Focus stacking applications for sample preparation

Abstract

Methods and apparatus apply focus stacks to sample preparation, which improves the accuracy of analysis tasks, facilitates automation, and improves throughput. A focus stack is applied to a set of sample images having different focal depths to produce a composite image and optionally a depth map in which features at different depths are in focus alignment. Sample locations are selected from the composite image, and localized material removal, measurement, or imaging operations are performed based on the sample locations. Depth values from the depth map are used to set a working depth of a tool for performing the local operation. Applications include sheet preparation for low temperature TEM analysis of biological samples. Other applications, techniques, and variants are disclosed.

Description

Background Art

[0001] Transmission electron microscopy (TEM) requires a thin sample (typically 10nm to 150nm thick) called a lamella. Lamella preparation can involve, for example, imaging to find the feature to be analyzed by TEM and milling to prepare the lamella to introduce the feature. Imaging-based workflows can also be applied in other sample preparation applications.

[0002] Some tools such as focused ion beam (FIB) or fluorescence microscopes may have a depth of focus that is smaller than the depth variation of the sample area being imaged, which may present a challenge for imaging using these tools. In some workflows, this problem can be circumvented by performing surface imaging using a scanning electron microscope (SEM) with a large depth of focus, and features identified in the SEM image can be used to guide subsequent FIB milling. However, when the viewing axes of the SEM and FIB are different, correlating an SEM view of a surface with a FIB view of the same surface can be difficult and error-prone, especially for surfaces with significant topographic variations ("ridged surfaces").

[0003] In other workflows, fluorescence imaging can be used to locate features inside a sample and cannot be replaced by SEM imaging. Using fluorescence images to guide thin section preparation may require screening through a series of fluorescence images taken at different focal depths to identify features for TEM imaging. This process can be tedious and is exacerbated by the inability to easily visualize relevant features at different depths.

[0004] Therefore, there remains a need for improved techniques for imaging as a guide to sample preparation in a range of applications not limited to the fabrication of TEM lamellae. Summary of the invention

[0005] In brief, examples of the disclosed technology use focus stacking to merge and present sample features at different depths all in focus in a single composite image. Sample locations can be selected from the composite image interactively by a user or by automated software. Local operations can be performed on the sample based on the selected location.

[0006] In some examples, focus stacking can also generate a depth map that can directly indicate the depth value of the selected location or the depth value at another location that has a predetermined spatial relationship with the selected location. The combination of the lateral coordinate of the composite image pixel and the depth value from the depth map can accurately specify the three-dimensional location of a given feature. The working depth of the tool can be set accordingly to accurately perform local operations.

[0007] In particular, the disclosed techniques can be applied to environments with topographic variations on the sample surface, glancing beams for imaging or milling, or beams with limited focal depth (e.g., comparable to or smaller than the sample size). The disclosed techniques can address the challenge of achieving nanometer precision when working with samples and tools with micrometer-scale variations. In one application, the disclosed techniques can be applied to milling TEM lamellae from samples, thereby increasing throughput for various reasons further described herein. However, the disclosed techniques are broadly applicable to improving workflow automation or improving the accuracy or yield of imaging-based analytical operations. Such operations may include material removal, measurement, or further imaging.

[0008] The above and other objects, features and advantages of the present invention will become more apparent from the following detailed description made with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a diagram of an example device by which the disclosed techniques may be implemented.

[0010] Figure 2 is a diagram illustrating sample installation configurations for some examples of the disclosed technology.

[0011] Figure 3 is a diagram illustrating an example beam and sample configuration of the disclosed technique.

[0012] Figure 4 is an illustration of pattern milling in an example of the disclosed technology.

[0013] Figure 5 is a collection of images illustrating a first example application of the disclosed technology.

[0014] Figure 6 is a flowchart depicting an example method according to the disclosed technology.

[0015] Figure 7 is a flow chart of a focus stacking method that may be used with the disclosed technology.

[0016] Figure 8 is a diagram illustrating generation of a focused image and a depth map in an example of the disclosed technology.

[0017] Fig. 9 is a collection of images illustrating focus stacking in a second example application of the disclosed technology.

[0018] FIG. 10A to FIG. 10B Provided together are a collection of images illustrating focus stacking in a third example application of the disclosed technology.

[0019] FIG. 11A to FIG. 11CProvided together is a collection of images illustrating a fourth example application of the disclosed technology.

