Microscope sample preparation method and associated system

Abstract

In an example, a method includes generating a set of predicted sample characterization values associated with each of a plurality of processing axes; and identifying a preferred processing axis based on the predicted sample characterization values. In another representative example, a method includes: extracting a sample specimen; generating a sample image of the sample specimen; identifying a preferred sample tilt angle; and rotating the sample specimen to an orientation corresponding to the preferred sample tilt angle. The method further includes processing the sample specimen to produce a sheet. In another representative example, a system includes: a focused ion beam (FIB) system; a sample holder; a platform; and a controller operable to cause the stage to rotate the sample specimen relative to the ion beam axis such that the ion beam approaches the sample specimen along a preferred processing axis.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. non-provisional application No. 18 / 451,759, filed on August 17, 2023, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure generally relates to methods of preparing samples for observation using a charged particle microscope, and more particularly to methods of preparing thin sections using focused ion beam milling. Background Art

[0004] Charged particle microscopy is a well-known and increasingly important technique for imaging microscopic objects, particularly in the form of electron microscopy. The basic class of electron microscopes finds practical application in the form of various device classes such as transmission electron microscopes (TEM), scanning electron microscopes (SEM) and scanning transmission electron microscopes (STEM), as well as various subclasses.

[0005] In a SEM, illumination of the sample by a scanning electron beam promotes the emission of "auxiliary" radiation from the sample, for example in the form of secondary electrons, backscattered electrons, X-rays, and cathodoluminescence (infrared, visible, and / or ultraviolet photons). One or more components of this emitted radiation are then detected and used for image accumulation purposes.

[0006] As an alternative to using electrons as the irradiation beam, charged particle microscopy can also be performed using other kinds of charged particles. In this regard, the phrase "charged particles" can be understood to cover, for example, electrons, positive ions (e.g., Ga or He ions), negative ions, protons and positrons. In addition to imaging and performing (local) surface modifications (e.g., milling, etching, deposition, etc.), charged particle microscopy can also have other functionalities, such as performing spectroscopy, examining diffraction patterns, etc.

[0007] In all cases, a charged particle microscope (CPM) will typically include at least a radiation source (eg, an electron source or an ion gun), a beam guidance system, a sample holder, and a detector.

[0008] Depending on the radiation being detected, the detector may take many different forms. Examples include photodiodes, CMOS detectors, CCD detectors, photovoltaic cells, X-ray detectors (such as silicon drift detectors and Si(Li) detectors), etc. In general, a CPM may include several different types of detectors, the selection of which may be called for in different situations. It is generally desirable that the detector exhibit a sufficiently wide dynamic range to represent a wide range of incident charged particle signal magnitudes.

[0009] While the present disclosure as a whole relates to the specific context of charged particle microscopy, and more specifically to electron microscopy, such description is not intended to be limiting, and the apparatus and methods disclosed herein may be applied in any suitable context within the scope of the present disclosure. Summary of the invention

[0010] Microscopy sample preparation methods and associated systems are disclosed herein.

[0011] In a representative example, a method includes generating, with a controller, a set of expected sample characterizing values ​​associated with each of a plurality of processing axes; and identifying, with the controller, a preferred processing axis based on the expected sample characterizing values.

[0012] In another representative example, a method includes: extracting a sample specimen from a bulk sample positioned within a chamber of a charged particle microscope (CPM) system; and imaging the sample specimen to produce a sample image representing a surface plane of the sample specimen. The method also includes: generating a set of expected sample characterization values ​​associated with each of a plurality of sample tilt angles; identifying a preferred sample tilt angle among the plurality of sample tilt angles; and rotating the sample specimen to an orientation corresponding to the sample tilt angle. Each expected sample characterization value represents a feature of the sample image expected along a processing axis corresponding to the sample tilt angle. The method also includes: processing the sample specimen using a focused ion beam (FIB) system of the CPM system to produce a thin section.

[0013] In another representative example, a system includes a FIB system, a sample holder, a platform, and a controller. The FIB system is configured to guide and focus an ion beam toward a sample specimen along an ion beam axis. The sample holder is configured to support the sample specimen relative to the ion beam, and the platform is configured to rotate the sample specimen relative to the ion beam. The controller includes a processor system and a computer-readable medium, the computer-readable medium storing processor-executable instructions, which, when executed by the processor system, cause the processor system to: generate a set of expected sample characterization values ​​associated with each of a plurality of processing axes; identify a preferred processing axis based on the expected sample characterization values; and cause the platform to rotate the sample specimen relative to the ion beam axis so that the ion beam approaches the sample specimen along the preferred processing axis.

[0014] The foregoing and other objects, features and advantages of the disclosed technology will become more apparent from the following detailed description made with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic diagram of a charged particle microscope (CPM) system according to an example.

[0016] Figure 2 is a schematic diagram of a CPM system according to another example.

[0017] Figure 3 is a top side view of a sample specimen supported by a sample holder in a default orientation relative to a processing axis of an ion beam according to an example.

[0018] Figure 4 is a top view of a sample specimen exhibiting curtain artifacts according to an example.

[0019] Figure 5 is rotated away from the default orientation according to the example Figure 3 Top side view of the sample specimen and sample holder.

[0020] Figure 6 is a top view of a sample specimen showing reduced curtain artifacts according to an example.

[0021] Figure 7 is a flow chart depicting a method of preparing a sample for observation with a charged particle microscope according to an example.

[0022] Fig. 8A is an illustration of a sample image according to an example.

[0023] Figure 8B It is based on the example Fig. 8A Visual depiction of a planar representation of a sample image.

[0024] Fig.9A is a visual depiction of the manner in which a set of expected sample characterization values ​​may be generated from a planar representation, according to an example.

[0025] Fig. 9B A set of expected sample characterization values ​​according to examples is illustrated.

[0026] Fig. 10A is a visual depiction of a planar representation of a sample specimen in a default orientation according to an example.

[0027] Fig. 10B The example corresponds to Fig. 10A The set of expected sample representation values ​​for the plane representation of .

[0028] Fig. 10C is based on the example relative to Fig. 10A Visual depiction of a flat representation of a rotated specimen specimen in the default orientation.

[0029] Fig. 10D The example corresponds to Fig. 10C The set of expected sample representation values ​​for the plane representation of .

[0030] Fig. 10E The example corresponds to FIG. 10A to FIG. 10D A collection of uniformity measures for examples of .

[0031] Fig.11A is a visual depiction of a planar representation of a sample specimen in a default orientation according to an example.

[0032] Fig. 11B The example corresponds to Fig.11A The set of expected sample representation values ​​for the plane representation of .

[0033] Fig. 11C is based on the example relative to Fig.11A The default orientation is a visual depiction of a planar representation of a sample specimen rotated in a first direction.

[0034] Fig.11D The example corresponds to Fig. 11C The set of expected sample representation values ​​for the plane representation of .

[0035] Fig.11E is based on the example relative to Fig.11A The default orientation is a visual depiction of a planar representation of a sample specimen rotated in a second direction.

[0036] Fig.11F The example corresponds to Fig.11E The set of expected sample representation values ​​for the plane representation of .

[0037] Fig.11G The example corresponds to FIG. 11A to FIG. 11F A collection of uniformity measures for examples of .

[0038] Fig.12 is a schematic diagram of a computing system that can be used to perform one or more methods of the present disclosure, according to an example. DETAILED DESCRIPTION

[0039] The present disclosure is generally directed to methods of preparing samples, such as thin sections, for observation using a charged particle microscope (CPM) system. As discussed in more detail below, such methods can facilitate preparing such samples in a manner that mitigates and / or minimizes artifacts, such as curtaining, that can otherwise reduce the uniformity of the sample. Thus, the example methods disclosed herein can result in the preparation of thin samples having a high degree of uniformity to facilitate subsequent analysis of the sample using a CPM system.

[0040] Many analytical techniques for measuring and / or characterizing properties of samples (e.g., semiconductor samples) rely on the sample being prepared in such a way that the sample has a uniform thickness and / or that the sample is thin enough to enable the use of analytical techniques such as transmission electron microscopy (TEM). Therefore, in many cases, the sample of interest is prepared to meet such physical requirements, such as by thinning the sample using a focused ion beam (FIB), before performing material analysis on the sample. However, in some cases, the sample may be non-uniform in composition and / or material properties, so FIB milling may result in undesirable structural artifacts.

[0041] As an example, a sample may include periodic features characterized by different material properties (e.g., such as may be associated with semiconductor heterostructures), which are removed by a FIB milling process at respectively different rates. When FIB milling is performed along an axis that intersects a series of similar features in the sample, the difference in milling rates can result in an artifact known as a "curtain," where the milled sample includes elongated regions with significantly varying thicknesses. The methods disclosed herein can be employed to mitigate and / or at least substantially eliminate the occurrence of such artifacts and / or inhomogeneities, thereby enabling the preparation of samples suitable for material analysis, even when such samples are characterized by inhomogeneous material properties. Specifically, and as described in more detail below, the occurrence of curtain artifacts can be understood to be associated with a processing axis of the FIB ion beam relative to the sample, which processing axis characterizes the orientation of the sample relative to the ion beam.

[0042] Thus, the present disclosure relates to methods of identifying a preferred processing axis along which to process (e.g., mill) a sample using a FIB, such as by modeling the effects of milling a sample along multiple and / or a range of processing axes and identifying a preferred processing axis based on the analysis. As described in more detail below, such analysis may be at least partially and / or fully automated and may be performed such that a preferred processing axis is identified and the sample is milled accordingly without manual intervention and / or touching the sample.

[0043] Exemplary Systems

[0044] Figure 1An example of a CPM system 100 is depicted that can be used when performing various aspects of the methods disclosed herein and / or can be used in conjunction with samples prepared according to the methods disclosed herein. The CPM system 100 includes a system controller 102 that is coupled to an ion beam source 104 that produces an ion beam 105 and an electron beam source 106 that produces an electron beam 107. Respective scanners 112, 114 are positioned to direct the scanned ion beam 113 and the scanned electron beam 115, respectively, relative to a specimen 120 (which may additionally or alternatively be referred to as a specimen 120). In some examples, an image is obtained based on the scanned electron beam 115, and the scanned ion beam 113 is used for specimen modification (e.g., before imaging the specimen 120 using the scanned electron beam 115). However, it should be understood that an image can be obtained using either or both of the scanned ion beam 113 and the scanned electron beam 115. In some examples, the CPM system includes only one of the electron beam source and the ion beam source. For example, for many biological specimens, only an electron beam is required.

[0045] The sample 120 is fixed to a platform 122, which is coupled to a platform controller 124, which in turn is coupled to the system controller 102. The platform 122 can generally provide one or more translations, rotations, or tilts as directed by the system controller 102. In response to the scanned ion beam 113 and / or the scanned electron beam 115, a beam 126 is directed from the sample 120 to an electron or ion detector 128 coupled to a system electronics 130. Such system electronics 130 may include one or more analog-to-digital converters (ADCs), digital-to-analog converters (DACs), amplifiers, and / or buffers for controlling the detector 128 and processing (e.g., amplifying, digitizing, and / or buffering) signals associated with the detector 128. In other examples, a photon detector is used that generates an electrical signal that is further processed by the system electronics. In most practical examples, at least one ADC is used to generate a digitized detector signal that can be stored as an image in one or more tangible computer-readable media (shown as an image storage device 132). In other examples, image storage can be performed remotely via a communication connection such as a wired or wireless network connection. The beam 126 may include and / or may be a scattered portion of the scanned ion beam 113, the scanned electron beam 115, secondary electrons, ions, and / or neutral atoms. An optical imager 151, such as a camera, may be coupled to the system controller 102 to produce an image of the specimen 120, such as to produce a substrate image showing a plurality of substrate segments. Such images may be processed to identify, locate, and align each of the segments using a charged particle beam for further (usually higher resolution) imaging.

[0046] The system controller 102 is coupled to a memory 135 that stores processor-executable instructions for image processing and / or for performing any of the methods disclosed herein. Figure 1 As shown, and as discussed in more detail below, such processor executable instructions may relate to processes such as sample extraction 136, preferred processing axis identification 138, and / or sample processing 140. The memory 135 may also provide a graphical user interface (GUI) 142 for various functions, including enabling a user to select a segment of the specimen 120 to be processed and / or defining a visible trace showing a segment of interest. Images (e.g., CPM-generated images and / or optical images) may be stored in the memory portion 132. Platform coordinates (including rotations) may also be stored in the memory portion 132. The system controller 102 establishes image acquisition parameters and communicates with the platform controller 124. Specimen images (such as preview images, segment images, substrate images, overview images) may be presented on a display 152, and system control and imaging parameters may be specified using values ​​from internal storage of the memory 135 or provided by a user using one or more user input devices 150. Reference herein Fig.12 Additional aspects and / or examples of the system controller 102 are disclosed.

