such as a confidence in the prediction.

[0066] The following equation (3) is an example of an optical aberration function. For illustrative purposes, equation (3) can obtain values ​​for defocus, astigmatism (astigX, astigY), coma (comaX, comaY), three-fold astigmatism (threefoldX, threefoldY), spherical aberration (spherical), star aberration (starX, starY), and four-fold astigmatism (fourfoldX, fourfoldY).

[0067]

number

[0068] The vector on the right hand side of the equation contains the values ​​of the optical aberrations for a given state of the optical system of the microscope system at a certain moment. When the beam tilt is adjusted by (tx, ty) radians, the induced image shift is given by (shiftX, shiftY), which can be measured from the image captured while applying the beam tilt (e.g., in operation 240). The measured shiftX, shiftY and the known tx, ty can be fitted to the function of equation (3) to obtain an estimate of the optical aberrations. The parameters tx, ty are time dependent, allowing the unknown time delay between the applied beam tilt pattern and the induced image shift pattern to be incorporated into the function for the fit. The variation of the optical aberration function can incorporate other auxiliary parameters such as the beam tilt pattern and / or sample drift.

[0069] Operations 220-230 can be repeated each time an aberration measurement is scheduled during the planned acquisition or each time a predetermined criterion for an aberration measurement is met during the planned acquisition. The aberration measurements over time can be displayed and monitored on a screen.

[0070] FIG. 3 illustrates an exemplary workflow for acquiring a sample, e.g., an acquisition of a sample containing instances of particles to be reconstructed. The workflow is part of a larger (automated or manual) workflow in which multiple (programmed or dynamically changing) sample positions are navigated with the goal of acquiring a high-resolution sample reconstruction. In 301, the method includes moving to a new region of interest on the sample. With reference to FIG. 1, it will be understood that moving to a new region of interest on the sample refers to moving to a new region along the optical axis of the charged particle beam microscope system. Various specific workflows for moving from a first region of interest to a next region of interest on the sample are known in the art and can be used to implement the methods described herein. For example, step 301 can include translating and / or tilting the sample 124 and / or the beam 112 relative to the optical axis 116 to a first or another region of interest on the sample 124. In some examples, the beam remains stationary during data acquisition and a grid containing the sample is shifted or translated to facilitate an image of the new region of interest.

[0071] In step 302, the method includes evaluating whether a new aberration acquisition is necessary, i.e., whether criteria for a new aberration measurement are met. Criteria for a new aberration measurement may include, for example, when another measurement is scheduled (such as a defocus measurement), when the last measurement is a specified amount of time ago, when more information about the aberrations in another region of the two-dimensional optical axis is acquired, or when a change in one or more environmental or operational variables is detected, for example, when a temperature change is detected, when a change in the sample position (e.g., a change in the relative height of the sample) is detected, etc. It will be understood that step 302 may also refer to only a single criterion. The new aberration acquisition may include an image of a test pattern having distortions indicative of the aberrations (e.g., using the exemplary method illustrated in FIG. 2), one or more images that may be processed to obtain one or more aberration parameters, and / or parameters characterizing the aberrations as discussed above.

[0072] If it is assessed that a new aberration acquisition is not required, the method proceeds to step 303. In step 303, the method includes acquiring a data acquisition for use in reconstructing an image or model of the particle and acquiring one or more aberration predictor values. For example, the data acquisition may include a low-dose high-resolution image acquisition of a selected region of the sample, and the predictor value may include a temperature measurement. The one or more predictor values ​​are stored in association with the data acquisition (e.g., in metadata for the data acquisition) in data acquisition storage 307.

[0073] If a new aberration acquisition is due, the method proceeds to step 304, which includes acquiring an aberration acquisition to measure one or more aberrations and acquiring one or more associated predictor values ​​for the aberrations. Both acquisitions are stored in the aberration acquisition storage 306. Optical aberrations may be measured using a relatively small amount of data (e.g., defocus) or a relatively large amount of data may be required for proper measurement (e.g., coma and astigmatism). The one or more predictor values ​​are stored in the data acquisition storage 306 in association with the aberration acquisition (e.g., in the metadata of the aberration acquisition). It will be appreciated that the metadata of the data acquisition may also include other values ​​that are not necessarily related to aberrations but may otherwise be related to good sample reconstruction, such as high pressure values, pixel size, or other system state parameters.