[0020] Fig.12 A generalized example of a suitable computing environment in which the described embodiments, techniques, and technologies related to imaging or milling may be implemented is illustrated. DETAILED DESCRIPTION

[0021] Introduction

[0022] Transmission electron microscopy (TEM) is increasingly used to analyze biological samples as well as materials science samples and semiconductor devices. In particular, cryo-electron microscopy enables structure determination in the absence of a crystal structure.

[0023] Throughput may be a key consideration in many applications. Typically, multiple sample sites can be prepared on a single substrate. For example, a FIB tool can be used to prepare the sample site as a thin slice. Placing the substrate at an oblique angle can bring more sample sites within the limited field of view of the FIB column. For illustration, compared with normal incidence, the sample area in the FIB field of view can be increased by about 1 / sin(10°)=5.75 relative to the FIB beam axis (i.e., angle of incidence 80°) with a 10° grazing angle orientation substrate surface. Glancing FIB provides another advantage, i.e., the sample material above and below the thin slice can be milled away, thereby allowing the thin slice to be attached to the original substrate for use, without, for example, separation and reattachment to a separate thin slice holder.

[0024] Therefore, it may be desirable to identify features and sample sites seen along the FIB beam axis. A FIB tool used for milling (e.g., at high beam current) can also be used for imaging (e.g., at low beam current). However, the depth of focus of a FIB beam may be limited to approximately 20 μm to 50 μm, which may be smaller than the size of a single 100 μm sample site, not to mention a typical 3 mm aperture grid plate.

[0025] To avoid the problem of depth of focus, some workflows use normal incidence SEM imaging of the sample. Although surface features can be easily identified from SEM images, it can be challenging to correlate feature positions from SEM images with FIB viewing angles for reasons including geometric correction for topographic variations (e.g., "raised surfaces"), contrast or edge diffraction variations between SEM and FIB, and time-dependent fluctuations in beam steering. The time spent on image correlation between SEM and FIB and the propensity for errors directly reduce the achievable throughput.

[0026] The disclosed technique applies focal stacking to allow FIB images (even at glancing incidence) to be used directly for feature identification. Thus, the same tool can be used for imaging and subsequent milling to prepare lamellae. Imaging can also be performed between milling operations to track and compensate for beam drift. In addition, focal stacking can provide a depth map as an auxiliary output, so that the working depth at or near the identified features can be accurately known, and the FIB milling can be set accordingly. Because changes in the beam focal depth are related to changes in the beam spot size, precise control of the FIB milling machine can directly provide tight tolerances on the resulting lamella thickness, which can be a critical dimension in TEM applications.

[0027] In other applications, features can be identified within the interior volume of a sample, for example, by fluorescence imaging of fluorophores attached to proteins or other biomolecules. Like FIB, fluorescence microscopy may also have a limited depth of focus, and full imaging of the sample volume may require a stack of 10 to 100 fluorescence images at different focal depths. Some workflows allow manual scanning between these images, such as using a slider on an interactive display to select or change image slices, but features at different depths cannot be shown simultaneously.

[0028] The disclosed technology applies focus stacking to produce a composite image that includes focused features from a wide range of depths. The ability to simultaneously visualize features at different depths can provide reliable assessments of the environment of the feature or of the features of a larger object of which the feature is a part. Specifically, the lateral distance in the composite image or the depth value in the accompanying depth map can provide clues about the orientation of the larger object or feature. Using this information, a user or an automated program can make reliable selections of those features that are advantageously oriented for TEM imaging or lamella preparation. In addition, the orientation information can guide the selection of lamella orientation so that the selected features have the desired orientation within the lamella for TEM analysis. In turn, the selected lamella orientation can guide the arrangement of the milling pattern used to form the lamella. Three-dimensional features and lamella placement information can be transformed from fluorescent beam coordinates (or sample coordinates) to FIB coordinates for milling using triangulation or matrix operations.

[0029] Focus stacking and depth map generation can be performed by automated software and can be easily integrated with the preceding steps (e.g., image acquisition) and subsequent steps (e.g., feature selection or pattern milling) in an automated or semi-automated workflow. Focus stacking and depth map generation are fast processes, typically taking 1ms to 10ms or 10ms to 100ms on a workstation or equipment controller, depending on the number and size of individual images.

[0030] the term

[0031] Unless otherwise expressly noted or contradictory to the context, the usage and meaning of all referenced terms in this section apply to the entire disclosure. The following terms are expanded to related word forms. For convenience, the terms in this section are organized into subsections (A) Overview, (B) Geometry, (C) Imaging, (D) Samples, and (E) Tools and Techniques for Sample Analysis.