[0047] It should be understood that Figure 1 The layout is presented schematically for ease of illustration, and the actual alignment of the various beam sources, optical camera 151, and charged particle detectors is not shown. Although a dual beam (ion / electron) system is illustrated, any suitable combination of ion beams and electron beams may be used, including systems that employ only ion beams or only electron beams. For example, in many practical examples such as electron microscopes, only electron beams are used for imaging. As additional examples, the various systems may include multiple FIB systems and / or multiple SEM systems.

[0048] Figure 2 Another example of a CPM system 210 is illustrated that can be used in performing aspects of the methods disclosed herein and / or can be used in conjunction with samples prepared according to the methods disclosed herein. In particular, Figure 2 An example is illustrated in which the CPM system 210 is a dual beam CPM system 210 including a scanning electron microscope (SEM) 241 and a focused ion beam (FIB) system 211. The SEM 241 includes a vertically mounted column, while the FIB system 211 includes a column mounted at an angle of about 52° from vertical. Figure 2 Examples of suitable hardware are disclosed, but it will be appreciated that the methods of the present disclosure may also be performed in conjunction with any other suitable hardware.

[0049] The SEM 241 includes a cathode 252 configured to emit an electron beam 243 by applying a voltage between the cathode 252 and an anode 254. The electron beam 243 is focused to a point on a sample 222 by a converging lens 256 and an objective lens 258. The sample 222 may include and / or may be any of a variety of materials, such as a semiconductor substrate. Therefore, the sample 222 may be additionally or alternatively referred to as a substrate 222 and / or a bulk sample 222.

[0050] The sample 222 may be positioned on and / or supported by a platform 225 (e.g., a translation platform) that is positioned within a lower chamber 226 of the CPM system 210. The platform 225 may preferably move in a horizontal plane (e.g., along the X-axis and the Y-axis) and vertically (e.g., along the Z-axis). In some examples, the platform 225 may also be tilted by about 60° and rotated about the Z-axis.

[0051] like Figure 2 As shown, the CPM system 210 may additionally include a sample holder 224 configured to support a sample specimen (e.g., a thin section) for investigation and / or characterization by the SEM 241 (e.g., when the SEM 241 is used as a TEM, and / or when the SEM 241 is used to perform a scanning transmission electron microscope (STEM)), and / or for processing a sample specimen for TEM analysis. For example, the sample holder 224 may be configured to support a sample specimen that is extracted and / or otherwise removed from the bulk sample 222 and is to be inspected and / or analyzed by the SEM 241. The sample holder 224 may support the sample specimen while processing the sample (e.g., while thinning the sample with the FIB system 211) and / or while analyzing with the SEM 241. In the present disclosure, the sample holder 224 may also be referred to as a TEM grid 224. The sample holder 224 may be supported by the platform 225 and / or may include at least a portion of the platform 225. For example, sample holder 224 may be configured to translate and / or rotate a sample specimen.

[0052] The lower chamber 226 may include a door 261 through which the sample 222 may be positioned onto the platform 225. The door 261 may also allow for maintenance of an internal gas supply reservoir if one is used. The door 261 may be interlocked so that it cannot be opened if the system is under vacuum. Although Figure 2 An example is illustrated in which the sample platform 225 may operate as a translation platform, but it is also within the scope of the present disclosure that the sample 222 may be supported by a motion platform (eg, a translation and / or rotation platform) supported by the sample platform 225 .

[0053] The focused electron beam 243 can be scanned in a two-dimensional pattern relative to the sample 222 by the deflection coils 260. Additionally or alternatively, the sample 222 can be moved relative to the electron beam 243 by the translation platform 225. The CPM system 210 may include a power supply and control unit 245 that is programmed and / or configured to control the operation of the focusing lens 256, the objective lens 258, and the deflection coils 260. When electrons in the electron beam 243 impact the sample 222, secondary electrons are ejected from the sample 222, which can be detected by the secondary electron detector 240, as discussed below. Additionally or alternatively, a STEM detector 262 can be positioned below the sample holder 224 and the platform 225 to collect electrons that are transmitted through the sample mounted on the sample holder 224.

[0054] The FIB system 211 includes a vacuum chamber having an upper neck portion 212 within which an ion source 214 (e.g., a liquid metal ion source) and a focusing column 216 including an extractor electrode and an electrostatic optical system are located. Figure 2 In the example of FIG. 2 , the axis of the focusing column 216 is tilted 52° relative to the axis of the electron beam 243 of the SEM 241. The upper neck portion 212 includes an ion source 214, an extraction electrode 215, a focusing element 217, and a deflection element 220. The ion source 214 is configured to emit an ion beam 218 that passes through the focusing column 216 and between the electrostatic deflectors 220 toward a sample 222.

[0055] The CPM system 210 additionally includes an ion pump 268 for evacuating the upper neck portion 212. The lower chamber 226 can be evacuated using a turbomolecular and mechanical pumping system 230 under the control of a vacuum controller 232. The vacuum system provides a pressure between about 1×10 -7 Torr and 5×10 -4 If etch assist gases, etch retardant gases, or deposition precursor gases are used, the chamber background pressure may increase, typically to about 1×10 -5 Entrust.

[0056] The CPM system 210 includes a high voltage power supply 234 that provides appropriate accelerating voltages to electrodes in the ion beam focusing column 216 for exciting and focusing the ion beam 218. When the focused ion beam 218 strikes the sample 222, material may be physically ejected (e.g., sputtered) from the sample. Additionally or alternatively, the ion beam 218 may decompose a precursor gas to deposit material on the sample 222.

[0057] A voltage supply 234 is connected to the ion source 214 and to appropriate electrodes in the ion beam focusing column 216 for forming an ion beam 218 having an energy of about 1 keV to 60 keV and for directing the ion beam 218 toward the sample 222. The CPM system 210 may additionally include a deflection controller and amplifier 236 coupled to the deflection plates 220. The deflection controller and amplifier 236 may operate according to a prescribed pattern provided by a pattern generator 238 so that the ion beam 218 may be manually or automatically controlled to move along a corresponding pattern on the upper surface of the sample 222. In some systems, the deflection plates 220 may be positioned upstream of the final lens.

[0058] In some examples, a beam blanking electrode may be positioned within the ion beam focusing column 216 to cause the ion beam 218 to strike the blanking aperture instead of the sample 222 when a blanking controller applies a blanking voltage to the blanking electrode.

[0059] Ion source 214 can provide a metal ion beam of any suitable composition, such as gallium. In a characteristic example, FIB system 211 can be configured to focus ion beam 218 to a spot size of less than 0.1 micrometers (μm) at sample 222, which can be used to modify sample 222 (e.g., by ion milling, enhanced etching, and / or material deposition) or to image sample 222.

[0060] Continue to refer Figure 2 , the CPM system 210 may include a charged particle detector 240, such as an Everhart-Thornley detector or a multichannel plate, which can be used to detect secondary ion or electron ejection. The charged particle detector 240 may be connected to a video circuit 242, which supplies a drive signal to a video monitor 244 and receives a deflection signal from a system controller 219. The position of the charged particle detector 240 within the lower chamber 226 may vary in different examples. For example, the charged particle detector 240 may be coaxial with the electron beam 243 and may include a hole for allowing the ion beam 243 to pass therethrough. In other embodiments, the secondary particles may be collected by a final lens and then deflected off-axis to be collected by the charged particle detector 240.

[0061] The micromanipulator 247 can precisely move objects within the vacuum chamber. The micromanipulator 247 can include a precision electric motor 248 positioned outside the vacuum chamber to provide X, Y, Z, and θ control of a portion 249 positioned within the vacuum chamber. The micromanipulator 247 can be equipped with different end effectors for manipulating small objects. In the embodiments described herein, the end effector is a fine probe 250. Additionally or alternatively, in some examples, the micromanipulator 247, the end effector, and / or the fine probe 250 can include and / or can be a replaceable needle, such as a replaceable tungsten needle.

[0062] In some examples, a gas delivery system 246 extends into the lower chamber 226 for introducing and directing a gaseous vapor toward the sample 222. Examples of such gas delivery systems are disclosed in U.S. Pat. No. 5,851,413 and U.S. Pat. No. 5,435,850, the disclosures of which are hereby incorporated by reference. For example, a metal organic compound may be delivered to the beam impact point to deposit a metal upon impact by an ion beam or electron beam. A precursor gas such as (CH3)3Pt(CpCH3) for depositing platinum or tungsten hexacarbonyl for depositing tungsten may be delivered to be decomposed by the electron beam 243.

[0063] The system controller 219 can be programmed and / or configured to control various aspects of the operation of the CPM system 210. For example, through the system controller 219, a user can cause the ion beam 218 or the electron beam 243 to scan in a desired manner through commands entered into a user interface of the system controller 219. Additionally or alternatively, the system controller 219 can control the CPM system 210 according to programmed instructions. Preferably, the controller is in communication with or includes a memory 270 storing instructions for automatically performing one or more steps, such as one or various aspects of the methods disclosed herein. Figure 1 The system controller 102 and / or Fig.12 The PC 1200 discloses other aspects and / or examples of the system controller 219. For example, the memory 270 of the system controller 219 may represent Figure 1 Memory 135 and / or Fig.12 Memory 1290.

[0064] In some embodiments, the CPM system 210 incorporates image recognition software to automatically identify areas of interest where aspects of the present disclosure may be performed. For example, the system may automatically locate similar features on a semiconductor wafer comprising multiple devices, and expose features of interest on different (or the same) devices and form images of these features.

[0065] In some examples, and as discussed above, the CPM system 210 can include a motion platform disposed on a bulk sample platform 225. In some such examples, the sample platform 225 can translate in at least the x-direction and the y-direction (e.g., in the xy plane), and the motion platform can independently translate in the z-direction and / or rotate about an axis. Using such a motion platform on top of the sample platform 225 can enable a user to observe nearly all positions and directions relative to the sample 222 by milling and observing into the volume. Thus, when the sample is removed from the bulk sample and strategically placed on the motion platform, the sample can be positioned at any desired position and in any desired orientation. Such adjustability in positioning the sample can, for example, enable the sample to be positioned at a short working distance and perpendicular to the electron beam of an electron microscope to obtain a high resolution image.

[0066] In some examples, the FIB system 211 can be used to prepare a sample specimen for observation by the SEM 241, such as by removing material from the sample specimen. In some such examples, the sample 222 is a bulk sample from which the sample specimen is extracted for analysis. The sample specimen can be extracted from the bulk sample in any suitable manner, such as by using the FIB system 211 to remove material around the sample specimen. Then, the extracted sample specimen can be mounted to a sample holder (e.g., sample holder 224) located on an independent motion platform. The sample holder may include and / or may be a needle-type sample holder and / or a grid-type sample holder. The independent motion platform allows for simultaneous milling and observation of samples from almost any direction. The sample can then also be observed at a working distance of 90 degrees and less from the electron beam for high-resolution characterization. A multi-plane sample entrance with a single weld and independent motion opens up many possibilities for users to explore the "size" of their samples. For example, a user can cross-cut a sample from one direction to locate a region of interest (ROI), and then cut the sample from another direction to characterize the sample from a completely different direction.

[0067] Additional examples of CPM systems, components thereof, and / or associated methods are disclosed in U.S. Pat. No. 9,488,554 and U.S. Pat. No. 9,653,260, the disclosures of which are hereby incorporated by reference. While the present disclosure as a whole relates to examples in which the CPM system 210 includes a SEM 241 for imaging the sample 222, not all examples require this. For example, it is also within the scope of the present disclosure that the systems and methods disclosed herein may be used in conjunction with any suitable CPM configuration, examples of which include a transmission electron microscope (TEM), a scanning transmission electron microscope (STEM), etc.

[0068] Sample processing

[0069] Figure 3 An example of a sample specimen 320 supported by a sample holder 310 relative to an ion beam 342 is illustrated. The sample specimen 320 may represent Figure 2 Similarly, the sample holder 310 may represent a sample sample extracted from the body sample 222. Figure 2 The sample holder 224 and / or the ion beam 342 may represent Figure 2 ion beam 218 of FIB system 211. Sample specimen 320 may be mounted to and / or attached to sample holder 310 in any suitable manner, such as by platinum welding.