[0074] The stored aberration predictor values ​​associated with the acquisition of steps 303 and 304, respectively, refer to any parameters on which aberrations may depend or on which aberrations may be predicted. For example, the aberration predictor values ​​may refer to two-dimensional position readings of the image beam shift, a timestamp, temperature, a sensor measurement from the surface of the imaging system, or any other environmental or operational variables that may affect the imaging system, sample position, or optical conditions.

[0075] Steps 303 and 304 may occur simultaneously or sequentially. For example, if criteria are met for a new aberration measurement, a new data acquisition may be performed in parallel with the aberration determination acquisition, for example using one and the same exposure. In some examples, the aberration measurement in step 304 may be performed subsequent to the data acquisition in step 303. In this way, a first portion of the exposure is used for the acquisition of a charged particle microscope image, which is beneficial for samples that are radiation sensitive.

[0076] Once a particular area of ​​the sample has been visited, the method proceeds to step 308. Method 308 includes checking whether all areas of interest on the sample have been visited. If no, the method returns to method step 301, i.e. moves to the next area of ​​interest on the sample and repeats the workflow as described above. If all areas of interest on the sample have been reached, the sample acquisition run ends.

[0077] 4a illustrates an exemplary workflow for deriving an aberration model from stored pairs of aberration acquisitions and acquired aberration predictor values ​​and using the model to reduce or at least partially correct aberrations. In step 405, the aberration acquisition storage device 306 is accessed and one or more aberration measurements are determined from each stored aberration acquisition. For example, the aberration measurements may be values ​​or qualification requirements of coma aberration present in each image. Methods for determining aberration measurements from aberration acquisitions are known in the art, and one particular exemplary method is described above and illustrated in FIG.

[0078] In step 406, a model is fitted using pairs of aberration measurements and corresponding aberration predictor values ​​to predict aberration measurements from the aberration predictor values. The method includes initializing a model, which may be, for example, a parametric model, a physical model, a statistical model, a linear model, a polynomial model, a decision tree, a random forest, a neural network, or a machine learning model. The model takes a set of aberration predictor values ​​as input and outputs predicted aberration measurements. The model is trained by obtaining predicted aberration measurements from the aberration predictor values, comparing the predicted measurements with the actual aberration measurements, and adjusting the parameters of the model to reduce discrepancies between the predicted measurements and the actual measurements. Various methods of training a model are known in the art and may be used to implement the methods disclosed herein. For example, the parameters of the model may be trained using a suitable optimization technique, such as gradient descent.

[0079] Once the model has been fitted, the data acquisition storage 307 is accessed in step 407 and the predictor values ​​acquired during the acquisition are input into the model for inference to obtain aberration measurements for each acquisition. For example, the aberration model may infer that 10 μm coma aberration is present from a given set of aberration predictor values. In step 408, corrections to the aberration measurements output by the model are determined for each data acquisition, respectively. Determining corrections for a given aberration measurement is known in the art and may include, for example, a Fourier filter to correct for phase flips and / or phase changes caused by the predicted aberration measurement, a transformation or distortion field to correct distortions caused by the predicted aberration measurement, or fine tuning adjustments to the microscope column as described in more detail below with reference to FIG. 5. In 410, the data corrections are applied to each data acquisition in a final step 410 and the data is reconstructed to obtain a full resolution, model, or image of the particle.

[0080] FIG. 4B illustrates an alternative for obtaining an aberration model. Step 401 is the same as step 405 described in FIG. 4A. In step 402, an aberration correction is determined from the aberration measurements obtained in step 401 for each aberration acquisition using methods known in the art. Corresponding pairs of one or more parameter values ​​defining the aberration correction for each aberration acquisition and corresponding aberration predictor values ​​are obtained. An aberration model in step 403 is adapted using the pairs obtained from step 402. During training, the output aberration correction parameters output by the model are compared with the corresponding known aberration correction parameters obtained in step 402. The parameters of the model are then updated to reduce the error between the output correction parameters and the known aberration correction parameters. It will be appreciated that the inputs and outputs of the model of FIG. 4b will necessarily differ from the model of FIG. 4a, but otherwise the model may be the same as described above.

[0081] In step 404, the data acquisition storage 307 is accessed and a set of predictor values ​​for each reconstruction acquisition are input into the model for inference to obtain the respective aberration corrections. The inferred corrections are applied to each data acquisition and the corrected reconstruction acquisitions are used to reconstruct an image or model of the particle in a final step 410.

