Correction of optical aberrations in charged particle beam microscopy

Abstract

The invention relates to correction of optical aberrations in charged particle beam microscopy. A charged particle beam microscope system is operated and directs a charged particle beam to a sample to produce a plurality of images for a plurality of regions of the sample. A respective first set of one or more aberration predictors is acquired for each of the plurality of images. During the image acquisition, aberration measurements and a corresponding second set of aberration predictors are periodically acquired. An aberration model is obtained using the aberration measurements and the corresponding second set of aberration predictors, wherein the model employs a set of aberration predictors as input and outputs predicted aberration data. The model is applied to the first set of aberration predictors to obtain corresponding aberration data for reducing or at least partially correcting aberration in the charged particle microscope image. A sample reconstruction is obtained using the acquired charged particle microscope image.

Description

Technical Field

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

[0002] Transmission electron microscopy (TEM) is a technique in which an electron beam is transmitted through a thin sample to form an image. As the electrons are transmitted through the sample, an image is formed by the interaction of the electrons with the atoms of the sample. The image can be captured by a camera. Due to the small wavelength of the transmitted electrons, TEM is able to produce high-resolution images. Sufficiently high-resolution images can be used to study and analyze the structural details of the sample at the atomic scale. However, optical aberrations in the microscope can limit the resolution of the image. BRIEF DESCRIPTION OF THE DRAWINGS

[0003] Figure 1 is a block diagram of a system for measuring and correcting optical aberrations in a charged particle beam microscope system operating in a transmission imaging mode, according to various implementations.

[0004] Figure 2 is a flow chart of a specific example method for obtaining aberration measurements, such as may be optionally used to facilitate various implementations described below.

[0005] Figure 3 is a flow chart of a workflow for acquiring aberration data according to various specific implementations.

[0006] Figure 4a and Figure 4b is a flow chart of a workflow for processing acquired aberration data to correct charged particle microscopy images according to various implementations.

[0007] Figure 5 is a flow chart of a workflow for processing acquired aberration data to tune a charged particle beam microscope system according to various implementations.

[0008] Figure 6 is a block diagram of a generalized computing system of a suitable computing environment that may be used in implementing methods for monitoring and correcting optical aberrations in a charged particle beam microscopy system. DETAILED DESCRIPTION

[0009] The subject matter disclosed herein relates to improved methods and systems for obtaining sample reconstruction. 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 related to aberrations in the system, the model being 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, star aberration, sixth-order star aberration, three-leaf aberration, rose aberration, linear distortion, pincushion distortion, barrel distortion, spiral distortion, and elliptical distortion. Specific embodiments of charged particle beam microscope systems relate to electron microscopy systems. Therefore, the present disclosure includes various aspects and embodiments described wherein the charged particles are electrons.

[0010] Sample reconstruction may refer to a method of extracting information from a plurality of acquired images and performing reconstruction. Examples of such reconstructions are (3D) structural reconstructions, 3D models, exit wave reconstructions, 3D atomic resolution reconstructions, or surface images (such as in the case of scanning imaging modes). Images may be acquired by 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 should be understood that the sample position refers to one or both of the axial position (e.g., x / y / z coordinates) of the sample and the rotation / tilt of the sample.

[0011] In one or more specific implementations, a method for acquiring multiple charged particle microscope images for sample reconstruction includes acquiring multiple charged particle microscope images for multiple regions of a sample using a charged particle beam microscope system (e.g., an electron microscope system). The method includes acquiring a corresponding first set of aberration prediction values ​​for each acquisition of multiple charged particle microscope images, such as the temperature of the optical system of the charged particle microscope, the position and / or configuration of the optical system of the charged particle microscope, and the like. When acquiring multiple charged particle microscope images, periodically acquiring aberration measurement results and corresponding second sets of aberration prediction values. The method also includes using the second set of aberration prediction values ​​and the aberration measurement results to obtain an aberration model, for example, by training or updating the model using the second set of aberration prediction values ​​and the aberration measurement results, or by reading the model from a memory. The model takes a set of aberration prediction values ​​as input and outputs predicted aberration data. The model is applied to the first set of aberration prediction values ​​to obtain corresponding predicted aberration data, which is used to reduce or at least partially correct the aberration. The aberration data can be used to (at least partially) correct aberrations in charged particle microscopy image data and / or can be used to reduce aberrations by tuning the charged particle beam microscopy system during image acquisition (thereby reducing aberrations in subsequently acquired images). In any case, a sample reconstruction is obtained using the acquired charged particle microscopy images, the first set of aberration predictions, and the aberration model.

[0012] The aberration prediction values ​​may include any system parameters upon which the aberrations are known to depend, or parameters for which the aberrations may otherwise be predicted. For example, the aberration prediction values ​​may include two-dimensional position readings of the electron beam, time stamps, temperature, sensor measurements from the surface of the imaging system, sample position readings (e.g., x / y / z position and / or sample tilt), or any other environmental or operating variable that may affect the imaging system or the optical state of the system.

[0013] The optical state can include parameters of the optical elements of the defined system, which can be shifted or otherwise fluctuated, for example, due to the variables discussed above. Such parameters of the optical elements of the defined system can include, for example, the two-dimensional or three-dimensional position of the beam, the defocus (focusing) or amplification of the beam, the state of the magnetic deflector or electrostatic deflector, the state of one or more magnetic lenses or electrostatic lenses. The optical state of the system can change an unknown amount during a data acquisition session. However, the optical state can be tuned by adjusting the settings of the magnetic deflector or electrostatic deflector and the settings of the magnetic lens or electrostatic lens (for example, by changing the supply current or voltage). In addition, the image obtained from the microscope barrel can be corrected, taking into account the optical state of the system's changes. By obtaining an aberration model, the workflow of the imaging or data acquisition session is made more robust relative to less than perfect situations (such as changes in parameters defined above). In particular, it is possible to more closely "follow" parameter changes and the aberrations that occur therewith during a data acquisition session, without the need for additional aberration measurements, thereby resulting in, for example, a higher resolution and / or a more accurate 3D model of the sample.

[0014] The aberration model can be any of a variety of suitable classes of models. In some examples, the aberration model can be a parametric model with key system parameters as input. In some examples, the model can 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.