[0070] like Figure 3 As shown, an ion beam 342 is directed toward a sample specimen 320 in a direction parallel to a processing axis 340, which is located at Figure 3 As used herein, the term "processing axis" is intended to refer to a direction relative to the sample specimen 320, and thus may be understood to mean and / or encompass a set of axes directed along such a direction. For example, while Figure 3 The processing axis 340 is illustrated as the axis along which the ion beam 342 travels, but in this example is parallel to Figure 3 Any other axis other than the z-axis in may also be described as representing the processing axis 340 .

[0071] The ion beam 342 may be moved relative to the sample specimen 320 (e.g., along a radial direction), such as by movement of the sample holder 310 and / or deflection of the ion beam 342. Figure 3 320) to remove material from the sample specimen 320. In particular, the ion beam 342 may be scanned across a portion of the sample specimen 320 to prepare a thin lamella 324, such as to prepare the sample specimen 320 for TEM analysis. Figure 3 In the example of FIG. 3 , the dashed area of ​​the sample specimen 320 may be understood to represent an area along which the ion beam 342 is scanned and removed by the ion beam 342 to produce the thin film 324 .

[0072] like Figure 3 As shown, the sample specimen 320 can be characterized by a sample width 330 as measured along the x-direction, a sample height 332 as measured along the z-direction, and / or a sample thickness 334 as measured along the y-direction. In various examples, the sample width 330 and / or the sample height 332 can be on the order of tens of micrometers, while the sample thickness 334 can be on the order of several micrometers. After thinning the sample specimen 320 using the ion beam 342, the thin slice 324 can have a thin slice thickness of about 0.5 micrometers to 1.5 micrometers (as measured along the y-direction).

[0073] When preparing sample specimen 320 for TEM analysis, it is generally desirable that lamella 324 be formed to have a consistent thickness. However, in some cases, various material characteristics and / or properties of sample specimen 320 may result in inconsistent lamella thickness produced by such FIB milling. For example, Figure 3 A sample specimen 320 is illustrated that includes a plurality of discrete features 336 that are etched by an ion beam 342 at a different rate than the surrounding material. As examples, the discrete features 336 may represent holes and / or channels extending through the sample specimen 320, or may represent material inhomogeneities having material properties (e.g., density) that result in a different etch rate than the surrounding material.

[0074] exist Figure 3 In the example of FIG. 3 , the discrete features 336 include high aspect ratio structures and / or features extending through the sample specimen 320 along respective feature axes 338 and distributed in a periodic arrangement. Figure 3 In the configuration, the discrete features 336 are arranged in columns aligned relative to the processing axis 340.

[0075] Thus, as the ion beam 342 is scanned across the sample specimen 320, the ion beam 342 may remove different amounts of material when the ion beam 342 is directed to the discrete features 336 relative to when the ion beam 342 is directed to the areas between the series of aligned discrete features 336. For example, the average etch rate of material encountered by the ion beam 342 at a given location along the x-axis may vary significantly depending on whether the given location along the x-axis is aligned with the series of discrete features 336.

[0076] As used herein, the term "high aspect ratio" may be used to describe features and / or structures whose extent in one direction is significantly greater (e.g., at least two times greater, at least three times greater, and / or at least five times greater) than the extent of the feature and / or structure in every other direction. For example, Figure 3 Each of the discrete features 336 has a dimension, as measured along a corresponding feature axis 338, that is significantly greater than the dimension of each such feature, as measured along the x-direction and the z-direction. Figure 3 336 may be described as representing a portion of a larger (e.g., longer) high aspect ratio feature of the bulk sample from which sample specimen 320 was extracted. As such, each of discrete features 336 may be described as representing a portion of a high aspect ratio structure of the bulk sample, even when such discrete features 336 are included in sample specimen 320 or slice 324 with a limited extent along feature axis 338.

[0077] Figure 4A thin slice 424 that may be produced by such an operation is illustrated, such as after milling with ion beam 342. Figure 3 Example profile of sheet 324. In particular, Figure 4 In the example, due to Figure 3 As shown by the different material removal rates produced by discrete features 336, lamella 424 is produced with a non-planar surface 422. This effect, which may be described as curtaining artifacts, may cause lamella 424 to be unsuitable for TEM analysis due to its widely varying thickness.

[0078] Similar to Figure 3 , Figure 5 A configuration is illustrated in which a sample specimen 520 is supported by a sample holder 510 relative to an ion beam 542 traveling in a direction parallel to a process axis 540. Specifically, Figure 5 The sample holder 510 and the sample specimen 520 are shown rotated about the y-axis by a sample tilt angle 544. Figure 3 The sample holder 310 and the sample specimen 320. Therefore, Figure 5 The configuration can be referenced Figure 3 The xyz coordinate axes shown (where the z axis is parallel to the process axis 340 / 540) or reference Figure 3 The rotational x'-y-z' coordinate axis shown (where the surface of the sample specimen 520 extends in the x'-z' plane) is characterized.

[0079] Since the sample specimen 520 is rotated by the sample tilt angle 544 relative to the processing axis 540, the discrete features 536 (which correspond to Figure 3 The discrete features 336 of the sample specimen 520 are at least partially misaligned with each other when viewed along the processing axis 540. Therefore, as the ion beam 542 scans across the sample specimen 520, the ion beam 542 can remove a similar amount of material from the sample specimen 520 at each point along the x-axis. For example, due to the effect of the discrete features 536 being effectively spread out along the x-axis, the average etch rate of material encountered by the ion beam 542 at a given location along the x-axis can be relatively consistent along the x-axis.

[0080] As used herein, the term "etch rate" may be used to describe and / or refer to any suitable metric that characterizes the rate at which the sample specimen 520 is milled by the ion beam 542. For example, the etch rate may be expressed in units of thickness of the sample milled per unit time, mass of the sample milled per unit time, etc. Additionally, it should be understood that the etch rate will generally depend on the properties of the ion beam 542 (e.g., ion species, accelerating voltage, focal size, etc.) as well as the properties of the sample specimen 520 (e.g., material composition). For purposes of this disclosure, it is generally sufficient to consider the etch rates that characterize different materials of the sample specimen 520 as merely relative quantities, such that any suitable definition and / or quantification of such etch rates may be used.

[0081] Figure 6 An example profile of a sheet 624 that can be produced by such an operation is illustrated. In particular, Figure 4 and Figure 6 By comparing the slices, it can be seen that Figure 6 The surface 622 of the sheet 624 is significantly less uneven due to orienting the sample specimen 520 in a manner that mitigates the curtain effect introduced by the discrete features 536 .

[0082] In general, the extent to which the curtain effect in a FIB milled lamella is mitigated will depend on the sample tilt angle 544 at which the sample specimen 520 is tilted relative to the processing axis 540. Disclosed herein are methods for determining a preferred value for the sample tilt angle 544, or in other words, a preferred orientation of the processing axis 540 relative to the sample specimen 520, based on observed and / or known physical properties of the sample specimen 520. In particular, the methods disclosed herein can be used to determine a preferred processing axis and / or a preferred sample tilt angle that minimizes and / or mitigates the curtain effect. Additionally or alternatively, such methods can be used to improve and / or optimize one or more physical properties of a lamella milled by ion beam milling, such as by enabling the lamella to be formed to have a uniform thickness. As described in more detail herein, such methods can be performed at least substantially automatically and / or without requiring user manipulation of the sample specimen. Such methods can also be performed in conjunction with sample specimens exhibiting any configuration and / or pattern of material properties, and can be based on any suitable combination of known and measured properties of the sample specimen.

[0083] Exemplary Methods

[0084] Figure 7 is a method for preparing a sample such as for scanning by SEM, TEM and / or STEM (e.g., using Figure 2 Flow chart of an example of a method 700 for performing measurements using a SEM 241. Figure 7As shown, method 700 includes, at 710 and using a controller, generating a set of expected sample characterization values ​​associated with each of a plurality of processing axes. Method 700 additionally includes, at 740 and using a controller, identifying a preferred processing axis based on the expected sample characterization values.

[0085] The controller used in performing method 700 may include and / or may be any suitable controller, such as Figure 1 The system controller 102 and / or the referenced Figure 2 Any controller in the disclosed controller. Fig.12 Additional examples of such controllers and / or portions thereof are described in greater detail.

[0086] exist Figure 7 In the embodiment, the steps and / or aspects of method 700 presented in dashed lines may be described as optional and / or as corresponding to selected examples of method 700, while the steps and / or aspects of method 700 presented in solid lines may be considered relevant to most and / or all examples. However, it should be understood that the steps and / or aspects of method 700 may be performed in any example of method 700. Figure 7 Any suitable combination of the steps and / or aspects of the depicted method 700.

[0087] As described in more detail below, the predicted sample characterization values ​​may be understood to represent the spatially varying material properties of the sample specimen as predicted along the directions corresponding to each of the processing axes (e.g., Figure 3 and Figure 5 In some examples, changes in predicted sample characterization values ​​associated with a given processing axis can be understood to represent expected and / or modeled changes in the thickness of a sample milled with an ion beam directed to the sample specimen along the processing axis. Therefore, and as described in more detail herein, processing and / or analyzing such predicted sample characterization values ​​can enable determination of a preferred processing axis along which the sample specimen can be milled to enhance and / or optimize the uniformity of the resulting thin slice.

[0088] In the present disclosure, reference may be made to a sample tilt angle (e.g., Figure 5 of the sample tilt angle 544) and / or the reference processing axis (e.g., Figure 3 and Figure 5The orientation of a sample specimen and / or an ion beam relative to each other and / or relative to other components may be described herein in terms of a processing axis 340 / 540 (of the sample). As used herein, the terms "sample tilt angle" and "processing axis" may be used interchangeably to represent and / or refer to the orientation of a sample specimen relative to an ion beam. For example, as used herein, the term "sample tilt angle" may be understood to refer to the angular orientation of the sample specimen relative to the ion beam, while the term "processing axis" may be understood to refer to the angular orientation of the ion beam relative to the sample specimen. Therefore, references herein to properties associated with a sample tilt angle may be equivalently understood to refer to properties associated with a processing axis corresponding to such a sample tilt angle, and vice versa. Similarly, references herein to angular measurements (e.g., expressed in degrees) may be equivalently understood to quantify the sample tilt angle and / or the associated processing axis.

[0089] In some examples, and as Figure 7 As shown, generating the expected sample characterizing values ​​at 710 includes generating a planar representation of the sample specimen, such as at an orientation corresponding to the processing axis, at 720. As described in more detail below, the planar representation includes and / or is a set of sample characterizing values ​​mapped onto a two-dimensional array of representation positions corresponding to respective portions of the sample specimen. Generating the expected sample characterizing values ​​at 710 may additionally include predicting the sample characterizing values ​​along an axis of the planar representation corresponding to the processing axis to generate a set of expected sample characterizing values ​​at 730.

[0090] In various examples and as Figure 7 As shown, identifying the preferred processing axis at 740 includes: generating a set of uniformity metrics corresponding to the plurality of processing axes under investigation at 750 and using a controller. Each uniformity metric corresponds to a set of expected sample characterization values ​​associated with the corresponding processing axis. In such examples, identifying the preferred processing axis at 740 also includes: identifying a preferred uniformity metric in the set of uniformity metrics at 760 and using a controller. Thus, the preferred processing axis can be identified to represent the processing axis corresponding to the preferred uniformity metric.

[0091] In some examples, method 700 includes one or more steps associated with manipulating and / or processing the sample specimen based on the preferred processing axis identified at 740. For example, and as Figure 7 As shown, method 700 may include rotating the sample specimen at 770 and / or processing the sample specimen at 780 .

[0092] The rotating sample specimen at 770 may include rotating the sample at a sample tilt angle and / or rotating the sample such that the ion beam approaches the sample specimen along a preferred processing axis. In some examples, the rotating sample specimen at 770 is supported by a platform and / or a sample holder (e.g., Figure 1 Platform 122, Figure 2Platform 225, Figure 2 The sample holder 224 and / or Figure 3 In some examples, the rotating sample specimen at 770 is performed in response to a command generated and / or transmitted by a controller.

[0093] In some examples, rotating the sample specimen at 770 is performed prior to (e.g., immediately prior to) processing the sample specimen at 780, such as to prepare the sample specimen for processing along the identified preferred processing axis. Additionally or alternatively, one or more other steps of method 700, such as generating a planar representation of the sample specimen at 720 and / or identifying a preferred processing axis at 740, may be performed (e.g., repeated) after rotating the sample specimen at 770. For example, after rotating the sample specimen at 770, method 700 may include repeating generating a planar representation of the sample specimen at 720 to generate an updated planar representation corresponding to the rotated sample specimen. Additionally or alternatively, after rotating the sample specimen at 770, method 700 may include repeating identifying a preferred processing axis at 740, such as based on the updated planar representation. As such, repeating one or more of generating a planar representation of the sample specimen at 720, identifying a preferred processing axis at 740, and / or rotating the sample specimen at 770 (and / or any sub-steps thereof) may allow for iterative refinement and / or confirmation of the preferred processing axis.