[0082] The workflows of Figures 3 and 4 may be performed individually or simultaneously, and may be run independently or interdependently. In some examples, the workflow of Figure 3 may be performed first to obtain image acquisition and aberration data, which are then fed through the steps of Figure 4a or Figure 4b to fit an aberration model for aberration correction and apply it to data acquisition for reconstruction. In this way, the distorting effects of aberrations during data acquisition can be corrected in a post-processing workflow, eliminating the need to correct aberrations on-the-fly while images are being acquired. In other examples, the fitting and correction workflows of Figures 4a or 4b may be performed on-the-fly during data acquisition, for example to limit processing latency.

[0083] FIG. 5 illustrates an exemplary workflow for deriving an aberration model from stored pairs of aberration acquisitions and acquired aberration predictor values ​​and using the model to fine-tune a charged particle beam microscope system to mitigate aberrations. Specifically, the workflow of FIG. 5 involves determining adjustments to be applied to the optical column of a charged particle beam microscope system to reduce one or more aberrations during an acquisition session (i.e., prior to sample reconstruction). The workflows of FIG. 3 and FIG. 5 are run substantially simultaneously in some implementations to provide output adjustments to the elements of the microscope to reduce aberrations during image acquisition. In this way, a model is obtained and relevant data is processed "on the fly" to achieve a suitable predictive performance of the model, as measured, for example, by an RMS error determined on test data not used to train the model, once sufficient data has been collected. In some embodiments, the model is pre-trained and loaded at the start of the workflow to enable on-the-fly correction in advance or from the beginning.

[0084] The steps of the workflow of FIG. 5 may also be combined, shared, or otherwise run simultaneously with the workflow of FIG. 4a or FIG. 4b, so that aberrations can be addressed both by corrections to the acquired images and by adjustments to the microscope system. Specifically, for example, if the predicted aberrations determined in steps 407 or 404 of FIG. 4a and FIG. 4b, respectively, satisfy conditions that indicate that the charged particle microscope system needs to be fine-tuned or that the predicted aberrations can be more optimally reduced or corrected by adjustments to the microscope column (not just data processing), the workflow can jump from FIG. 4 to FIG. 5, whereby the subsequent correction step can include fine-tuning the charged particle microscope system. In the example of the operational cooperation of FIG. 4 and FIG. 5, it will be appreciated that adjustments to the microscope can result in sudden changes in the expected aberration values ​​that should be taken into account in the fitted model of FIG. 4a or FIG. 4b. This can be done, for example, by including a step function over time in the aberration model. Alternatively, two separately fitted aberration models can be obtained and applied respectively before and after the microscope change.

[0085] In some examples, the workflow of FIG. 4a or FIG. 4b may be combined with the workflow of FIG. 5 by using two models, e.g., by running in parallel, or by running one model on-the-fly and the other in post-processing. The two models may correspond to each respective type of aberration prediction described herein. For example, in examples where the models directly predict image correction and / or system adjustments, the first model may correspond to the workflow of FIG. 4b and infer aberration corrections for data acquisition, and the second model may correspond to the workflow of FIG. 5 and infer system adjustments for application to a charged particle microscope system. Alternatively, in some examples, the method includes only one model, e.g., a model that infers one or more aberrations present in the aberration acquisition, and the method runs the workflows of FIG. 4a and FIG. 5 using the same model. Other operational combinations of the workflows of FIG. 3, FIG. 4a, FIG. 4b, and FIG. 5 using one or more models run in parallel, on-the-fly, or in post-processing are equally contemplated and encompassed by the present disclosure.

[0086] Step 502 may be the same or similar to steps 401 and 405 of Figures 4a and 4b. In particular, aberration data (e.g., characterizing the type and characteristics of one or more aberrations present, or characterizing adjustments to the charged particle beam microscope system to reduce the present aberrations) is determined from the stored aberration acquisitions using techniques known in the art. In step 504, an aberration model is fitted using the aberration data and aberration predictor value pairs of each corresponding aberration acquisition. The training of the model may be similar to that described in Figures 4a and 4b. During training, the predicted aberration data output by the model is compared to the corresponding known aberration data obtained in step 502. The parameters of the model are then updated to reduce the error between the output predicted aberration data and the known (measured) aberration data. The model and the model training process may be shared between the workflows of Figures 4 and 5 where the same type of aberration data is predicted, e.g., aberration data identifying aberrations.