[0015] The aberration model may also be described with reference to its inputs and outputs. In particular, a set of predicted aberration values ​​are input into the aberration model. More specifically, the aberration model may take as input any system parameters on which known aberrations depend, while acquiring the system parameters for each of a plurality of charged particle microscope images. Predicted aberration data are then output for the aberration model. It should be understood that the predicted aberration data may define predicted aberrations, or may define corrections or system adjustments for potential aberrations. In particular, the predicted aberration data may include parameters specifying the type and numerical properties of one or more aberrations that may be present. These properties may include the magnitude or direction of one or more aberrations (such as coma), or a set of values ​​indicating aberration properties of a set of identified aberrations (e.g., coma and spherical aberration), or at least one of any other forms of data that may characterize one or more aberrations, so that the characterized aberrations may be reduced or corrected, for example, by applying a correction transformation to the corresponding data acquisition and / or applying a setting adjustment to the microscope system based on the predicted aberration data in a subsequent step. In particular, the output predicted aberration data of the model includes parameters specifying image corrections or setting adjustments for the microscope system, and thus the output of the aberration model can be directly used to specify operations to reduce or at least partially correct the predicted distortion effects of the aberrations. Corrections or system adjustments (e.g., corrections to the corresponding charged particle microscope images, or adjustments to the microscope system) can be applied appropriately to reduce the aberrations before obtaining a sample reconstruction.

[0016] In one or more examples, the corresponding predicted aberration data includes one or more aberration corrections for application to the charged particle microscopy image. In such examples, the corresponding predicted aberration data is used to correct aberrations in each acquired charged particle microscopy image, and the corrected charged particle microscopy image is used in sample reconstruction.

[0017] In some examples, the corresponding predicted aberration data may characterize one or more aberrations, such as, for example, defocus, astigmatism, coma, spherical aberration, star aberration, trefoil aberration, rosette aberration, linear distortion, pincushion distortion, barrel distortion, spiral distortion, elliptical distortion, or other aberrations. In such examples, correcting the charged particle microscopy image may include calculating an aberration correction for the charged particle microscopy image based on the corresponding predicted aberration data and correcting the charged particle microscopy image using the calculated aberration correction.

[0018] In some examples, the corresponding predicted aberration data may characterize one or more aberration corrections that can be used to correct aberrations in a charged particle microscope image for sample reconstruction. In such examples, the predicted aberration data may include parameters specifying a transformation that can be used to correct aberrations in the image, for example including scaling, rotation and / or tilt operations, or a Fourier filter. For example, the transformation may be a distortion field that corrects the aberrations. It should be understood that determining the correction (of any form) to a known aberration can be implemented 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 are incorporated herein by reference. Therefore, in this example, using the output of the aberration model, the distortion effects of the aberrations can be corrected after data acquisition in a post-processing step. By correcting the aberrations in post-processing, the need to urgently monitor the imaging system to correct the aberrations during data acquisition is reduced. However, the image correction can also be determined 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 in order to limit processing delays.

[0019] In one or more examples, once the predicted aberration data is obtained, it is determined whether the corresponding predicted aberration data satisfies a condition indicating that the charged particle microscope system needs to be tuned or indicates that there is an aberration that needs to be reduced. In some examples, if the condition, which may be a predetermined threshold of the value of the aberration or adjustment, is met, the charged particle microscope system is tuned to reduce the aberration.

[0020] In some examples, the predicted aberration data may include parameters specifying adjustments to settings of the optical system, such as adjustments to currents applied to one or more components of the optical system, to 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, for example, defocus, astigmatism, coma, spherical aberration, star aberration, trefoil aberration, rose aberration, linear distortion, pincushion distortion, barrel distortion, spiral distortion, elliptical distortion, or other aberrations. In such examples, the charged particle beam microscope system can be tuned by calculating one or more adjustments for the charged particle beam microscope system based on the predicted aberration data and applying the adjustments to the necessary components of the charged particle beam microscope system. It should be understood that in the case of large inferred aberration values, it may be better to apply adjustments to the optical elements of the microscope to reduce aberrations immediately during acquisition. In the case of smaller aberrations, corrections can be more easily applied to images, for example to correct aberrations in post-processing. These techniques may thus be used in combination, e.g. if conditions indicating that the microscope system needs to be adjusted are not met, the predicted aberration data may still indicate that (small) aberrations are present which can be corrected by correction of the corresponding acquired image / data acquisition.

[0021] In some examples, the method includes: while acquiring images, periodically inferring aberration data and determining whether an adjustment condition is met, and continuing to acquire images after tuning the charged particle beam microscope system. For example, periodic aberration data inference can be performed at each acquisition, after every n acquisitions, or according to a rule (e.g., periodic acquisitions can be triggered by a sensor on the microscope system). After the periodic acquisitions, images can continue to be acquired, but now using the adjusted microscope system, i.e., with reduced aberrations after the adjustment.

[0022] In one or more examples, the predicted aberration data can characterize or correct for coma or multiple aberrations, including coma, spherical aberration, and the like.

[0023] In some examples, the aberration model may include multiple models for various aberrations. For example, the aberration model may include multiple separate models to characterize each of the multiple aberrations, such as one model for each aberration. In other examples, the aberration model may be a single model with one or more outputs for the corresponding one or more aberrations. In this way, models for different aberrations may be calculated separately and applied one after another, 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 wide aberration mitigation and / or sample reconstruction.

[0024] In some examples, the aberration prediction values ​​include any one or more of the following: one or more temperature values ​​corresponding to corresponding one or more temperature sensors; a timestamp of the acquisition; an optical state of the charged particle beam microscope system; a measurable physical state of the charged particle beam microscope system; sensor measurements from a surface of the imaging system; a sample position reading; and / or a position reading of an optical device of the charged particle microscope. By training the model on periodically acquired aberration prediction values ​​including those described above, the imaging process is made more robust to changes in temperature or other systems over time, thereby allowing higher resolution sample reconstruction to be consistently achieved. In addition, the need for urgent monitoring and correction of the imaging system during data acquisition is reduced.

[0025] In one or more examples, periodically acquiring aberration measurements can include acquiring aberration measurements for each charged particle microscopy image. For example, aberration measurement acquisition can be performed after each exposure. In this way, the first portion of the exposure is used to acquire the charged particle microscopy image, which is beneficial for samples that may be sensitive to radiation.

[0026] Obtaining aberration measurements for each charged particle microscope image can include obtaining one or more aberration measurements at low precision (e.g., with significant noise in the estimates of the aberration values). By fitting and applying the model as the low precision measurements are made for each exposure, the noise in the measurements can be effectively averaged out, and thus the low precision measurements can be "denoised".

[0027] In one or more examples, aberration measurements may be obtained periodically during image acquisitions, for example, each aberration measurement is obtained between 1 and 200 image acquisitions, such as every 20th image acquisition.

[0028] The aberration measurements used to train the model may include one or more of the following: one or more images acquired while varying one or more microscope parameters; one or more images that can be processed to obtain one or more aberration parameters; and / or parameters characterizing aberrations. For example, one or more images may be acquired while varying beam tilt. In such examples, one or more images (or shifts therebetween) may indicate the presence of one or more aberrations and thus can be processed to obtain one or more aberration parameters.