[0094] Processing the sample specimen at 780 may include processing in any suitable manner. For example, processing the sample specimen at 780 may include milling the sample specimen along a preferred processing axis using an ion beam (e.g., ion beam 218 of FIB system 211), such as to produce a lamella as described herein. As used herein, the term "processing" as used to describe an action performed on a sample specimen may be understood to encompass any suitable FIB milling process.

[0095] As described herein, generating a set of expected sample characterizing values ​​at 710 includes generating such a set of expected sample characterizing values ​​associated with each of a plurality of processing axes. Thus, generating a planar representation of the sample specimen at 720 and each of the expected sample characterizing values ​​at 730 are performed relative to each of the plurality of processing axes under consideration.

[0096] In some examples, generating the set of uniformity metrics at 750 includes determining a uniformity metric at each of a plurality of processing axes in a test range of processing axes and / or sample tilt angles. The test range of processing axes and / or sample tilt angles used in performing method 700 can be determined and / or selected in any suitable manner. In some examples, the processing axes and / or sample tilt angles can be relative to a default orientation in which the processing axis of the ion beam is parallel to the main axis of the sample specimen (such as in Figure 3 In particular, Figure 3 It can be described as an example representing a sample tilt angle of 0.

[0097] In various examples described herein, the testing range of the processing axis covers a range from -10 degrees to +10 degrees; however, it should be understood that any suitable angular range may be used. In some examples, the angular range representing the testing range of the processing axis may be based at least in part on the range of motion of the sample holder and / or an associated motion platform supporting the sample specimen.

[0098] Additionally, the processing axes used in performing method 700 may include any subset of the full range of processing axes in the tested range of processing axes. For example, method 700 may include repeating the identified preferred uniformity metric at 760 at processing axes separated by a common angular interval, such as 0.1 degrees, 1 degree, 2 degrees, or greater than 2 degrees.

[0099] FIG. 8A to FIG. 8B An example of the manner in which a planar representation of a sample specimen may be generated is shown in . Fig. 8A An example of a sample image 840 representing a sample specimen such as in a region of interest is illustrated. For example, Fig. 8A The sample image 840 may be described as depicting the image as viewed in the xz plane. Figure 3 Sample specimen 320.

[0100] Although the present disclosure as a whole describes and / or illustrates sample image 840 as an image of a graphical representation, it should be understood that sample image 840 may include and / or may be any suitable data structure that may correspond to such a graphical representation, and that such a graphical representation need not be generated and / or displayed during execution of method 700.

[0101] The sample image 840 may be divided into a plurality of sample image regions 842, each of which represents a corresponding portion of the sample specimen. Fig. 8A In the example of , each sample image region 842 includes and / or corresponds to a respective plurality of sample image pixels 844 that form sample image 840. In other examples, each sample image region 842 may include and / or correspond to a single respective sample image pixel 844.

[0102] Additionally or alternatively, in some examples, each image pixel 844 may represent and / or correspond to a plurality of sub-pixels, which in turn represent sample information at a finer spatial resolution than the image pixel 844. Thus, in some examples, such as when the sample image 840 corresponds to an image configured for graphical representation, each sample image region 842 may include and / or correspond to a plurality of sample image pixels 844, each of which includes and / or corresponds to a plurality of sub-pixels. In other examples, each sample image region 842 may include and / or correspond to one or more sub-pixels of the sample image 840. For example, the sample image 840 may correspond to an image configured for graphical display and / or a data structure having image pixels 844, and each sample image region 842 used to represent the sample image 840 (as discussed below) may correspond to one or more sub-pixels. Thus, in such an example, each sample image region 842 may represent a finer degree of spatial resolution than the image pixel 844.

[0103] Figure 8B Illustrated based on Fig. 8A 840 of a sample image 840. The planar representation 850 includes an array of representation locations 852, each representation location corresponding to a respective sample image region 842 of the sample image 840, and each representation location is associated with a respective sample characterization value 854. The sample characterization value 854 may therefore be understood as representing one or more aspects or properties of the sample specimen associated with the sample image region 842 corresponding to the associated representation location 852.

[0104] In general, each uniformity metric corresponds to a quantity having a reduced dimension relative to an expected set of sample characterizing values ​​associated with the corresponding processing axis, which in turn has a reduced dimension relative to a planar representation of the sample specimen. Thus, the uniformity metric associated with each processing axis can be described as a reduced dimensional representation of the sample characterizing values ​​of the planar representation relative to the corresponding processing axis. Each processing axis can therefore be associated with a corresponding uniformity metric value.

[0105] exist Figure 8B In the example of , each representation position 852 corresponding to a sample image region 842 that exhibits a first visual property (e.g., filled with white or gray space) is associated with a sample characterization value 854X1. Similarly, each representation position 852 corresponding to a sample image region 836 that exhibits a second visual property (e.g., such as being partially occupied by discrete features in the sample image region 842) is associated with a sample characterization value 854X2.

[0106] While the present disclosure as a whole relates to examples in which plane representation 850 is described and / or illustrated as a two-dimensional array, it should be understood that plane representation 850 may include and / or may be any suitable data structure and that the controller may transform such data structure in any suitable manner corresponding to the method steps described herein.

[0107] Each sample characterization value 854 may have any of a variety of forms and / or structures. For example, each sample characterization value 854 may include and / or may be a scalar or multi-dimensional (eg, vector) quantity.

[0108] In some examples, each sample characterization value 854 includes or is a numerical quantity characterizing a material property associated with the corresponding representative location 852. As an example, such a material property may represent the density of the sample specimen at the location corresponding to the representative location. As another example, such a material property may represent the rate (e.g., relative rate and / or absolute rate) at which a portion of the sample specimen corresponding to the representative location will be milled by the ion beam. In examples where each sample characterization value 854 includes a multi-dimensional quantity, each sample characterization value 854 may represent multiple different material properties associated with the representative location 852, such as corresponding values ​​of an anisotropic material property associated with the representative location 852 measured along different directions, etc.

[0109] Generating the planar representation 850 based on the sample image 840 can be performed in any of a variety of ways. For example, and as Figure 7 As shown, generating a plan representation of the sample specimen at 720 may include generating a sample image (e.g., sample image 840) at 722 and processing the sample image at 720 to generate a plan representation (e.g., plan representation 850). Any or all of generating a plan representation of the sample specimen at 720, generating a sample image at 722, and / or processing the sample image at 724 may be performed at least in part by a controller.

[0110] Generating the sample image at 722 may be performed in any of a variety of ways. For example, generating the sample image at 722 may include: Figure 2 The sample image can be captured and / or recorded by capturing a microscopic image of the sample specimen using a SEM 241 of the embodiment of the present invention. Additionally or alternatively, generating the sample image at 722 can include: Figure 3 In other examples, generating the sample image at 722 may include: retrieving, using a controller, an image of the sample specimen that was previously captured, generated, and / or stored.

[0111] Processing the sample image at 724 may also be performed in any of a variety of ways. In various examples, processing the sample image at 724 includes: at least in part based on the sample image, converting the sample characterization value (e.g., Figure 8B The sample representation value 854) is mapped to the two-dimensional representation position of the plane representation (for example, Figure 8B The representation position 852) is on the array. For example, refer to FIG. 8A to FIG. 8B , mapping the sample characterizing values ​​854 onto the representative locations 852 may include assigning a corresponding sample characterizing value 854 to each representative location 852 based at least in part on image intensity values ​​of the image pixels 844 forming the corresponding image region 842. Such image intensity values ​​may include, represent, and / or may be a grayscale value associated with each image pixel 844, a color value associated with each image pixel 844, and / or any other suitable quantity representing the sample image 840 at each image pixel 844.

[0112] Mapping the sample characterization values ​​854 onto the representation locations 852 may be performed in any suitable manner based on the image intensity values. For example, the mapping may be performed such that each sample characterization value 854 corresponds to an image intensity value of an image pixel 844 of the corresponding image region 842 according to a predetermined transfer function. As a more specific example, such a transfer function may assign each sample characterization value 854 to be proportional to and / or otherwise uniquely related to an image intensity value of one or more image pixels 844 in the corresponding image region 842 (e.g., an average image intensity value in the corresponding image region 842). The use of such a transfer function may facilitate the generation of the planar representation 850 such that each sample characterization value 854 may take any one of a range and / or sequence of values. For example, this may be particularly advantageous when the sample image 840 represents multiple (e.g., more than two) material properties to be represented in the planar representation 850 using corresponding sample characteristic values.

[0113] Additionally or alternatively, such a transfer function may assign each sample characterization value 854 based on a threshold filter, such as a function that assigns the sample characterization value 854 as one of two values ​​(e.g., 0 or 1) based on a comparison of an image intensity value to a threshold value. The use of such a transfer function and / or filter may be particularly appropriate in examples where sample specimens may be modeled as representing a small number of significantly different material properties.

[0114] For example, refer to Fig. 8A, the sample specimen (or its sample image 840) may include a primary material at locations corresponding to one or more primary material image regions 832, various secondary features at locations corresponding to one or more secondary feature image regions 834, and a plurality of discrete features at locations corresponding to discrete feature image regions 836. The primary material may have a first material property (e.g., density and / or ion beam etch rate), while the secondary features may have material properties similar to the material properties of the primary material, and the discrete features may have material properties that are significantly different from those of the primary material. In such examples, applying a threshold filter to process the sample image 840 is operable to ignore relatively minor variations in material properties between the primary material and the secondary features, and instead represent the discrete features individually as being materially distinct from the remainder of the sample specimen. FIG. 8A to FIG. 8B In the context of, applying such a threshold filter can be described as applying a sample characterization value X1 to each representation position 852 corresponding to the primary material image region 832 and the secondary feature image region 834, and applying a sample characterization value X2 to each representation position 852 corresponding to the discrete feature image region 836.

[0115] Additionally or alternatively, in some examples, processing the sample image at 722 may include masking the sample image to isolate one or more features of the sample image. Such masking operations may operate in a similar manner and / or to a similar effect as the threshold filtering described above, such as to isolate Fig. 8A The discrete features in the discrete features image region 836 are generated Figure 8B The plane representation 850.

[0116] Additionally or alternatively, in some examples, generating the planar representation of the sample specimen at 720 and / or processing the sample image at 724 may include comparing the sample image to one or more known properties of the sample specimen. As an example, the sample specimen may be known to include features having a well-defined shape (e.g., a circle) and / or size (e.g., a diameter) as viewed in the plane of the sample image, while the sample image may represent such features as having a slightly distorted and / or otherwise inaccurate shape and / or size. In such examples, the planar representation may be prepared such that the features are represented as having known properties (e.g., a shape and / or size) and are positioned in a manner represented in the sample image.

[0117] As a more specific example, the sample specimen may include features that are known to be circular in the plane of the sample image, but appear to be non-circular in the sample image (and / or in processed, filtered, and / or masked versions thereof). In such an example, the planar representation may be generated by representing such features as circular features located at locations corresponding to (e.g., centered about) the locations shown in the sample image. In all such examples, generating the planar representation of the sample specimen at 720 may (but is not required to) include generating a modified version of the sample image that reflects the known properties of the sample specimen and generating the planar representation by processing the modified sample image.

[0118] In various examples, processing the sample image at 722 includes determining a sample material property corresponding to each representation location and assigning a sample characterization value to the representation location based at least in part on the sample material property. In the above examples, the sample characterization values ​​are typically identified and / or assigned based on the sample image, such as based on image intensity values ​​of corresponding image pixels. Thus, in such examples, the sample material property may be determined based at least in part on one or more features of the sample image. For example, such processing may be used to model the image intensity values ​​of the sample image as directly representing the corresponding material property.

[0119] Additionally or alternatively, in some examples, the sample material properties may be based at least in part on a known material composition of the sample. For example, processing the sample image at 724 may include associating each sample image region with a corresponding sample material known to be present in the sample specimen (e.g., based on image intensity values ​​of image pixels in the sample image region), and assigning a sample characterization value to correspond to the known material property of such sample material.