[0087] In step 506, each data acquisition is input into the model for inference to obtain predicted data for aberrations present in the data acquisition. In step 508, it is determined whether the predicted aberration data output by the model for each data acquisition meets certain criteria or conditions that indicate that the charged particle beam microscope system needs to be fine-tuned or the aberrations need to be reduced. For example, the predicted aberration data can include an estimated quantification of the type of aberrations present in the data acquisition. Alternatively, the predicted aberration data can define adjustments (e.g., changes in voltage or current) to be applied to one or more elements of the charged particle beam microscope system to reduce the aberrations. The criteria can include determining whether the predicted aberration data is within a confidence interval of the predicted aberrations and / or whether the absolute value of the predicted aberrations or the absolute value of the adjustments exceeds a predetermined threshold. The predetermined threshold can refer to, for example, a maximum value of the aberration that can be sufficiently corrected in the image data using the workflow of FIG. 4a or FIG. 4b, or a value of the aberration above which the aberrations are better mitigated by adjustments to the microscope system.

[0088] In step 510, if the predicted aberration data exceeds a predetermined condition, it is determined that the charged particle beam microscope system needs to be fine-tuned. If the output predicted aberration data defines parameters of predicted aberrations in the image, corresponding adjustments to be applied to the charged particle beam microscope system are determined and then applied to the elements of the microscope system to mitigate the aberrations. Similarly, if the output predicted aberration data instead defines predicted adjustments to apply to the components of the microscope system, the adjustments are applied. By running the steps of FIG. 5 in parallel with the data acquisition session (e.g., FIG. 3), the charged particle beam microscope system can be continuously monitored and adjusted to mitigate aberrations, leading to improved images during acquisition with reduced aberrations and higher resolution sample reconstructions. Furthermore, by running the steps of FIG. 5 in parallel with the steps of FIG. 4, aberrations can be addressed in both the microscope system and the image. For example, if the magnitude of the estimated aberrations is less than the predetermined condition of FIG. 5, the aberrations can instead be more optimally corrected by corrections to the image acquisition data, for example using method steps 408 and / or 410 of FIG. 4a or FIG. 4b.

[0089] 6 is a block diagram of a generalized exemplary computing system (or computing environment) that may be used in the methods described herein. In some examples, the methods may use a single computing system. In other examples, the methods may use multiple computing systems that may communicate over a network.

[0090] Referring to Figure 6, a computing system 700 includes one or more processing units 710, 715 and memory 720, 725. In Figure 6, this basic configuration 730 is included within a dashed line. The processing units 710, 715 execute computer-executable instructions. A processing unit may be a general purpose central processing unit (CPU), an application-specific integrated circuit (IC), or a combination of both. The tangible memory 720, 725 may be a processor in a general-purpose integrated circuit (ASIC), or any other type of processor. In a multi-processing system, multiple processing units execute computer-executable instructions to increase processing power. For example, FIG. 6 shows a central processing unit 710 as well as a graphics processing unit or co-processing unit 715. The tangible memory 720, 725 may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two accessible by the processing units. The memory 720, 725 stores software 780 that implements one or more innovations described herein in the form of computer-executable instructions suitable for execution by the processing units. The software 780 may include, for example, one or more of the logic of the aberration calculator 156, the aberration corrector 168, or the methods described herein.

[0091] A computing system may have additional features. For example, computing system 700 may include a storage device 740, one or more input devices 750, one or more output devices 760, and one or more communication connections 770. An interconnection mechanism (not shown), such as a bus, controller, or network, interconnects the components of computing system 700. Typically, operating system software (not shown) provides an operating environment for other software executing within computing system 700 and coordinates the activities of the components of computing system 700.

[0092] Tangible storage 740 may be removable or non-removable and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other medium that can be used to non-transitoryly store information and that can be accessed within computing system 700. Storage 740 stores instructions for software 780 that implements one or more of the innovations described herein.

[0093] The input device 750 may be a keyboard, a touch input device such as a mouse, a pen, or a trackball, a voice input device, a scanning device, or another device that provides input to the computing system 700. The output device 760 may be a display, a printer, speakers, a CD writer, or another device that provides output from the computing system 700.

[0094] The communications connection(s) 770 enable communication over a communications medium to another computing entity. The communications medium conveys information such as computer-executable instructions, audio or video input or output, or other data in a modulated data signal. A modulated data signal is a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, the communications medium may use electrical, optical, RF, or other carriers.

[0095] In consideration of the above-described implementations of the disclosed subject matter, the present application discloses the following additional examples: It should be noted that one feature of the example alone, or one or more features of the example taken in combination, and optionally in combination with one or more features of one or more additional examples, are further examples that are also included within the scope of the present application disclosure.