[0029] Parameters characterizing aberrations are known in the art and may include, for example, Thon ring analysis. Such aberration characterization parameters and methods are discussed, for example, in Barthel J, Thust A "Aberration measurement in HRTEM: implementation and diagnostic use of numerical procedures for the highly precise recognition of diffractogram patterns", Ultramicroscopy, Vol. 111, No. 1, 2010, pp. 27-46, which is incorporated herein by reference.

[0030] The parameters characterizing the aberration can also include image shifts across the multiple images acquired. In a specific example, a test pattern is applied to a beam tilt coil in a microscope barrel, and the resulting beam tilt causes an aberration-induced image shift across the multiple images acquired, such as discussed in EP 4235731 A1. In this specific example, when a charged particle beam microscope system is operated in a transmission imaging mode and the charged particle beam is directed to a sample, a time sequence of beam tilt is applied to the charged particle beam in a certain pattern to generate an image sequence. When the charged particle beam transitions between a beam tilt in the time sequence of beam tilt and a sequentially adjacent beam tilt in the time sequence of beam tilt, at least some images in the images in the image sequence are captured. The image sequence can be analyzed according to the method disclosed herein to obtain sample reconstruction. In this specific example, the image of the test pattern refers to such a pattern that is configured so that the beam tilt causes an image change (e.g., an image shift) in the image sequence, which can be related to the optical aberration in the microscope system. For example, image changes can be determined from an image sequence and used to estimate one or more optical aberration values ​​for a microscope system, and / or can be determined by a trained neural network that receives the image sequence or information derived from the image sequence (e.g., a power spectrum of the image) as input data. In this specific example of aberration measurements, the beam tilt pattern is a periodic pattern, such as a Lissajous figure. The use of a periodic pattern can make it possible to determine an unknown deviation of the applied beam tilt pattern relative to an unknown time delay and frequency of the image changes measured over time. The periodic pattern can also make the system robust to other effects that can induce image changes (e.g., sample shifts and mechanical vibrations), thereby allowing signal processing to be used to separate different signal sources.

[0031] In some examples, the sample reconstruction includes a single particle analysis (SPA) reconstruction and / or a three-dimensional reconstruction. The sample reconstruction may include or be based on charged particle microscopy acquisition, where many images are acquired at different sample positions. For example, the sample reconstruction may be used for electron tomography reconstruction.

[0032] In some implementations, a charged particle beam microscope system may include one or more memories storing computer readable instructions configured to cause one or more processors to perform operations including the methods described above.

[0033] In some specific implementations, a computer-readable medium includes instructions to be executed by one or more data processing devices, which, when executed, cause the corresponding one or more devices to perform operations including the above-mentioned method.

[0034] In one or more specific implementations, a method for training an aberration model for reducing or at least partially correcting aberrations in sample reconstruction includes accessing a plurality of previously acquired pairs of sets of aberration predictions and corresponding aberration measurements, and using these pairs to train the model. The acquired aberration model takes a set of aberration predictions as input and outputs predicted aberration data. In some examples, the aberration model can be used to obtain a reconstruction or improved resolution of the 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 predictions as input to the aberration model, obtaining an output of the aberration model in response to the set of predictions, and comparing the output of the model with the corresponding known aberration measurement for each set of predictions input. The input aberration predictions include: one or more temperature values ​​(each temperature value corresponds to a corresponding one or more sensors or corresponds to a different acquisition time); the time at which the aberration measurement is obtained; the sample position; the optical state of the charged particle beam microscope system; the measurable physical state of the charged particle beam microscope system; and / or the position of the optical device of the charged particle beam microscope system. Then update and / or tune the parameters of the model to reduce the error between the output value and the known aberration data. Training can include using the aberration measurement results to train the model so that the predicted aberration data characterizes the aberration, or alternatively, training can include determining parameters that define the correction of the aberration in the image at least partially corrected or the adjustment of the charged particle beam microscope system corresponding to the aberration measurement results, and using the determined parameters to train the model so that the predicted aberration data characterizes the correction and / or adjustment. Various methods of training models are known in the art and are applicable to the methods disclosed herein. For example, the model can be a deep learning model, such as an artificial neural network (ANN). Gradient descent, stochastic gradient descent or other optimization algorithms similar to ADAM can be used to train the ANN, as well known in the ANN field. The model can be any other parameter model, such as a physical model of aberration, a polynomial model, etc., wherein any suitable optimization routine (including the above-mentioned optimization routine) is used to update the parameters to reduce errors.

[0035] In one or more specific implementations, a method for acquiring multiple charged particle microscope images for sample reconstruction includes acquiring multiple charged particle microscope images for multiple regions of the sample. The method includes acquiring a corresponding first set of aberration prediction values ​​for each of the multiple charged particle microscope images, such as the temperature of the optical system of the charged particle beam microscope system, the position and / or configuration of the optical system of the charged particle beam microscope system, and the like. The sample reconstruction is obtained using the acquired charged particle microscope images. In these specific 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 having to use the images acquired for reconstruction to obtain or update the model. In this way, obtaining the aberration model may only include reading the trained aberration model from a memory. The above examples are also applicable to these specific implementations in a similar manner.

[0036] This theme is described with various specific implementations and examples. In some cases, as will be appreciated by those skilled in the art, the disclosed specific implementations and examples may be practiced without one or more of the disclosed specific details, or may be practiced with other methods, structures, and materials not specifically described herein. Unless the context clearly specifies otherwise, such as if the proposed combination involves incompatible or mutually exclusive elements, all specific implementations and examples described herein and shown in the accompanying drawings may be combined to form any number of combinations without any restrictions. Unless the context clearly indicates otherwise, the sequential order of actions in any process described herein may be rearranged, such as if one action requires the result of another action as input.

[0037] For simplicity, and for continuity of description, the same or similar reference numerals may be used for the same or similar elements in different drawings, and when an element in one drawing appears with the same or similar reference numerals in other drawings, the description of the element will be considered to be continued. In some cases, the term "corresponding to" can be used to describe the correspondence between elements of different drawings. In example use, when an element in a first drawing is described as corresponding to another element in a second drawing, the element in the first drawing is considered to have the characteristics of the other element in the second drawing, and vice versa, unless otherwise specified.

[0038] The word "include" and its derivatives such as "comprises" and "containing" are to be interpreted in an open, inclusive sense, i.e., as "including but not limited to". The singular forms "a", "an", "at least one", and "the" include plural referents unless the context indicates otherwise. When used between the last two elements of a list of elements, the term "and / or" refers to 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 indicates otherwise. When used to describe a range of sizes, the phrase "between X and Y" means a range that includes X and Y. As used herein, "device" may refer to any individual device, a collection of devices, a portion of a device, or a collection of portions of a device.