[0120] While the present disclosure as a whole relates to examples in which generating a planar representation at 720 is performed using a sample image, not all examples require this. For example, generating a planar representation at 720 may be performed without reference to a sample image, such as based on one or more known and / or user input properties of a sample specimen, additionally within the scope of the present disclosure. For example, a controller may store and / or receive sample specifications corresponding to a material composition of a sample specimen in a format other than an image and / or planar representation. In such examples, generating a planar representation at 720 may include retrieving the sample specifications and mapping sample characterization values ​​to representation locations of the planar representation based at least in part on the sample specifications.

[0121] In various examples, generating the planar representation of the sample specimen at 720 may be performed such that the planar representation represents the sample specimen in any one of a plurality of planes and / or at any one of various milling stages. Figure 3, the planar representation may represent the sample specimen 320 as viewed in the xz plane, with each discrete feature 336 modeled by the planar representation being along a feature axis 338 (which is in the xz plane). Figure 3 Thus, as ion beam 342 mills and thins sample specimen 320 to produce lamella 324, the exposed surface of sample specimen 320 will include a similar and / or identical pattern of discrete features 336 as viewed in the xz plane, which in turn can be modeled by a similar and / or identical planar representation.

[0122] FIG. 9A to FIG. 11G The plane representation of the sample specimen is an example of a manner in which the identifying of the preferred processing axis at 740 may be performed. As discussed above, the curtain effect associated with milling a sample specimen having periodically repeating structural features along which such structural features are at least partially misaligned with each other may be mitigated by milling the sample specimen along the processing axis. Thus, identifying the preferred processing axis at 740 generally includes identifying a preferred processing axis that satisfies this condition.

[0123] As an example, Fig.9A A plan view 950 of a sample specimen is illustrated that is rotated by a sample tilt angle 944 relative to a series of vertical columns 956. The plan view 950 may represent Figure 8B The plane representation 850 of 950 or any other suitable plane representation. For simplicity, the plane representation 950 is illustrated as a grid of representation positions 952 corresponding to sample representation values ​​forming the plane representation 950.

[0124] As discussed above, the sample tilt angle 944 may be understood to represent and / or correspond to a processing axis. For example, generating a planar representation of the sample specimen at 720 may include generating a default planar representation of the sample specimen at an orientation corresponding to a default processing axis, and rotating the default planar representation by a sample tilt angle corresponding to a difference between the considered processing axis and the default processing axis.

[0125] As a more specific example, Figure 8B The plane representation 850 can be described as representing the plane corresponding to Figure 3 320. Specifically, in the default orientation, the processing axis 340 extends parallel to the z-axis. Figure 3 The illustrated processing axis 340 may therefore be described as a default processing axis. Fig.9A The planar representation 950 can then be described as representing the sample after rotating the sample tilt angle 944. Figure 8B The default plane representation 850, the sample tilt angle may correspond to and / or may be Figure 5 The sample tilt angle is 544. Therefore, Fig.9APlanar representation 950 may be described as representing sample specimen 520 as rotated relative to ion beam 542 such that processing axis 540 is no longer parallel to the z' axis of sample specimen 520. However, it should be understood that any orientation of the processing axis relative to the sample specimen may be considered a default processing axis.

[0126] refer to Fig.9A , when the plane representation 950 is rotated to a sample tilt angle 944 corresponding to the selected processing axis, an expected sample characterization value may be generated by dividing the plane representation 950 into a plurality of representation slices 956. For each representation slice 956, a corresponding portion of the plane representation 950 may be transformed into a corresponding expected sample characterization value 960.

[0127] Each representation slice 956 can be described as representing and / or including a corresponding subset of sample characterization values ​​of the plane representation 950. In some examples, transforming a portion of the plane representation 950 into a corresponding expected sample characterization value 960 for a selected representation slice 956 includes: transforming so that the expected sample characterization value 960 represents the sample characterization value contained within the representation slice 956 and / or otherwise associated with the representation slice. In particular, the present disclosure as a whole relates to examples in which each expected sample characterization value 960 represents the sum of the sample characterization values ​​contained within the corresponding representation slice 956. However, this is not required, and each expected sample characterization value 960 can be additionally within the scope of the present disclosure based on the associated sample characterization value in any suitable manner. As an example, transforming a subset of sample characterization values ​​into an expected sample characterization value corresponding to a given representation slice 956 may include: calculating an average sample characterization value within the representation slice, calculating a maximum sample characterization value within the representation slice, and / or calculating a distribution of sample characterization values ​​within the representation slice.

[0128] To keep it simple, Fig.9A The planar representation 950 is illustrated as being rotated by a sample tilt angle 944 relative to a series of vertical columns representing slices 956 of the representation. In implementation, Fig.9A The transformations visually depicted in FIG. 950 may be implemented in any of a variety of ways. For example, the plane representation 950 may represent a two-dimensional data structure (e.g., a matrix) that represents sample representation values ​​after a rotation transformation is applied to the data structure. Such a rotation transformation may, for example, map each sample representation value to a value corresponding to Fig.9A In this example, each representation slice 956 may represent a coordinate system in the horizontal and vertical directions of the base coordinate system. Fig.9A The coordinate values ​​of the horizontal dimension of the image are spaced such that the corresponding expected sample characterization value 960 represents the sum of the sample characterization values ​​found within such interval.

[0129] In other examples, transforming the planar representation into predicted sample characterization values ​​may not include transforming the planar representation itself, but may instead include predicting sample characterization values ​​along axes that are angled relative to a fundamental dimension of the planar representation. In such examples, each such predicted axis may represent a corresponding processing axis. In all examples, regardless of the manner in which the planar representation is transformed to produce a set of predicted sample characterization values, the predicted sample characterization values ​​may be described as being predicted along an axis of the planar representation (e.g., relative to a direction of the planar representation).

[0130] In all examples, transforming the planar representation into an expected set of sample characterization values ​​may include performing any suitable averaging, anti-aliasing, merging, and / or other steps known in the data processing arts to account for and / or mitigate artifacts associated with rotational transformations.

[0131] Each representation slice 956 may have any suitable width, such as may be determined and / or measured relative to the width of the rotation plane representation 950 and / or relative to its representation position 952. For example, for simplicity, considering the case where the sample tilt angle 944 is 0, each representation slice 956 may correspond to a single column of representation positions 952, or may correspond to multiple columns of representation positions 952.

[0132] Fig.9A The expected sample characterization value set 960 is illustrated as a series of values ​​P1, ..., P10 associated with the corresponding representation slice 956. Similarly, Fig. 9B Another set of expected sample characterization values ​​960 is illustrated as a graphical plot of expected sample characterization values ​​960 , where the horizontal axis identifies the index corresponding to the representation slice 956 .

[0133] Fig. 9B Also illustrated is the manner in which the expected sample characterization value set 960 can generate a corresponding uniformity metric 970 representing the processing axis. Specifically, in the examples disclosed herein, the uniformity metric 970 is determined as the difference between the maximum and minimum values ​​exhibited in the expected sample characterization value set 960. In other examples, other statistical properties of the expected sample characterization value set 960 can be used as the uniformity metric 970 or used in the calculation of the uniformity metric. Examples of such statistical properties include the mean of the expected sample characterization value set 960, the maximum of the expected sample characterization value set 960, the minimum of the expected sample characterization value set, and / or the standard deviation of the expected sample characterization value set 960.

[0134] In some examples, the set of expected sample characterization values ​​may be truncated and / or otherwise revised such that the uniformity metric is determined based on a subset of the full set of expected sample characterization values. Fig.9A, it can be seen that each of the expected sample characterization values ​​P1, P2, P9, and P10 represents a sum of fewer sample characterization values ​​than each of the expected sample characterization values ​​P3-P8. Therefore, it can be assumed that P1, P2, P9, and P10 represent outliers of the statistical trend exhibited by the remaining expected sample characterization values. In this example, pruning the set of expected sample characterization values ​​may include removing P1, P2, P9, and P10 from the set of expected sample characterization values ​​before transforming the set of expected sample characterization values ​​into the uniformity metric.

[0135] Additionally or alternatively, in some examples, the estimated sample characterization values ​​at 730 may include defining representation slices 956 such that each representation slice covers the same number of sample characterization values. Fig.9A , the outlier effect described above may be associated with the fact that the rotated plane representation 950 is inscribed within the dashed rectangle formed by the plurality of representation slices 956. Thus, and as Fig.9A As shown, the representation slice 956 on the edge may contain and / or represent a smaller scale of the planar representation 950 .

[0136] Alternatively, forming multiple representation slices such that the multiple representation slices are inscribed within the rotated plane representation 950 may result in each representation slice containing the same or approximately the same number of sample characterization values. Thus, defining the representation slices in this manner may allow for the generation of a set of expected sample characterization values ​​that do not need to be truncated prior to analysis. Because the proportion of change in the total plane representation 950 (based on the sample tilt angle) is excluded from the total area of ​​the representation slices in this example, such a method may also include scaling the expected sample characterization values ​​by a factor that depends on the sample tilt angle to normalize the values ​​across the sample tilt angles.

[0137] In some examples, a graphical plot of expected sample characterization values ​​960 (such as Fig. 9B 956) can be understood and / or interpreted as representing the physical properties of the sample specimen milled by the ion beam along the corresponding processing axis. For example, in examples where the sample characterizing values ​​represent material density and / or another material property related to milling rate, each predicted sample characterizing value can be interpreted as representing the x position (following the x-position of the ion beam) corresponding to the corresponding representative slice 956. Figure 3 and Figure 5 The total mass or total milling resistance encountered at the coordinate system of the milling machine.

[0138] In this way, one or more statistical values ​​representing an expected set of sample characterizing values ​​for a given processing axis may then be interpreted as similar statistical values ​​representing expected physical properties of the sample specimen when milled along such processing axis. For example, when the uniformity metric 970 is measured as the difference between the maximum and minimum values ​​exhibited in the expected set of sample characterizing values ​​960, the uniformity metric 970 may represent an expected maximum thickness or thickness variation of the sample specimen when the sample specimen is milled along the corresponding processing axis.

[0139] FIG. 10A to FIG. 10D The manner in which rotation of a planar representation of a sample specimen can result in a changed set of expected sample characterization values ​​is illustrated. In particular, Fig. 10A A plan view 1050A of a sample specimen in a default orientation corresponding to a default processing axis is illustrated, and Fig. 10B The resulting expected sample characterization value set 1060B is illustrated. For clarity, Fig. 10A Planar representation 1050A is illustrated as a visual representation in which sample characterization values ​​are represented as grayscale values.

[0140] In contrast, Fig. 10C A rotated plane representation 1050C is illustrated, which shows the plane representation 1150A rotated by a sample tilt angle of 9° relative to the default orientation, and Fig. 10D A set of expected sample characterization values ​​1060D corresponding to the processing axis is illustrated. Fig. 10B and Fig. 10D The corresponding uniformity metrics 1070B and 1070D corresponding to each processing axis are additionally labeled. Fig. 10B and Fig. 10D It can be seen that using a processing axis rotated 9° relative to the default orientation ideally results in a substantially smaller uniformity metric 1070D.

[0141] To identify a preferred processing axis, method 700 may include generating a set of uniformity metrics by repeatedly generating a set of expected sample characterization values ​​and determining a corresponding uniformity metric for each of a plurality of processing axes. Fig. 10E Indicates that the Fig. 10A The plane representation 1050A is rotated through a set of uniformity metrics 1070 for sample tilt angles 1044 within a range of ±10°, with each sample tilt angle separated by an interval of 0.25°.

[0142] As discussed above, the uniformity metric, when calculated as the difference between the maximum expected sample characterizing value and the minimum expected sample characterizing value, can be understood as representing the expected thickness (or variability in thickness) of the sample specimen when milled along the corresponding process axis. Thus, in some examples, the preferred process axis can be selected as the process axis corresponding to the minimum value in the set of uniformity metrics. Fig. 10EIn the example of , it can be seen that the minimum value of the uniformity metric set 1070 corresponds to a preferred sample tilt angle 1046 of approximately 4.5°.

[0143] exist FIG. 10A to FIG. 10E In the example, and as Fig. 10E As can be seen, the uniformity metric set 1070D associated with the planar representation 1050A (and / or a rotation thereof) can be described as being substantially symmetric with respect to the sample tilt angle 1044. Such a property can be associated with a corresponding symmetry of the planar representation. For example, because a version of the planar representation 1050A tilted by the sample tilt angle (e.g., as represented by the planar representation 1050C) is substantially mirror-symmetric to a version tilted the same degree in the opposite direction, the expected sample characterization values ​​associated with a given sample tilt angle and those associated with the inverse sample tilt angle in this example are substantially identical.