[0032] A. Summary

[0033] A "controller" is an electronic device coupled to one or more actuators to effect a change in a physical parameter, or coupled to one or more sensors to monitor a physical parameter. Some controllers may include a microprocessor that may be programmed to execute machine-readable instructions. The description of computing devices herein generally applies to such controllers. Such controllers may include additional electronic circuitry, such as filters and amplifiers. Other controllers may include analog circuitry, such as filters and amplifiers, without any microprocessor.

[0034] A "parameter" is a quantity that can have a specific "value". While parameters often have numeric values, this is not required, and some parameter values ​​can be logical values, strings, or data structures. Some parameters can be control parameters, such as those set by a controller to affect a physical device or physical operation. Other parameters can be sensed parameters, such as those determined by the physical environment and measured or evaluated (e.g., to form an image or to measure or evaluate from an image) by a controller or measurement device.

[0035] "Software" refers to a computer executable program, instructions, or associated data structures. Software may be in an active or static state. In an active state, the software may be loaded into a memory or may be executed by one or more processors. In a static state, the software may be stored on a computer-readable medium, awaiting transmission or execution. "Instructions" (or "program instructions") encode the operations to be performed by the processor (sometimes together with one or more operands, operand addresses, or destination addresses). A set of instructions may be organized to perform a function. "Interactive software" refers to a software program that presents information to a user and receives commands from the user in response to the information presented. "Automated software" refers to a software program that receives information from equipment or from other software programs and selects one or more actions to be performed by equipment or other software programs without user input.

[0036] A "user" is a person. An exemplary user may control an analytical process or equipment, or may receive presentations or notifications of data from such analytical equipment. A user may interact with equipment, samples, or software via a communications network, a computer input or output interface, or client software.

[0037] B. Geometry

[0038] "Depth" refers to a coordinate in a direction along the beam, where the depth value increases as one moves away from the source of the beam. Thus, in a multi-beam system, for example, with an electron microscope viewing the sample from directly above and a FIB with the electron beam oriented at about 50°, each beam will have its own depth coordinate. "Working depth" is the depth at or near the depth at which the beam is expected to interact with the sample. Some tools have beam controls for setting the working depth, which may correspond to the depth at which the beam is focused or has a minimum cross-sectional area, such as a "beam waist." "Depth of focus" is the depth at which the beam has optimal focus, which may be the depth at which the lateral extent of the beam is the smallest, and may typically be the working depth. "Depth of focus" is the size of the range over which the beam is focused according to a predetermined criterion. For illustration, a depth of focus of 30 μm at a depth of focus of 100 μm may be understood as a beam having an acceptable focus from 85 μm to 115 μm.

[0039] "Height" refers to a coordinate in a direction normal to a major surface of a sample. The sample may typically be mounted with a major surface in contact with the stage, with height values ​​increasing from the contact surface through the sample toward and beyond the other major surface.

[0040] "Height variation" refers to the difference between the maximum height and the minimum height of a feature, structure, or surface of a sample. The height variation can be over the entire sample, over the entire surface of the sample, over the entire structure or feature present in or on the sample, over the imaging area of ​​the sample, or over some other defined area of ​​interest. Some samples of interest herein may have a surface height variation in the range of 1 μm to 2 μm, 2 μm to 5 μm, 5 μm to 10 μm, or 10 μm to 20 μm within a surface lateral range of 2 μm, 5 μm, 10 μm, 20 μm, 50 μm, or 100 μm.

[0041] The "major surface" of a sample is a sample surface whose area does not substantially exceed any other surface of the sample. For ease of description, the sample is considered to have top and bottom major surfaces, wherein the bottom surface is supported on a stage and the top surface is exposed to one or more tools or beams. In an example, the beam may also be incident on the sample from below. Advantageously, the major surface need not follow the detailed topographical changes of the sample surface, but may be a similar surface, such as a smooth surface derived from an actual surface, a matrix surface of the sample, a back surface of the sample, or a stage surface in contact with the sample, any of which may have no topographical changes.

[0042] An "area" is a contiguous two-dimensional portion of a sample surface, or a contiguous three-dimensional portion of a sample volume.

[0043] A "region of interest" (ROI) is a portion of a sample that includes a feature or structure that is the subject of subsequent analysis. The ROI can be defined relative to the surface of the sample (e.g., the area of ​​an imaged surface that contains a particular feature, or the area in which a pattern can be milled to form a slice containing this feature) or relative to the volume (e.g., the volume that contains the structure to be further analyzed). The term ROI does not refer to any human interest.