[0039] Figure 1 A system 100 is illustrated according to one specific implementation that can measure and reduce or partially correct optical aberrations in a charged particle beam microscope system when the charged particle beam microscope system is operated 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 Figure 1 The particular configuration of the charged particle beam microscope system 104 is shown, and may be used with any charged particle beam microscope system, such as a system operating in a scanning imaging mode, or the like.

[0040] In the illustrated example, the charged particle beam microscope system 104 includes a charged particle beam microscope column 120 (hereinafter referred to as the microscope column 120 ) enclosed in the vacuum chamber 106 . The microscope column 120 defines an optical axis 116 of the charged particle beam microscope system 104 .

[0041] A sample 124 to be studied may be disposed along the optical axis 116. The sample 124 may be supported by a sample holder 140, which in some examples may have the ability to translate, rotate, and / or tilt the sample 124 relative to the optical axis 116. The sample holder 140 may allow different regions of the sample 124 to be positioned relative to the optical axis 116 for study.

[0042] At the top of the microscope tube 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 strike and interact with a sample 124. The charged particle beam 112 passes through the sample 124 to produce an image.

[0043] A camera 136 located at the bottom of the microscope barrel 120 may be operated to capture images of the sample 124. The camera 136 may 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 may be capable of operating in a "movie" mode to capture a sequence of images of the sample.

[0044] Microscope column 120 may include various charged particle beam lens systems (hereinafter referred to as lens systems) that direct charged particle beam 112 along optical axis 116 and configure charged particle beam 112 to provide constant illumination covering a desired field of view on sample 124. In some examples, the various lens systems may also project an image resulting from the interaction of charged particle beam 112 with sample 124 into the field of view of camera 136.

[0045] In some examples, the various lens systems may include a condenser lens system 122, an objective lens system 126, and a projection lens system 130, which may have any suitable configuration to modify the charged particle beam 112 to provide a desired field of view. The condenser lens system 122 may be configured to condense the charged particle beam 112 into a nearly parallel beam. The objective lens system 126 may be configured to focus an image formed by transmitting the charged particle beam 112 through the sample 124. The projection lens system 130 may be configured to project an image from the objective lens system 126 to the field of view of the camera 136.

[0046] In some examples, condenser lens system 122 may include condenser lenses 122a, 122b and other components, such as condenser stigmator 122c (ie, a device that corrects for axial astigmatism) and condenser aperture 122d.

[0047] In some examples, objective system 126 may include objective 126a and other components, such as beam deflector 126b (which may be used to center the beam), objective stigmator 126c (i.e., a device to correct axial astigmatism), and objective aperture 126d. In the illustrated example, sample 124 is positioned above objective 126a (in other examples, sample 124 may be inserted into objective 126a).

[0048] In some examples, projection lens system 130 may include, for example, intermediate lens 130a (eg, to magnify an image) and projection lenses 130b, 130c. Projection lens system 130 may include other components, such as an stigmator, an image deflector, and an aperture.

[0049] The components of the lens system can be electro-optical components that can be tuned by changing the current applied to the components. For example, the various lenses of the lens systems 122, 126, 130 can be electromagnetic lenses. The electromagnetic lens modifies the charged particle beam by the magnetic field generated by the solenoid. By changing the current to the solenoid, the magnetic field can be changed, which will change the focal length and magnification of the lens accordingly. Other components of the lens systems 122, 126, 130 (such as stigmators and beam deflectors) can be electromagnetic devices that can be tuned in the same manner as described for the electromagnetic lens. The ability to tune the components of the lens systems 122, 126, 130 by changing the current supplied to the components can allow aberration correction to be applied to the microscope barrel 120 on the fly (e.g., when the charged particle beam microscope system 104 is operated in a transmission imaging mode).

[0050] Lens systems 122, 126, 130 may include 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., before focusing the charged particle beam 112).

[0051] In some specific examples involving measuring aberrations of an image with a test pattern, the system 100 may 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 parameter formula:

[0052] x=A sin(at+δ) (1)

[0053] y=B sin(bt) (2)

[0054] The Lissajous figure can be as simple as a circle (e.g., parametric formulas (1) and (2) give a circle when a / b = 1, A = B, and δ = π / 2 radians) or can be more complex (e.g., the ratio a / b is not equal to 1).

[0055] In such examples including a test pattern, the system 100 may include a beam deflector 134 inserted into the microscope barrel 120. The beam deflector 134 may be controlled according to the pattern to tilt the charged particle beam 112 in the X-direction and the Y-direction over time. The beam deflector 134 may apply a tilting motion to the charged particle beam 112 before the charged particle beam 112 reaches the sample 124 (e.g., before the charged particle beam 112 is focused). The beam deflector 134 may be an electro-optical deflector (e.g., an electrostatic beam deflector or a magnetic beam deflector) that will allow the tilt angle applied to the charged particle beam 112 to be changed by changing the current to the beam deflector.

[0056] The beam deflector 134, operating according to a pattern, applies a temporal sequence of beam tilts to the charged particle beam 112. Each beam tilt produces an image. Thus, the temporal sequence of beam tilts produces a sequence of images. Image shifts between consecutive (or adjacent) images in the sequence of images can indicate optical aberrations in the microscope system.

[0057] System 100 may include a frame grabber 148 in communication with camera 136. Frame grabber 148 may, for example, form an interface between camera 136 and computing system 154 (e.g., a server). Frame grabber 148 is an electronic device that can capture individual image frames from an image data stream generated by camera 136.

[0058] In such an example involving 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) image shifts over time between consecutive image frames. The image shift tracker 152 may use any suitable imaging processing technique to measure image shifts. An example 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 a computing device 154.

[0059] The computing system 154 also acquires or otherwise receives aberration prediction values ​​corresponding to the image frames received from the frame grabber 148. The aberration prediction values ​​are preferably received with each of the multiple images acquired by the frame grabber, and may refer to any parameter on which the aberration may depend or from which the aberration determination may be otherwise predicted. For example, the aberration prediction value may refer to a two-dimensional position reading of the image beam shift, a timestamp, temperature, a sensor measurement result from a surface of the imaging system, or any other environmental or operating variable that may affect the imaging system or optical state. Any of these key parameters are input to the computing system 154 along with the corresponding image frames they receive.