[0144] However, in other examples, the planar representation may have properties that cause the associated uniformity metric to be asymmetric with respect to the sample tilt angle. As an example, Fig.11A An example of a planar representation 1150A is illustrated in which the depicted features are non-uniform in shape and size, and Fig. 11B Illustrated with Fig.11A A set 1160B of expected sample characterization values ​​associated with a default orientation. Fig. 11C The example shows a rotated plane representation 1150C in which the plane representation 1150A is rotated by 9°. Fig.11E The example shows that the plane 1150A is rotated by -9° (i.e., Fig. 11C The rotation plane rotated 9° in the opposite direction of 1150E. Fig.11D and Fig.11F The corresponding Fig. 11C and Fig.11E Compare the expected sample characterization value sets 1160D and 1160F for the orientation. Fig.11D and Fig.11F As can be seen, the uniformity metrics 1170D / 1170F are asymmetric with respect to the sample tilt angle, where the sample characterization value sets 1160D / 1160F are expected to be visually distinct from each other in characteristics.

[0145] This asymmetry is Fig.11G As further illustrated in Fig.11G Indicates that the Fig.11A The plane representation 1150A is rotated by a set of uniformity metrics 1170 for sample tilt angles 1144 within the range of ±10°. Fig.11GIn the example of , it can be seen that the minimum value of the uniformity metric set 1170 corresponds to a preferred sample tilt angle 1146 of approximately +3°, which corresponds to a uniformity metric 1170 that is significantly smaller than the uniformity metric associated with a sample tilt angle of -3°.

[0146] exist Fig. 10E and Fig.11G In the example of , identifying a preferred sample tilt angle 1046 / 1146 corresponds to identifying a sample tilt angle 1044 / 1144 that minimizes a uniformity metric 1070 / 1170. In other examples, the preferred sample tilt angle may correspond to a sample tilt angle other than the sample tilt angle at which the uniformity metric is minimized. For example, while the preferred sample tilt angle may correspond to a relatively small uniformity metric value, there may be a range of uniformity metrics that are close to the minimum uniformity metric, where other considerations may be used to determine the preferred sample tilt angle.

[0147] As a more specific example, and as Fig.11G As shown, identifying the preferred processing axis at 740 may include identifying a subset of uniformity metrics 1170 that fall within a preferred range 1174 of uniformity metrics. In such examples, the preferred processing axis may be identified such that the uniformity metric 1170 corresponding to the preferred processing axis is within the preferred uniformity metric value range 1174.

[0148] In such examples, a preferred uniformity metric value range may be determined based at least in part on a minimum value of the uniformity metric. For example, a preferred uniformity metric value range may include a uniformity metric value range that is within a threshold percentage of the minimum uniformity metric. In such examples, the threshold percentage may take any of a number of values, examples of which include at least 1%, at least 5%, at least 10%, at least 20%, at most 25%, at most 15%, at most 7%, and / or at most 2%.

[0149] In examples where the preferred sample tilt axis is determined at least in part based on considerations other than strictly minimizing the uniformity metric, any suitable quantity may be used in such a determination. Such additional considerations may, for example, be based on other properties of the set of expected sample characterization values ​​associated with each sample tilt angle. In particular, in some examples, generating the set of uniformity metrics at 750 may include, for each processing axis, transforming the set of expected sample characterization values ​​into a quadratic uniformity metric. Thus, identifying the preferred processing axis at 740 may be based at least in part on a quadratic uniformity metric. As an example, the quadratic uniformity metric may represent an average expected sample characterization value of the set of expected sample characterization values, a maximum expected sample characterization value of the set of expected sample characterization values, a minimum expected sample characterization value of the set of expected sample characterization values, a standard deviation of the set of expected sample characterization values, and the like.

[0150] In some examples, therefore, identifying a preferred processing axis may include: generating a set of uniformity metrics to identify a minimum uniformity metric; determining a preferred subset of uniformity metrics that fall within a preferred range of uniformity metrics; and determining a preferred subset of processing axes corresponding to the preferred subset of uniformity metrics. Such a method may then include: identifying, among the preferred subset of processing axes, a processing axis corresponding to a secondary uniformity metric that is maximized, minimized, and / or otherwise preferred among the secondary uniformity metrics corresponding to the processing axes in the preferred range of processing axes.

[0151] Any aspect of the method disclosed herein may be performed at least partially or completely by a controller and / or automatically without human intervention. Figure 2 When performed on the CPM system 210 of the present invention, the methods disclosed herein can allow the sample specimen to be analyzed and milled along the preferred processing axis using the FIB system 211 after the sample specimen is extracted from the bulk sample 222, without removing the sample specimen from the lower chamber 226 and / or without breaking the vacuum formed in the lower chamber 226. Therefore, the methods disclosed herein can be performed during run time to mill the sample specimen along the preferred processing axis specifically determined for the sample specimen, without the need for manual analysis and / or manipulation of the sample specimen, thereby improving the quality of the thin section while reducing system downtime.

[0152] References Figures 7 to 11G Any of the methods disclosed in any of the above may also be described as including any of the methods disclosed herein. Figure 1 to Figure 2 Any of the disclosed method steps. For example, Figure 7 The method 700 may also include the steps of positioning a bulk sample (e.g., bulk sample 222) on a platform (e.g., bulk sample platform 225); extracting a sample specimen from the bulk sample; transferring the sample specimen to a sample holder (e.g., sample holder 224); and / or rotating the sample with the sample holder and / or platform by a sample tilt angle corresponding to a preferred processing axis. In examples where the method 700 includes processing the sample specimen at 780 to produce a thin section, the method 700 may also include analyzing the thin section using a TEM (e.g., SEM 241 operating in TEM mode).

[0153] Fig.12 The following discussion is intended to provide a brief, general description of an exemplary computing environment in which the disclosed techniques may be implemented. In particular, some or all portions of the computing environment may be used with the methods and apparatus described above to, for example, generate expected sample characterization values, generate planar representations, generate and / or process sample images, identify preferred processing axes, and / or perform any portion of the methods disclosed above.

[0154] Although not required, the disclosed technology is described in the general context of computer executable instructions executed by a personal computer (PC), such as program modules. Typically, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In addition, the technology of the present disclosure can be implemented with other computer system configurations including: handheld devices, tablet computers, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, microcomputers, mainframe computers, virtual machines, containerized applications, Kubernetes clusters, etc. The disclosed technology can also be practiced in a distributed computing environment where remote processing devices linked by a communication network perform tasks. In a distributed computing environment, program modules can be located in both local memory storage devices and remote memory storage devices. In some cases, such processing is provided in a CPM system. The disclosed system can be used to control image acquisition, and provide a user interface and act as an image processor.

[0155] refer to Fig.12 , an exemplary system for implementing the disclosed technology includes a general-purpose computing device in the form of an exemplary conventional PC 1200, which includes one or more processing units 1202, a system memory 1204, and a system bus 1206 that couples various system components including the system memory 1204 to the one or more processing units 1202. The system bus 1206 can be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The exemplary system memory 1204 includes a read-only memory (ROM) 1208 and a random access memory (RAM) 1210. A basic input / output system (BIOS) 1212, which contains the basic routines that help transfer information between elements within the PC 1200, is stored in the ROM 1208.

[0156] The exemplary PC 1200 also includes one or more storage devices 1230, such as a hard disk drive for reading from and writing to a hard disk, a magnetic disk drive for reading from or writing to a removable magnetic disk, and an optical disk drive for reading from or writing to a removable optical disk (such as a CD-ROM or other optical media). Such storage devices may be connected to the system bus 1206 via a hard disk drive interface, a magnetic disk drive interface, and an optical disk drive interface, respectively. The drives and their associated computer-readable media provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for the PC 1200. Other types of computer-readable media capable of storing data accessible by the PC, such as magnetic tape cartridges, flash memory cards, solid-state drives, digital video disks, CDs, DVDs, RAM, ROM, etc., may also be used in the exemplary operating environment.

[0157] A plurality of program modules may be stored in a storage device 1230 including an operating system, multiple operating systems, virtual operating systems, one or more application programs, other program modules, and / or program data. In some examples, one or more aspects of the methods disclosed herein may be programmed, implemented, encoded, trained, and / or otherwise transferred to a program module via machine learning, neural networks, artificial intelligence, and the like.

[0158] The exemplary PC 1200 may include various devices configured for user interface. For example, a user may enter commands and information into the PC 1200 via one or more input devices 1240 such as a keyboard and / or a pointing device such as a mouse. For example, a user may enter a command to start image acquisition and / or start one or more methods disclosed herein. Other input devices may include a digital camera, a microphone, a joystick, a game pad, a button, a dial, a satellite dish, a scanner, etc. In some examples, several such input devices may be integrated into a single user interface device, such as may be used in conjunction with a CPM system. These and other input devices are often connected to one or more processing units 1202 via a serial port interface coupled to the system bus 1206, but may be connected via other interfaces such as a parallel port, a game port, a universal serial bus (USB), or a wired or wireless network connection. A monitor 1246 or other type of display device is also connected to the system bus 1206 via an interface such as a video adapter, and may display one or more images of a sample or specimen before, after, and / or during the execution of one or more methods disclosed herein. Monitor 1246 may also be used to select segments or specific image alignments and alignment procedures for processing, such as correlation, feature identification, and preview area selection or other image selection. Other peripheral output devices may be included, such as speakers and printers (not shown).

[0159] PC 1200 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer 1260. In some examples, one or more network or communication connections 1250 are included. Remote computer 1260 can be another PC, a server, a router, a network PC and / or a peer device or other common network node, and typically includes many or all of the elements described above with respect to PC 1200, although in some examples, the remote computer 1260 may be a PC, a server, a router, a network PC, and / or a peer device or other common network node, and typically includes many or all of the elements described above with respect to PC 1200. Fig.12 Only memory storage device 1262 is shown. Personal computer 1200 and / or remote computer 1260 can be connected to a local area network (LAN) and / or a wide area network (WAN). Such networking environments are common in offices, enterprise-wide computer networks, intranets and the Internet.

[0160] like Fig.12 As shown, the memory 1290 (or a portion of the memory or other memory) may store processor-executable instructions for sample extraction, for identifying a preferred processing axis, and / or for processing a sample. For example, such processor-executable instructions may cause the PC 1200 and / or another component (e.g., Figure 1 The CPM system 100 and / or Figure 2 Any suitable component of the CPM system 210 of the present invention) can perform any of the methods disclosed herein. In addition, the memory 1290 can include processor-executable instructions for setting cross-correlation, image alignment such as image rotation and translation, selection of reference images and ROIs, and / or recording platform coordinates for alignment. In some examples, the processor-executable instructions can generate displayed images that show segment identification, processing of preview images, and / or acquisition of additional images.

[0161] General considerations

[0162] As used in this application and in the claims, the singular forms "a", "an", and "the" include plural forms unless the context clearly dictates otherwise. In addition, the term "comprising" means "including". Furthermore, the term "coupled" does not exclude the presence of intervening elements between the coupled items.

[0163] Unless otherwise indicated, the term "substantially" as used herein refers to the listed values ​​and / or properties and any values ​​and / or properties that are at least 75% of the listed values ​​and / or properties. Equivalently, the term "substantially" refers to the listed values ​​and / or properties and any values ​​and / or properties that differ from the listed values ​​and / or properties by at most 25%. For example, "substantially equal" refers to completely equal amounts, as well as amounts that differ from each other by at most 25%.

[0164] The systems, devices, and methods described herein should not be construed as limiting in any way. Rather, the present disclosure relates to all novel and non-obvious features and aspects of the various disclosed examples, either individually or in various combinations and sub-combinations formed with each other. The disclosed systems, methods, and devices are not limited to any particular aspect or feature or combination thereof, nor do the disclosed systems, methods, and devices require the presence of any one or more specific advantages or solutions to problems. Any theory of operation is for ease of explanation, but the disclosed systems, methods, and devices are not limited to such theory of operation.

[0165] Although the operations of some of the disclosed methods are described in a particular sequential order for ease of presentation, it should be understood that this manner of description includes rearrangement unless a particular order is required by the specific language described below. For example, the operations described in sequence may be rearranged or performed simultaneously in some cases. In addition, for simplicity, the accompanying drawings may not show the various ways in which the disclosed systems, methods, and devices can be used in conjunction with other systems, methods, and devices. In addition, the specification sometimes uses terms like "produce" and "provide" to describe the disclosed methods. These terms are high-level abstractions of the actual operations performed. The actual operations corresponding to these terms will vary depending on the specific implementation and can be easily discerned by those skilled in the art.

[0166] In some examples, values, procedures, etc. may be characterized by qualifying terms such as "lowest," "best," "minimum," "extreme," etc. It should be understood that such descriptions are intended to indicate that a selection may be made among a number of functional alternatives used, and that such selection is not necessarily better, lesser, or otherwise preferred than other selections.