Claims

1. 1. A method for acquiring charged particle microscope images for sample reconstruction, comprising: acquiring a plurality of charged particle microscope images of a plurality of regions of a sample using a charged particle beam microscope system; obtaining, for each acquisition of the plurality of charged particle microscope images, a respective first set of one or more aberration predictor values; periodically acquiring a corresponding second set of aberration measurement values ​​and aberration predictor values ​​while acquiring the plurality of charged particle microscope images; using the aberration measurements and the corresponding second set of aberration predictor values ​​to obtain an aberration model, the aberration model taking as input the set of aberration predictor values ​​and outputting predicted aberration data; applying the aberration model to the first set of aberration predictor values ​​to obtain respective predicted aberration data; using the predicted aberration data to reduce or partially correct aberrations in the charged particle microscope image; and and obtaining a sample reconstruction using the acquired charged particle microscope image.

2. correcting the charged particle microscope image for each acquisition using the respective predicted aberration data; and using the corrected charged particle microscope image for sample reconstruction; The method of claim 1 further comprising:

3. the respective predicted aberration data characterizing one or more aberrations and correcting the charged particle microscope image; calculating an aberration correction for said charged particle microscope image from said respective predicted aberration data; and correcting the charged particle microscope image using the calculated aberration correction. The method of claim 2.

4. The method of claim 2 , wherein the respective predicted aberration data includes an aberration correction for application to the charged particle microscope image.

5. determining whether the predicted aberration data meets a condition indicating that the charged particle beam microscope system needs to be fine-tuned; in response to determining that the condition is satisfied, fine-tuning the charged particle beam microscope system to reduce aberrations; and The method of claim 1 further comprising:

6. the predicted aberration data characterizing one or more aberrations and fine-tuning the charged particle beam microscope system; calculating adjustments of the charged particle beam microscope system from the predicted aberration data; and and fine-tuning the charged particle beam microscope system using the adjustment. The method according to claim 5.

7. The method of claim 5 , wherein the predicted aberration data includes fine-tuning adjustments to the charged particle beam microscope system.

8. 6. The method of claim 5, wherein the method includes periodically predicting aberration data, determining whether the condition is met while acquiring images, and continuing to acquire images after fine-tuning the charged particle beam microscope system.

9. The method of claim 1 , wherein the predicted aberration data characterizes or reduces or partially corrects coma aberration.

10. The method of claim 1 , wherein the predicted aberration data characterizes or reduces or partially corrects a plurality of aberrations.

11. The aberration predictor value is one or more temperature values, each temperature value corresponding to a respective one or more sensors or a different acquisition time; Time to get data acquisition, an optical state of the charged particle beam microscope system; a measurable physical state of the charged particle beam microscope system; Sample location, and / or . the position of an optical system of the charged particle beam microscope system.

12. The method of claim 1 , wherein the aberration measurement is triggered by a detected temperature change above a threshold.

13. The method of claim 1 , wherein an aberration measurement is obtained for each charged particle microscope image.

14. The aberration measurement value is one or more images acquired while varying one or more microscope parameters; one or more images that may be processed to obtain one or more aberration parameters; The method of claim 1 , further comprising one or more of:

15. The method of claim 14 , wherein the one or more microscope parameters is beam tilt.

16. The method of claim 1 , wherein the sample reconstruction comprises a single particle analysis (SPA) reconstruction and / or a three-dimensional reconstruction.

17. A charged particle beam microscope system comprising one or more processors and one or more memories, the one or more memories storing computer readable instructions configured to cause the one or more processors to perform operations including the steps recited in any one of claims 1 to 16.

18. A computer readable medium comprising instructions which, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations including the steps of any one of claims 1 to 16.

19. 1. A method for training an aberration model for reducing or at least partially correcting aberrations in an image acquired using a charged particle beam microscope system, the method taking as input a set of predictor values ​​and generating an output of predicted aberration data, the method comprising: obtaining pairs of aberration measurements and a corresponding set of aberration predictor values, the aberration predictor values ​​including one or more temperature values, each temperature value corresponding to a respective one or more sensors or different acquisition times, a time at which the aberration measurements are obtained, a sample position, an optical state of the charged particle beam microscope system, a measurable physical state of the charged particle beam microscope system, and / or a position of an optical system of the charged particle beam microscope system; and training the aberration model using the pairs.

20. a.) training includes using the aberration measurements to train the aberration model, and the predicted aberration data characterizes the aberrations; or b.) training includes determining parameters that define corrections that at least partially correct aberrations in an image and / or adjustments to the charged particle beam microscope system that reduce aberrations corresponding to the aberration measurements, and training the aberration model using the determined parameters, wherein the predicted aberration data characterizes the corrections and / or adjustments; 20. The method of claim 19.

Images