[0060] The system 100 may include an aberration calculator 156 that determines values ​​for optical aberrations in a charged particle beam microscope system. The optical aberrations may be determined, for example, based on Thon ring analysis or measured image shifts of a series of images acquired while using a pattern to change beam tilt; one or more parameters characterizing the aberrations; or one or more images that can be processed to obtain aberration parameters. Examples of optical aberrations that may be found and measured in a charged particle beam microscope are defocus, two-fold astigmatism, three-fold astigmatism, four-fold astigmatism, five-fold astigmatism, sextuple astigmatism, axial coma, fifth-order axial coma, spherical aberration, sixth-order spherical aberration, star aberration, sixth-order star aberration, three-leaf aberration, rose aberration, linear distortion, pincushion distortion, barrel distortion, spiral distortion, and elliptical distortion. The aberration calculator 156 may be configured to determine values ​​for any combination of these optical aberrations and other optical aberrations not specifically mentioned.

[0061] In one example, the aberration calculator 156 may receive data acquisition or charged particle microscopy images from the computing system 154 and measure optical aberrations in the microscope barrel 120 based thereon. In one example, the aberration calculator 156 may estimate the optical aberrations from the image shift tracker 152, which fits the image shift to an optical aberration function that provides a relationship between parameters of the image shift, e.g., between the test pattern image shift, beam tilt, and optical aberrations in the microscope barrel 120. In some examples, the optical aberration function may be extended with one or more auxiliary parameters that are also fitted.

[0062] In one example, the aberration calculator 156 may receive image data directly from the camera 136 or receive image frames from the frame grabber 148. The aberration calculator may process the image data to measure aberrations using any of a variety 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, Vol. 111, No. 1, 2010, pp. 27-46.

[0063] According to the methods described herein, the aberration calculator 156 fits an aberration model for predicting the aberration data. Figure 1, the aberration calculator 156 uses the multiple sets of aberration prediction values ​​obtained during data acquisition from the computing system 154 and the known corresponding aberration measurements determined by the aberration calculator 156. In particular, the aberration calculator 156 fits a model (e.g., a parametric model) that, when obtained, takes a set of aberration prediction values ​​as input and outputs predicted aberration data. The predicted aberration data may be values ​​that indicate corresponding aberrations, or may be values ​​of a correction parameterization to be applied to the acquired image.

[0064] The system 100 may include an aberration corrector 168 communicatively coupled to the aberration calculator 156. The aberration corrector 168 acquires the predicted aberration data, corrects the received image and / or adjusts the instrument within the optical system, and obtains 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 relevant one or more aberrations. Once the correction or adjustment is determined, the aberration corrector 168 applies the correction to the corresponding image data or optical system (i.e., microscope barrel). For example, the aberration corrector 168 may apply adjustments to components of the microscope barrel 120 (e.g., components of the lens system) to reduce aberrations based on the predicted aberration data determined by the aberration calculator 156. In particular, where the component is an electro-optical component, aberration corrector 168 may vary the current applied to the component in order to change how the component modifies charged particle beam 112. In some examples, aberration corrector 168 may apply adjustments to components of microscope barrel 120 based on predetermined conditions, such as if an optical aberration exceeds a predetermined threshold or if a confidence level of an estimated aberration value exceeds a predetermined threshold (e.g., if the absolute value of the estimated aberration value is less than a confidence interval for the optical aberration).

[0065] In some examples, aberration calculator 156 and aberration corrector 168 can be programs or scripts running on computing system 154. Aberration corrector 168 can communicate with aberration calculator 156. In some examples, computing system 154 can be coupled to a display 164 (e.g., a console of charged particle beam microscope system 104). In some examples, computing system 154 can present current aberration values ​​and aberration trends on display 164.

[0066] The aberration calculator 156 and the aberration corrector 168 may be implemented in software and / or hardware. The software components of the aberration calculator 156 and the aberration corrector 168 may be stored in one or more computer-readable storage media or computer-readable storage devices and executed by one or more processors (e.g., processors in the computing system 154). Any of the computer-readable media herein may be non-transitory (e.g., volatile memory such as DRAM or SRAM, non-volatile memory such as magnetic storage devices, optical storage devices, etc.) and / or tangible. It should be understood that the system 100 may 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) may be shared across multiple computing systems (or servers) that are communicatively coupled to each other to execute different programs or scripts separately.

[0067] Figure 2 is an example of using a time series of beam tilts in a certain pattern to measure the performance of a charged particle beam microscope system (e.g., Figure 1 Flowchart of an example method 200 for measuring optical aberrations in a charged particle beam microscope system 104) and acquiring a corresponding sequence of microscope images. This is one method of measuring values ​​that characterize aberrations, and it should be understood that other measurements for making such measurements may alternatively be used in the disclosed aberration correction methods, and in particular for fitting or training aberration models. Figure 2 Each is shown once and in a particular order, but the operations may be reordered and / or repeated as needed and appropriate (e.g., the different operations performed may be performed in parallel as appropriate).

[0068] At 210, the method includes operating a charged particle beam microscope system in an imaging mode (e.g., transmission imaging). Operation 210 may include generating a charged particle beam (e.g., an electron beam) using a charged particle source at the top of a microscope barrel and directing the charged particle beam to a sample using optical components in the microscope barrel. The charged particle beam interacts with the sample to produce an image. Operation 210 may include projecting the image into a field of view of a camera at the bottom of the microscope barrel.

[0069] At 220, as described in operation 210, in the case where the charged particle beam microscope system is operated in a transmission imaging mode, the method may include applying a time sequence of beam tilts to a charged particle beam coming out of a charged particle source in a certain pattern. Operation 220 may include generating a pattern. Operation 220 may include applying a time sequence of beam tilts by traversing the pattern. At each point on the traversal path, the x and y coordinates of the point determine the amount of tilt applied to the charged particle beam in the X direction and the Y direction. The amount of tilt can be used to control a beam deflector inserted in the microscope barrel to tilt the beam. In some examples, the pattern may be a periodic pattern. In some examples, the periodic pattern may be a Lissajous figure (or Lissajous curve).

[0070] At 225, when the beam tilt is applied to the charged particle beam as described in operation 220, the method may include operating the camera in a movie mode to capture an image of the sample. For each beam tilt, the camera can obtain a complete image of the sample. Therefore, the camera can capture a sequence of images corresponding to the time sequence of applying the beam tilt to the charged particle beam. The time between applying the beam tilt and the actual tilt of the beam can vary so that there is an unknown time delay between the beam tilt pattern and the captured image sequence. 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 beam tilt patterns or sample shifts, may also be incorporated into the optical aberration function.