[0167] As used herein, the term "image" may refer to a displayed view of a specimen (e.g., as presented on a display device) and / or stored data that may be used to generate the displayed image. Examples of such stored data include digital data such as stored in a non-transitory computer-readable medium, such as, for example, JPG, TIFF, BMP, or other formats.

[0168] Innovations may be described in the general context of computer executable instructions, such as those instructions included in program modules and executed in a computing system on a target real or virtual processor. In general, program modules or components include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The functions of program modules may be combined or split between program modules as needed in various examples. Computer executable instructions for program modules may be executed in a local or distributed computing system. In general, a computing system or computing device may be local or distributed, and may include any combination of dedicated hardware and / or general hardware with software that implements the functions described herein, and examples of the computing system or computing device include personal computers, handheld devices, tablet computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, virtual machines, containerized applications, Kubernetes clusters, etc.

[0169] In various examples described herein, a module (e.g., a component or an engine) may be "programmed" and / or "coded" to perform certain operations or provide certain functionality, indicating that computer executable instructions for the module may be executed to perform such operations, cause such operations to be performed, or otherwise provide such functionality. Although functionality described with respect to a software component, module, or engine may be performed as a discrete software unit (e.g., a procedure, a function, a class method), it need not be implemented as a discrete unit. That is, functionality may be incorporated into a larger or more general program, such as one or more lines of code in a larger or general program.

[0170] The algorithm described may, for example, be embodied as software or firmware instructions executed by a digital computer. For example, any of the disclosed methods may be performed by one or more of a computer or other computing hardware that is part of a microscopy tool. The computer may be a computer system comprising one or more processors (processing devices) and a tangible, non-transient computer-readable medium (e.g., one or more optical media disks, volatile memory devices (such as DRAM or SRAM), or non-volatile memory or storage devices (such as hard disk drives, NVRAM, and solid-state drives (e.g., flash drives)). The one or more processors may execute computer-executable instructions stored on one or more tangible, non-transient computer-readable media, and thereby perform any of the disclosed technologies. For example, software for executing any of the disclosed examples may be stored as computer-executable instructions on the one or more volatile, non-transient computer-readable media, which, when executed by the one or more processors, cause the one or more processors to perform any of the disclosed technologies or technology subsets.

[0171] The principles of the disclosed technology have been described and illustrated with reference to the illustrated examples, and it should be appreciated that the illustrated examples may be modified in arrangement and detail without departing from such principles. For example, the example elements executed in software may be implemented in hardware, and vice versa. In addition, the techniques from any example may be combined with the techniques described in any one or more of the other examples. It should be understood that processes and functions such as those described with reference to the illustrated examples may be implemented in a single hardware or software module, or separate modules may be provided. The above specific arrangements are provided for ease of description, and other arrangements may be used.

[0172] Additional Embodiments of the Disclosed Technology

[0173] In view of the above specific implementation of the disclosed subject matter, the present application discloses the additional embodiments listed below. It should be noted that one feature of a separate example or one or more features of the example adopted in combination and optionally in combination with one or more features of one or more additional examples are also additional examples belonging to the disclosure of the present application.

[0174] Embodiment 1. A method, comprising: generating, with a controller, a set of predicted sample characterization values ​​associated with each of a plurality of processing axes; and identifying, with the controller, a preferred processing axis based on the predicted sample characterization values.

[0175] Embodiment 2. A method according to embodiment 1, wherein generating the set of predicted sample characterization values ​​comprises, for each of the plurality of processing axes: generating a planar representation of the sample specimen, wherein the planar representation comprises sample characterization values ​​mapped onto a two-dimensional array of representation positions corresponding to corresponding portions of the sample specimen; and predicting the sample characterization values ​​along an axis of the planar representation corresponding to the processing axis.

[0176] Embodiment 3. The method of embodiment 2, wherein said generating said set of expected sample characterizing values ​​comprises: generating such that said set of expected sample characterizing values ​​has reduced dimensionality relative to said planar representation of said sample specimen.

[0177] Embodiment 4. A method according to any embodiment herein, in particular any one of embodiments 2 to 3, wherein the sample specimen comprises one or more high aspect ratio features extending through the sample specimen along a feature axis, and wherein the planar representation represents the sample specimen as viewed in a plane at least substantially perpendicular to the feature axis.

[0178] Embodiment 5. The method according to any embodiment herein, in particular any one of embodiments 2 to 4, wherein each sample feature value is a scalar.

[0179] Embodiment 6. A method according to any embodiment herein, in particular any one of embodiments 2 to 4, wherein each sample feature value comprises a multidimensional quantity.

[0180] Embodiment 7. The method according to any embodiment herein, in particular any one of embodiments 2 to 6, wherein each sample characterization value comprises a numerical quantity characterizing a material property associated with the corresponding representation location.

[0181] Embodiment 8. The method of Embodiment 7, wherein the material property comprises a density of the sample specimen at a location corresponding to the representative location.

[0182] Embodiment 9. The method according to any embodiment herein, in particular any one of embodiments 7 to 8, wherein the material property comprises a relative rate at which a portion of the sample specimen corresponding to the representative location will be milled by the ion beam.

[0183] Embodiment 10. The method according to any embodiment herein, in particular any one of embodiments 2 to 9, wherein said generating said planar representation of said sample specimen is performed at least in part by said controller.

[0184] Embodiment 11. A method according to any embodiment herein, in particular any one of embodiments 2 to 10, wherein the predicting of the sample characterization value along the axis of the planar representation comprises one or both of the following: (i) applying a rotational transformation to the planar representation at an angle corresponding to the processing axis; and (ii) predicting the sample characterization value along a direction angled relative to the planar representation at an angle corresponding to the processing axis.

[0185] Embodiment 12. A method according to any embodiment herein, in particular any one of embodiments 2 to 11, wherein generating the planar representation of the sample specimen comprises: generating a sample image of the sample specimen; and processing the sample image of the sample specimen using the controller to generate the planar representation of the sample specimen.

[0186] Embodiment 13. The method of embodiment 12, wherein the representative positions correspond to corresponding portions of the sample image.

[0187] Embodiment 14. The method according to any embodiment herein, in particular any one of embodiments 12 to 13, wherein said generating said sample image comprises: capturing a microscopic image of said sample specimen.

[0188] Embodiment 15. The method according to any embodiment herein, in particular any one of embodiments 12 to 14, wherein the sample specimen comprises an exposed surface, and wherein generating the sample image comprises imaging the exposed surface.

[0189] Embodiment 16. The method of embodiment 15, wherein the exposed surface extends at least substantially in a surface plane, and wherein imaging the exposed surface comprises imaging such that a plane of the sample image corresponds to the surface plane.

[0190] Embodiment 17. The method according to any embodiment herein, in particular any one of embodiments 12 to 16, wherein said generating said sample image comprises: using said controller to retrieve a previously captured image of said sample specimen.

[0191] Embodiment 18. A method according to any embodiment herein, in particular any one of embodiments 12 to 17, wherein the processing of the sample image comprises: mapping the sample characterization values ​​onto the two-dimensional representation position array based at least in part on the sample image.

[0192] Embodiment 19. A method according to embodiment 18, wherein the sample image includes multiple image regions, each image region includes corresponding one or more image pixels having corresponding image intensity values, wherein each representation position represents a corresponding image region, and wherein mapping the sample characterization value onto the two-dimensional representation position array includes: assigning a corresponding sample characterization value to each representation position based at least in part on the image intensity value of the one or more image pixels of the corresponding image region.

[0193] Embodiment 20. The method of Embodiment 19, wherein the image intensity values ​​include grayscale values.

[0194] Embodiment 21. A method according to any embodiment herein, in particular any one of embodiments 19 to 20, wherein the mapping of the sample characterization values ​​onto the two-dimensional representation position array is performed such that each sample characterization value corresponds to the image intensity value of the one or more image pixels of the corresponding image area according to a predetermined transfer function.

[0195] Embodiment 22. A method according to any embodiment herein, in particular any one of embodiments 19 to 21, wherein processing the sample image comprises applying a threshold filter to the sample image to redistribute the corresponding image intensity values ​​of one or more pixels having image intensity values ​​that are above or below a threshold.

[0196] Embodiment 23. A method according to any embodiment herein, in particular any one of embodiments 19 to 22, wherein mapping the sample characterization values ​​onto the two-dimensional array of representation locations is performed at least in part based on one or more known material properties of the sample specimen.

[0197] Embodiment 24. A method according to any embodiment herein, in particular any one of embodiments 12 to 23, wherein processing the sample image comprises: masking the sample image to isolate one or more features of the sample image.

[0198] Embodiment 25. A method according to any embodiment herein, in particular any one of embodiments 2 to 24, wherein generating the planar representation of the sample specimen comprises: determining a sample material property corresponding to each representation position; and assigning the sample characterization value to the representation position based at least in part on the sample material property.

[0199] Embodiment 26. The method of Embodiment 25, wherein the determining the sample material property comprises making a determination based at least in part on a known material composition of the sample specimen.

[0200] Embodiment 27. The method according to any embodiment herein, in particular any one of embodiments 25 to 26, wherein the determining the sample material property comprises: making a determination based at least in part on one or more features of a sample image of the sample specimen.

[0201] Embodiment 28. A method according to any embodiment herein, in particular any one of embodiments 1 to 27, wherein the sample specimen includes structural features arranged to produce periodically repeating material properties in the sample specimen, and wherein the identifying the preferred processing axis includes: identifying so that the structural features are at least partially misaligned with each other when observed along the preferred processing axis.

[0202] Embodiment 29. A method according to any embodiment herein, in particular any one of embodiments 1 to 28, wherein the identifying the preferred processing axis comprises: using the controller to generate a set of uniformity metrics corresponding to the multiple processing axes, wherein each uniformity metric corresponds to the set of expected sample characterization values ​​associated with the corresponding processing axis; using the controller to identify a preferred uniformity metric in the set of uniformity metrics; and using the controller to identify the preferred processing axis as the processing axis corresponding to the preferred uniformity metric.

[0203] Embodiment 30. The method of embodiment 29, wherein each uniformity metric comprises a quantity having a reduced dimension relative to the expected set of sample characterization values ​​associated with the corresponding processing axis.

[0204] Embodiment 31. A method according to any embodiment herein, in particular any one of embodiments 29 to 30, wherein generating the set of uniformity metrics comprises, for each of the plurality of processing axes: generating a planar representation of the sample specimen at an orientation corresponding to the processing axis, wherein the planar representation comprises sample characterization values ​​mapped onto a two-dimensional array of representation positions corresponding to corresponding portions of the sample specimen; generating the set of expected sample characterization values ​​based on the planar representation and the processing axis; and transforming the set of expected sample characterization values ​​into the uniformity metric.

[0205] Embodiment 32. The method of embodiment 31, wherein said generating said set of uniformity metrics further comprises: pruning said set of expected sample characterization values ​​before transforming said set of expected sample characterization values ​​into said uniformity metrics.

[0206] Embodiment 33. A method according to any embodiment herein, in particular any one of embodiments 31 to 32, wherein generating the planar representation comprises: generating a default planar representation of the sample specimen at an orientation corresponding to a default processing axis; and rotating the default planar representation of the sample specimen by an angle corresponding to the difference between the processing axis and the default processing axis.

[0207] Embodiment 34. A method according to any embodiment herein, in particular any one of embodiments 31 to 33, wherein generating the expected set of sample characterization values ​​comprises: dividing the planar representation into a plurality of representation slices, each representation slice comprising a corresponding subset of sample characterization values; and for each representation slice, transforming the corresponding subset of sample characterization values ​​corresponding to the representation slice into corresponding expected sample characterization values ​​of the expected set of sample characterization values.

[0208] Embodiment 35. The method of embodiment 34, wherein transforming the respective subset of sample characterization values ​​into the corresponding expected sample characterization values ​​comprises summing the sample characterization values ​​within each representation slice to produce the corresponding expected sample characterization values.

[0209] Embodiment 36. A method according to any embodiment herein, in particular any one of embodiments 34 to 35, wherein the transforming of the corresponding subset of sample characterization values ​​into the corresponding expected sample characterization values ​​comprises: calculating one or more of: (i) the average sample characterization value within each representation slice; (ii) the maximum sample characterization value within each representation slice; and (iii) the distribution of sample characterization values ​​within each representation slice.