[0071] At 230, the method may include acquiring aberration measurements for optical aberrations in the microscope barrel from the images captured in operation 225. In one example, the optical aberrations may be estimated based on an optical aberration function that relates image shifts to beam tilt and optical aberrations of the microscope barrel. As another example, the optical aberrations may be obtained by optimizing a function that provides a relationship between an image sequence and optical aberrations in the microscope barrel. As another example, the optical aberrations may be obtained as inferences of a trained neural network. The inferences may include predictions of the neural network and other information such as confidence in the predictions.

[0072] The following formula (3) is an example of an optical aberration function. For schematic illustration, formula (3) can obtain values ​​for defocus, astigmatism (astigX, astigY), coma (comaX, comaY), threefold astigmatism (threefoldX, threefoldY), spherical aberration (sphere), star aberration (starX, starY) and fourfold astigmatism (fourfoldX, fourfoldY).

[0073]

[0074] The vector on the right side of the formula contains the value of the optical aberration for a given state of the optical system of the microscope system at a certain moment in time. When the tilt of the beam is adjusted by (tx, ty) radians, the induced image shift will be given by (shiftX, shiftY), which can be measured from the image captured when the beam tilt is applied (for example, in operation 240). The measured shiftX, shiftY and the known tx, ty can be fit to the function of formula (3) to obtain an estimate of the optical aberration. The parameters tx, ty are time-dependent, which allows the unknown time delay between the applied beam tilt pattern and the induced image shift pattern to be considered in the function used for fitting. The change in the optical aberration function can be combined with other auxiliary parameters, such as beam tilt pattern and / or sample shift.

[0075] Operations 220 through 230 may be repeated each time an aberration measurement is due during a planned acquisition or each time a predetermined criterion for an aberration measurement is met during a planned acquisition. The aberration measurements over time may be displayed on a screen and monitored.

[0076] Figure 3 An example workflow for acquiring a sample (e.g., a sample containing instances of particles to be reconstructed) is illustrated. The workflow is part of a larger (automatic or manual) workflow in which multiple (programmed or dynamically changing) sample positions are navigated to, with the goal of acquiring a high-resolution sample reconstruction. At 301, the method includes moving to a new region of interest of the sample. Reference Figure 1 , it should 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 on a sample to a next region of interest 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 region of interest on the sample 124. In some examples, the beam remains stationary during data acquisition, and the grid containing the sample is shifted or translated to facilitate imaging of the new region of interest.

[0077] At step 302, the method includes evaluating whether a new aberration acquisition is needed, i.e., whether a criterion for a new aberration measurement is met. The criterion for a new aberration measurement may include, for example, when another measurement (such as a through focus measurement) is scheduled, when the last measurement was before a specified amount of time, when more information about aberrations in another region of the two-dimensional optical axis is acquired, or if a change in one or more environmental or operating variables is detected, e.g., if a temperature change is detected, if a change in the sample position (e.g., a change in the relative height of the sample) is detected, etc. It should be understood that step 302 may also involve only a single criterion. The new aberration acquisition may include an image of a test pattern with distortions indicative of aberrations (e.g., using Figure 2 , example methods shown), one or more images that can be processed to obtain one or more aberration parameters, and / or parameters characterizing aberrations, as discussed above.

[0078] If the assessment concludes that a new aberration acquisition is not needed, the method proceeds to step 303. At step 303, the method includes acquiring a data acquisition for reconstructing an image or model of the particle and acquiring one or more aberration prediction values. For example, the data acquisition may include a low-dose, high-resolution image acquisition at a selected region of the sample, and the prediction value may include a temperature measurement. The one or more prediction values ​​are stored in association with the data acquisition (e.g., in metadata of the data acquisition) in a data acquisition storage device 307.

[0079] If a new aberration acquisition is due, the method proceeds to step 304, which includes acquiring an aberration acquisition for measuring one or more aberrations and acquiring one or more associated predicted values ​​for the aberrations. Both acquisitions are stored in an aberration acquisition storage device 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 to make appropriate measurements (e.g., coma and astigmatism). The one or more predicted values ​​are stored in the data acquisition storage device 306 in association with the aberration acquisition (e.g., in metadata for the data acquisition). It should be understood that the metadata for the data acquisition may also include other values ​​that are not necessarily related to aberrations but may be relevant to good sample reconstruction, such as high tension values, pixel size, or other system state parameters.

[0080] The aberration prediction values ​​associated with and stored with the acquisitions of steps 303 and 304, respectively, refer to any parameter that the aberration may depend on or from which the aberration may be predicted. For example, the aberration prediction value may refer to a two-dimensional position reading of the image beam shift, a time stamp, temperature, sensor measurements from the surface of the imaging system, sample position, or any other environmental or operating variable that may affect the imaging system or optical state.

[0081] Steps 303 and 304 may occur simultaneously or sequentially. For example, if the criteria for a new aberration measurement are met, a new data acquisition may be performed together with the aberration determination acquisition, e.g., using the same exposure. In some examples, the aberration measurement at step 304 may be performed after the data acquisition at step 303. In this way, the first portion of the exposure is used to acquire a charged particle microscope image, which is beneficial for radiation-sensitive samples.

[0082] Once a particular region of the sample has been visited, the method proceeds to step 308. Method 308 includes checking whether all regions of interest of the sample have been visited. If "no", the method returns to method step 301, i.e., moves to the next region of interest on the sample and repeats the workflow as described above. If all regions of interest of the sample have been reached, the sample acquisition run is terminated.

[0083] Figure 4a An example workflow for obtaining an aberration model from a pair of stored aberration acquisitions and obtained aberration prediction values ​​and using the model to reduce or at least partially correct the aberrations is illustrated. At step 405, the aberration acquisition storage device 306 is accessed and one or more aberration measurements are determined from the stored corresponding aberration acquisitions. For example, the aberration measurement can be a value or quantification of coma present in the corresponding image. Methods for determining aberration measurements from aberration acquisitions are known in the art, and a specific example method is discussed above and described in Figure 2 Shown in.

[0084] At step 406, pairs of aberration measurements and corresponding aberration prediction values ​​are used to fit a model to predict aberration measurements based on the aberration prediction values. The method includes initializing a model, which can 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 aberration model takes a set of aberration prediction values ​​as input and outputs a predicted aberration measurement result. The model is trained by obtaining predicted aberration measurements from the aberration prediction values, comparing the predicted measurements with the actual aberration measurements, and adjusting the parameters of the model to reduce the difference between the predicted measurements and the actual measurements. Various methods of training models are known in the art and can be used to implement the methods disclosed herein. For example, the parameters of the model can be trained using a suitable optimization technique (such as gradient descent).

[0085] Once the model is fitted, the data acquisition storage device 307 is accessed at step 407, and the predictions acquired during the acquisition are input into the model for extrapolation to obtain an aberration measurement result for each acquisition. For example, the aberration model may infer the presence of 10 μm coma from a given set of aberration predictions. At step 408, a correction for the aberration measurement result output by the model is determined for each data acquisition. Determining the correction for a given aberration measurement result is known in the art and may include, for example, a Fourier filter to correct for phase flips and / or phase shifts caused by the predicted aberration measurement result, a transform or distortion field to correct for distortion caused by the predicted aberration measurement result, or a tuning adjustment to the microscope barrel, as will be described below with reference to Figure 5 At 410, data correction is applied to the corresponding data acquisition in a final step 410, and the data is reconstructed to obtain a full resolution, model or image of the particle.

[0086] FIG. 4B illustrates an alternative example for obtaining an aberration model. Step 401 is the same as step 405 described in FIG. 4A . At step 402, the aberration correction for each aberration acquisition is determined based on the aberration measurement results obtained at step 401 using methods known in the art. Corresponding pairs of one or more parameter values ​​defining the aberration correction for each aberration acquisition and the corresponding aberration prediction values ​​are obtained. The pairs obtained from step 402 are used to fit the aberration model at step 403. During training, the output aberration correction parameters output by the model are compared with the corresponding known aberration correction parameters obtained at 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 should be understood that although Figure 4b The input and output of the model must be related to Figure 4a The model is different, but the model can be the same as above.

[0087] At step 404, the data acquisition storage device 307 is accessed and a set of predicted values ​​for each reconstruction acquisition is input into the model for extrapolation to obtain the corresponding aberration correction. In a final step 410, the extrapolated correction is applied to the corresponding data acquisition, and the image or model of the particle is reconstructed using the corrected reconstruction acquisition.

[0088] Figure 3 The workflows of FIG. 4 may be executed separately or simultaneously, and may run independently or interdependently. In some examples, the Figure 3 The workflow for obtaining image acquisition and aberration data is then Figure 4a or Figure 4bThe step of is fed to fit the aberration model for aberration correction and thus applied to the data acquisition for reconstruction. In this way, the distortion effects of aberrations during data acquisition can be corrected in a post-processing workflow, thereby eliminating the need for on-the-fly correction of aberrations when acquiring images. In other examples, Figure 4a or Figure 4b The fitting and correction workflow can be performed on the fly during data acquisition, for example in order to limit processing delays.

[0089] Figure 5 An example workflow for deriving an aberration model from pairs of stored aberration acquisitions and acquired aberration prediction values ​​and using the model to tune a charged particle beam microscope system to mitigate aberrations is illustrated. In particular, Figure 5 The workflow of the present invention 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., before sample reconstruction). In order to feed the output adjustments to elements of the microscope and reduce aberrations during image acquisition, in some embodiments, Figure 3 and Figure 5 The workflows are run substantially simultaneously. In this way, the model is obtained and, once sufficient data has been collected to achieve suitable predictive performance of the model (e.g., as measured by RMS error determined on test data not used to train the model), the relevant data is processed "on the fly." In some embodiments, the model is pre-trained and loaded at the beginning of the workflow so that on-the-fly corrections can be made earlier or even from the outset.

[0090] Figure 5 The workflow steps can also be Figure 4a or Figure 4b The workflows of are combined, shared or otherwise run simultaneously, so that aberrations can be addressed both by correction of the acquired image and by adjustment of the microscope system. In particular, if, for example, Figure 4a and Figure 4b If the predicted aberration determined in step 407 or step 404 satisfies a condition indicating that the charged particle microscope system needs to be tuned or that the predicted aberration can be more optimally reduced or corrected by adjusting the microscope column (rather than just in data processing), the workflow can jump from FIG. 4 to Figure 5 , whereby subsequent calibration steps may include tuning the charged particle microscope system. It will be appreciated that in conjunction with FIG. 4 and Figure 5 In the example of a microscope adjustment that may result in a sudden change in the expected aberration value, this should be Figure 4a or Figure 4bThis 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 before and after the microscope is replaced, respectively.

[0091] In some examples, Figure 4a or Figure 4b The workflow can be accomplished by using two models with Figure 5 The two models may correspond to each respective type of aberration prediction as discussed herein. For example, in an example where the model directly predicts image correction and / or system adjustment, the first model may correspond to Figure 4b workflow and infer aberration correction for data acquisition, while the second model can correspond to Figure 5 Alternatively, in some examples, the method includes only one model, for example, a model for inferring one or more aberrations present in the aberration acquisition, and the method runs the same model Figure 4a and Figure 5 Workflows using one or more models running in parallel, on the fly, or in postprocessing Figure 3 , Figure 4a , Figure 4b and Figure 5 Other operational combinations of the workflows are equally contemplated and covered by the present disclosure.

[0092] Step 502 can be performed with Figure 4a and Figure 4b Steps 401 and 405 are the same or similar. In particular, aberration data (e.g., characterizing the type and nature of one or more aberrations present, or characterizing adjustments to the charged particle beam microscope system to reduce the aberrations present) are determined from the stored aberration acquisitions using techniques known in the art. At step 504, the aberration model is fitted using pairs of aberration data and aberration prediction values ​​for each corresponding aberration acquisition. The training of the model can be similar to Figure 4a and Figure 4b During training, the predicted aberration data output by the model is compared with the corresponding known aberration data obtained at 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 model training process can be seen in FIG. Figure 5 The invention can be shared between workflows in which the same kind of aberration data is predicted, such as aberration data of identification aberrations.

[0093] At step 506, each data acquisition is input into the model for inference to obtain predicted data regarding the aberrations present in the data acquisition. At step 508, it is determined whether the predicted aberration data output by the model for each data acquisition meets certain criteria or conditions indicating that the charged particle beam microscope system needs to be tuned or the aberrations need to be reduced. For example, the predicted aberration data may include an estimated quantification of the type of aberration present in the data acquisition. Alternatively, the predicted aberration data may define adjustment values ​​(e.g., voltage or current changes) to be applied to one or more elements of the charged particle beam microscope system to reduce the aberrations. The criteria may include determining whether the predicted aberration data is within a confidence interval of the predicted aberration and / or whether the absolute value of the predicted aberration or the absolute value of the adjustment value exceeds a predetermined threshold. The predetermined threshold may refer, for example, to a value using Figure 4a or Figure 4b The workflow determines the maximum value of the aberrations that can be adequately corrected in the image data, or the value of the aberrations above which the aberrations are better mitigated by adjustments to the microscope system.