[0210] Embodiment 37. A method according to any embodiment herein, in particular any one of embodiments 31 to 36, wherein the transforming of the set of expected sample characterization values ​​into the uniformity metric comprises: calculating one or more of: (i) an average expected sample characterization value of the set of expected sample characterization values; (ii) a maximum expected sample characterization value in the set of expected sample characterization values; (iii) a minimum expected sample characterization value in the set of expected sample characterization values; and (iv) a standard deviation of the set of expected sample characterization values.

[0211] Embodiment 38. A method according to any embodiment herein, in particular any one of embodiments 31 to 37, wherein the transforming of the set of expected sample characterization values ​​into the uniformity metric comprises calculating the uniformity metric as the difference between the maximum expected sample characterization value and the minimum expected sample characterization value in the set of expected sample characterization values.

[0212] Embodiment 39. A method according to any embodiment herein, in particular any one of embodiments 29 to 38, wherein generating the set of uniformity metrics comprises individually determining the uniformity metric corresponding to each of the multiple processing axes, such that each processing axis is associated with a corresponding uniformity metric value.

[0213] Embodiment 40. A method according to any embodiment herein, in particular any one of embodiments 29 to 39, wherein generating the set of uniformity metrics comprises determining a uniformity metric at each of a plurality of processing axes in a test range of the processing axes.

[0214] Embodiment 41. The method of embodiment 40, wherein the testing range covers a processing axis range of approximately -10 degrees to +10 degrees.

[0215] Embodiment 42. A method according to any embodiment herein, in particular any one of embodiments 29 to 41, wherein the identifying the preferred processing axis comprises: identifying so that the preferred processing axis corresponds to the minimum value of the uniformity metric in the uniformity metric set.

[0216] Embodiment 43. A method according to any embodiment herein, in particular any one of embodiments 29 to 42, wherein identifying the preferred processing axis comprises: identifying a subset of the uniformity metric set having uniformity metric values ​​within a preferred uniformity metric value range; and identifying the preferred processing axis so that the uniformity metric corresponding to the preferred processing axis is within the preferred uniformity metric value range.

[0217] Embodiment 44. A method according to embodiment 43, wherein the identifying the subset of the uniformity metric set includes: identifying so that each uniformity metric value in the preferred uniformity metric value range is within a threshold percentage of a minimum uniformity metric of the preferred uniformity metric value range.

[0218] Embodiment 45. A method according to Embodiment 44, wherein the threshold percentage is one or more of at least 1%, at least 5%, at least 10%, at least 20%, at most 25%, at most 15%, at most 7% and at most 2%.

[0219] Embodiment 46. A method according to any embodiment herein, in particular any one of embodiments 29 to 45, wherein generating the set of uniformity metrics comprises, for each of the plurality of processing axes: generating a planar representation of the sample specimen at an orientation corresponding to the processing axis, wherein the planar representation comprises sample characterization values ​​mapped onto a two-dimensional array of representation positions corresponding to respective portions of the sample specimen; generating an expected set of sample characterization values ​​based on the planar representation and the processing axis; and transforming the expected set of sample characterization values ​​into a secondary uniformity metric, and wherein identifying the preferred processing axis is based at least in part on the secondary uniformity metric.

[0220] Embodiment 47. A method according to embodiment 46, wherein the secondary uniformity metric represents one or more of the following: (i) an average expected sample characterization value of the set of expected sample characterization values; (ii) a maximum expected sample characterization value in the set of expected sample characterization values; (iii) a minimum expected sample characterization value in the set of expected sample characterization values; and (iv) a standard deviation of the set of expected sample characterization values.

[0221] Embodiment 48. A method comprising one or more of the following: extracting a sample specimen from a bulk sample positioned in a chamber of a charged particle microscope (CPM) system; imaging the sample specimen to produce a sample image representing a surface plane of the sample specimen; generating a set of expected sample characterization values ​​associated with each of a plurality of sample tilt angles, each expected sample characterization value representing a feature of the sample image expected along a processing axis corresponding to the sample tilt angle; identifying a preferred sample tilt angle among the plurality of sample tilt angles; rotating the sample specimen to an orientation corresponding to the sample tilt angle; and processing the sample specimen using a focused ion beam (FIB) system of the CPM system to produce a thin section.

[0222] Embodiment 49. A method according to embodiment 48, wherein the chamber is a sealed chamber, and wherein one or more of extracting the sample specimen, imaging the sample specimen, identifying the preferred sample tilt angle, rotating the sample specimen, and processing the sample specimen are performed while the chamber remains sealed.

[0223] Embodiment 50. The method according to any embodiment herein, in particular any one of embodiments 48 to 49, wherein said imaging of said sample specimen comprises: imaging using a scanning electron microscope (SEM) of said CPM system.

[0224] Embodiment 51. The method according to any embodiment herein, in particular any one of embodiments 48 to 50, further comprises: after processing the sample specimen to produce the thin slice, performing transmission electron microscopy (TEM) analysis on the thin slice.

[0225] Embodiment 52. A computer-readable medium storing processor-executable instructions, which, when executed by a processor system, cause the processor system to perform a method according to any embodiment herein, in particular any one of embodiments 1 to 51.

[0226] Embodiment 53. A system comprising: a focused ion beam (FIB) system, the FIB system being configured to guide and focus an ion beam toward a sample specimen along a processing axis; a sample holder, the sample holder being configured to support the sample specimen relative to the ion beam; a platform, the platform being configured to rotate the sample specimen relative to the ion beam; and a controller, the controller comprising a processor system and a computer-readable medium, the computer-readable medium storing processor-executable instructions, the processor-executable instructions, when executed by the processor system, causing the processor system to perform a method according to any one of Embodiments 1 to 51.

[0227] Embodiment 54. The system of Embodiment 53 further comprises: a scanning electron microscope (SEM) configured to direct and focus an electron beam toward the sample specimen.

[0228] Embodiment 55. The system of Embodiment 54, wherein the FIB system is configured to generate a thin section from the sample specimen, and wherein the SEM is configured to perform transmission electron microscopy (TEM) on the thin section.

[0229] Unless otherwise stated, the features described herein with respect to any embodiment may be combined with other features described in any one or more other embodiments. For example, any one or more steps and / or features of a method may be combined with any one or more steps and / or features of another method.

[0230] In view of the many possible ways in which the principles of the present disclosure may be applied, it should be recognized that the illustrated configurations depict examples of the disclosed technology and should not be taken as limiting the scope of the disclosure or the claims. Rather, the scope of the claimed subject matter is defined by the following claims and their equivalents.

Claims

1. A method, comprising: generating, with a controller, a set of predicted sample characterization values ​​associated with each of a plurality of processing axes; as well as A preferred processing axis is identified, using the controller, based on the predicted sample characterization value.

2. The method of claim 1 , wherein said generating said set of expected sample characterization values ​​comprises, for each of said plurality of processing axes: generating a planar representation of a sample specimen, wherein the planar representation comprises sample characterizing values ​​mapped onto a two-dimensional array of representation locations corresponding to respective portions of the sample specimen; and The sample characterizing values ​​are projected along an axis of the planar representation corresponding to the processing axis. 3 . The method of claim 2 , wherein each sample characterizing value comprises a numerical quantity characterizing a material property associated with the corresponding representative location.

4. The method of claim 2, wherein said generating said planar representation of said sample specimen comprises: determining a property of the sample material corresponding to each indicated location; as well as The sample characterizing value is assigned to the representative location based at least in part on the sample material property.

5. The method of claim 4, wherein said determining said sample material property comprises: The determination is made based at least in part on a known material composition of the sample specimen.

6. The method of claim 4, wherein said determining said sample material property comprises: The determination is made based at least in part on one or more features of a sample image of the sample specimen.

7. The method of claim 2, wherein said generating said planar representation of said sample specimen comprises: generating a sample image of the sample specimen; as well as processing the sample image of the sample specimen with the controller to generate the planar representation of the sample specimen, Wherein said processing said sample image comprises: mapping said sample characterizing values ​​onto said two-dimensional array of representation positions based at least in part on said sample image.

8. The method of claim 7, wherein said generating said sample image comprises one or both of the following: (i) capturing a microscopic image of the sample specimen; and (ii) retrieving, with the controller, a previously captured image of the sample specimen.

9. The method according to claim 7, wherein said processing said sample image comprises: A threshold filter is applied to the sample image.

10. The method of claim 2, wherein the predicting the sample characterizing value along the axis of the planar representation comprises one or both of: (i) applying a rotational transformation to the planar representation at an angle corresponding to the processing axis; and (ii) projecting the sample characterizing value along a direction that is angled relative to the planar representation at an angle corresponding to the processing axis.

11. The method of claim 1 , wherein said identifying said preferred processing axis comprises: generating, with the controller, a set of uniformity metrics corresponding to the plurality of processing axes, wherein each uniformity metric corresponds to the set of expected sample characterization values ​​associated with a corresponding processing axis; identifying, with the controller, a preferred uniformity metric from the set of uniformity metrics; as well as The preferred processing axis is identified, with the controller, as the processing axis corresponding to the preferred uniformity metric.

12. The method of claim 11, wherein said generating said set of uniformity metrics comprises, for each of said plurality of processing axes: generating a planar representation of a sample specimen at an orientation corresponding to the processing axis, wherein the planar representation includes sample characterizing values ​​mapped onto a two-dimensional array of representation locations corresponding to respective portions of the sample specimen; generating the set of expected sample characterization values ​​based on the plane representation and the processing axis; as well as The set of expected sample characterization values ​​is transformed into the uniformity metric.

13. The method of claim 12, wherein said transforming said set of expected sample characterization values ​​into said uniformity metric comprises: The uniformity metric is calculated as a difference between a maximum expected sample characterizing value and a minimum expected sample characterizing value in the set of expected sample characterizing values.

14. The method of claim 12, wherein said generating said planar representation comprises: generating a default planar representation of the sample specimen in an orientation corresponding to a default processing axis; as well as The default planar representation of the sample specimen is rotated by an angle corresponding to the difference between the processing axis and the default processing axis.

15. The method of claim 11, wherein said identifying said preferred processing axis comprises: The preferred processing axis is identified such that it corresponds to a minimum value of the uniformity metric among the set of uniformity metrics.

16. A method comprising: extracting a sample specimen from a bulk sample positioned within a chamber of a charged particle microscope (CPM) system; imaging the sample specimen to produce a sample image representing a surface plane of the sample specimen; generating a set of predicted sample characterization values ​​associated with each sample tilt angle of a plurality of sample tilt angles, each predicted sample characterization value representing a characteristic of the sample image predicted along a processing axis corresponding to the sample tilt angle; identifying a preferred sample tilt angle among the plurality of sample tilt angles; rotating the sample specimen to an orientation corresponding to the sample tilt angle; as well as The sample specimen is processed using a focused ion beam (FIB) system of the CPM system to produce thin sections.

17. The method of claim 16, wherein the chamber is a sealed chamber, and wherein one or more of extracting the sample specimen, imaging the sample specimen, identifying the preferred sample tilt angle, rotating the sample specimen, and processing the sample specimen are performed while the chamber remains sealed.

18. The method of claim 16, wherein imaging the sample specimen comprises: Imaging is performed using a scanning electron microscope (SEM) of the CPM system, and wherein the method further comprises, after processing the sample specimen to produce the lamella, performing a transmission electron microscopy (TEM) analysis on the lamella using the SEM.

19. A system, comprising: a focused ion beam (FIB) system configured to direct and focus an ion beam along an ion beam axis toward a sample specimen; a stage configured to rotate the sample specimen relative to the ion beam; and A controller comprising a processor system and a computer-readable medium storing processor-executable instructions that, when executed by the processor system, cause the processor system to: Generating a set of expected sample characterizing values ​​associated with each of a plurality of processing axes, wherein said generating said set of expected sample characterizing values ​​comprises, for each of said plurality of processing axes: generating a planar representation of a sample specimen, wherein the planar representation comprises sample characterizing values ​​mapped onto a two-dimensional array of representation locations corresponding to respective portions of the sample specimen; and projecting the sample characterizing value along an axis of the planar representation corresponding to the processing axis; identifying a preferred processing axis based on the predicted sample characterization value; as well as The stage is caused to rotate the sample specimen relative to the ion beam axis such that the ion beam approaches the sample specimen along the preferred processing axis.

20. The system of claim 19, further comprising: a scanning electron microscope (SEM) configured to direct and focus an electron beam toward the sample specimen to capture a microscopic image of the sample specimen, wherein the processor-executable instructions, when executed by the processor system, cause the processor system to: generate the planar representation of the sample specimen based at least in part on the microscopic image, wherein the FIB system is configured to generate a lamella from the sample specimen, and wherein the SEM is configured to perform transmission electron microscopy (TEM) on the lamella.

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