[0094] At step 510, in the event that the predicted aberration data exceeds a predetermined condition, it is determined that the charged particle beam microscope system needs to be 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 elements of the microscope system to mitigate the aberrations. Similarly, if the output predicted aberration data instead defines predicted adjustments to be applied to components of the microscope system, the adjustments are applied. By communicating with a data acquisition session (e.g., Figure 3 ) together Figure 5 4, the charged particle beam microscope system can be continuously monitored and adjusted to mitigate aberrations, resulting in improved images with reduced aberrations during acquisition and higher resolution sample reconstruction. Figure 5 The aberrations can be accounted for in both the microscope system and the image. For example, if the magnitude of the inferred aberration is below Figure 5 If the predetermined condition is Figure 4a or Figure 4b In method steps 408 and / or 410, correction of the image acquisition data is utilized to more optimally correct aberrations.

[0095] Figure 6 is a block diagram of a generalized example computing system (or computing environment) that can be used in the methods described herein. In some examples, the method can use a single computing system. In other examples, the method can use multiple computing systems that can communicate over a network.

[0096] refer to Figure 6, the computing system 700 includes one or more processing units 710, 715 and memories 720, 725. Figure 6 , the basic configuration 730 is included within the dashed line. Processing units 710, 715 execute computer executable instructions. The processing units may be general purpose central processing units (CPUs), processors in application specific integrated circuits (ASICs), or any other type of processor. In a multi-processing system, multiple processing units execute computer executable instructions to increase processing power. For example, Figure 6 A central processing unit 710 and a graphics processing unit or co-processing unit 715 are shown. 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 both that can be accessed by the processing unit. 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 unit. The software 780 may include, for example, one or more of the aberration calculator 156, the aberration corrector 168, or the logic of the methods described herein.

[0097] The computing system may have additional technical features. For example, the 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, a controller, or a network interconnects the components of the computing system 700. Typically, operating system software (not shown) provides an operating environment for other software executed in the computing system 700 and coordinates the activities of the components of the computing system 700.

[0098] Tangible storage 740 may be removable or non-removable and include disks, magnetic tapes or cartridges, CD-ROMs, DVDs, or any other medium that can be used to store information in a non-transitory manner and that can be accessed within computing system 700. Storage 740 stores instructions for software 780 that implement one or more innovations described herein.

[0099] Input device 750 may be a touch input device such as a keyboard, a mouse, a pen or a trackball, a voice input device, a scanning device, or another device that provides input to computing system 700. Output device 760 may be a display, a printer, a speaker, a CD burner, or another device that provides output from computing system 700.

[0100] Communication connection 770 enables communication with another computing entity via a communication medium. The communication medium transmits 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 of its characteristics set or changed in a manner that encodes information in the signal. By way of example and not limitation, the communication medium may use electrical, optical, RF or other carriers.

[0101] In view of the above specific implementation of the disclosed subject matter, the present application discloses the additional examples enumerated below. It should be noted that one feature of a separate example or more than one feature of an example taken in combination and optionally in combination with one or more features of one or more other examples is also an additional example falling within the disclosure of the present application.

Claims

1. A method for acquiring a charged particle microscope image for sample reconstruction, the method comprising: acquiring a plurality of charged particle microscope images for a plurality of regions of the sample using a charged particle beam microscope system; For each acquisition of the plurality of charged particle microscope images, acquiring a corresponding first set of one or more aberration prediction values; When acquiring the plurality of charged particle microscope images, periodically acquiring aberration measurement results and corresponding second set of aberration prediction values; using the aberration measurements and the corresponding second set of aberration predictions to obtain an aberration model, wherein the model takes a set of aberration predictions as input and outputs predicted aberration data; applying the model to the first set of predicted aberration values ​​to obtain corresponding predicted aberration data; using the predicted aberration data to reduce or partially correct aberrations in the charged particle microscope image; and The acquired charged particle microscopy images are used to obtain a sample reconstruction.

2. The method according to claim 1, further comprising: using the corresponding predicted aberration data to correct each acquired charged particle microscope image; as well as The corrected charged particle microscope images are used for sample reconstruction.

3. The method according to claim 2, wherein the corresponding predicted aberration data characterizes one or more aberrations, and correcting the charged particle microscope image comprises: calculating an aberration correction for the charged particle microscope image based on the corresponding predicted aberration data; as well as The charged-particle microscope image is corrected using the calculated aberration correction.

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

5. The method according to any one of claims 1 to 2, further comprising: determining whether the predicted aberration data satisfies a condition indicating a need to tune the charged particle beam microscope system; as well as In response to determining that the condition is met, the charged particle beam microscope system is tuned to reduce aberrations.

6. The method according to claim 5, wherein the predicted aberration data characterizes one or more aberrations, and tuning the charged particle beam microscope system comprises: calculating adjustments for the charged particle beam microscope system based on the predicted aberration data; as well as The adjustment is used to tune the charged particle beam microscope system.

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

8. A method according to any one of claims 5 to 7, comprising periodically predicting aberration data while acquiring images and determining whether the condition is met, and continuing to acquire images after tuning the charged particle beam microscope system.

9. A method according to any preceding claim, wherein the predicted aberration data characterises or reduces or partially corrects coma aberration.

10. A method according to any preceding claim, wherein the predicted aberration data characterises or reduces or partially corrects a plurality of aberrations.

11. A method according to any preceding claim, wherein the aberration prediction value comprises one or more of the following: One or more temperature values, each temperature value corresponding to a corresponding one or more sensors or corresponding to a different acquisition time; The time when the data was obtained; the optical state of the charged particle beam microscope system; a measurable physical state of the charged particle beam microscope system; Sample location; and / or Position of the optics of the charged particle beam microscope system.

12. A method according to any preceding claim, wherein the aberration measurement is triggered by a detected temperature change above a threshold value.

13. A method according to any one of claims 1 to 11, wherein aberration measurements are obtained for each charged particle microscope image.

14. A method according to any preceding claim, wherein the aberration measurements comprise one or more of: one or more images acquired while varying one or more microscope parameters; one or more images capable of being processed to obtain one or more aberration parameters; Parameters that characterize aberrations.

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

16. A method according to any preceding claim, 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 having computer-readable instructions stored thereon, wherein the computer-readable instructions are configured to cause the one or more processors to perform operations including the steps according to any one of claims 1 to 16.

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

19. A method of training an aberration model for reducing or at least partially correcting aberrations in images acquired with a charged particle beam microscope system, wherein the aberration model takes as input a set of predicted values ​​and produces as output predicted aberration data, the method comprising: Obtaining pairs of aberration measurements and corresponding sets of aberration predictions, wherein the aberration predictions include one or more of: one or more temperature values, each temperature value corresponding to a corresponding one or more sensors or to a different acquisition time; the time at which the aberration measurements were 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 optic of the charged particle beam microscope system; and The aberration model is trained using the pairs.

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

Images