[0044] A "surface normal" is the outward-facing normal of a sample surface. A surface normal may apply to a major surface of a sample, but this is not required, and a surface normal may also exist at a cut surface or topographical feature of a sample.

[0045] The term "lateral" may be used with respect to the sample or the beam. In the former case, the term "lateral" refers to a direction within or parallel to a major surface of the object. In the latter case, lateral refers to a direction within a plane perpendicular to the direction of propagation of the beam. In cases where it is unclear from the context, the term may be appropriately qualified to indicate whether reference is to the sample or the beam. For example, the "lateral extent of the surface" is the extent along the sample surface, while the "lateral extent of the image" is the extent in the image plane.

[0046] The terms "top", "bottom", "upward", "downward", "above", "below", "horizontal", "vertical", etc. are used for convenience relative to a common configuration in which an exposed major surface of a horizontal sample has an outward normal pointing upward, such as a sample that can approach a processing tool from above. The axis of the processing tool (e.g., the boresight of an imaging tool or the beam axis of a FIB milling machine) can be generally vertical down to the surface, or can be tilted at an angle (sometimes in the range of 40° to 60°) relative to the normal. Those skilled in the art will understand from this disclosure that the choice of the actual orientation can be varied without departing from the scope of the disclosed technology.

[0047] C. Imaging

[0048] The term "imaging" refers to a process for obtaining a two-dimensional representation (referred to as an "image") of parameter values ​​over a region of interest of a sample. In some examples, the imaging parameter may be backscatter or secondary emission from an incident particle beam (e.g., by a SEM or low current focused ion beam), but this is not required, and visible light or other parameters may also be imaged. In the disclosed examples, the image may indicate an object (e.g., a cell, protein, fluorophore, nanostructure, or fiducial) on or in the sample, or a feature of such an object (e.g., an edge).

[0049] A "composite image" is an image formed by combining information from multiple input images (eg, by focus stacking). The composite image may provide more information than any of the input images, or may provide less erroneous information than the input images.

[0050] A "depth map" is a data structure, image, or software function that indicates depth values ​​as a function of lateral position of an imaging beam. A depth map may provide a depth value for each position (e.g., each pixel) of an in-focus region of a composite image obtained by focus stacking. The depth values ​​of the depth map may be transformed, for example, using triangulation, into height values ​​for corresponding positions on an imaging surface of the sample, and the resulting height values ​​may be organized into a data structure, image, or software function to provide a "height map" of height values ​​as a function of lateral position on or within the sample.

[0051] "Focus stacking" refers to an image processing technique for combining individual images of a sample to obtain a composite image. Each individual image may have a corresponding depth of focus and a corresponding area of ​​the image that is in focus. The composite image may have an in-focus area that is larger than the in-focus area of ​​any individual image. Some focus stacking techniques may also generate a depth map. Focus stacking may be performed using a variety of scale transforms, such as the Laplacian pyramid technique. Focus stacking is a form of image fusion. The term "focus stacking" may also be used as a verb to perform this technique.

[0052] An "image pyramid" is a sequence of images obtained by multiple iterations of blurring (e.g., low-pass filtering) and downsampling. At each iteration, if a low-pass image is stored, the pyramid may be referred to as a "Gaussian pyramid." If, except for the last iteration, a high-pass complement of the low-pass image is stored, the pyramid may be referred to as a "Laplacian pyramid." A Laplacian pyramid may be preferred for storing, for example, focus stacking, for reasons including greater compressibility than a Gaussian pyramid, and the information provided directly at each level of the pyramid (e.g., each continuous length specification).

[0053] An "imager" (sometimes, an "imaging tool") is a device capable of imaging. In the disclosed examples, a SEM or FIB may be used as an imager. Some imagers may use the response to an incident light beam (e.g., beam current, secondary emission, backscatter, or fluorescence) as a measurement parameter for forming an image. Imagers that use an incident electron beam or ion beam may be referred to as an "electron microscope" (e.g., a SEM) or an "ion microscope" (e.g., a FIB), respectively. Imagers in which the measurement parameter includes the intensity, wavelength, or another property of the light may be referred to as an "optical microscope." Some optical microscopes use an incident optical beam (e.g., for fluorescence), but this is not required, and other beams or diffuse or ambient lighting may also be used. Various detectors may be used to measure particles or radiation emitted from the imaged sample, including secondary electron detectors, secondary ion detectors, photodetectors, or spectrometers.

[0054] A "multi-scale transform" of a multidimensional dataset is a decomposition of a dataset according to the scale length (range) of the features represented in the dataset. For example, fewer pixels with a coarse granularity may be sufficient to represent coarse scale features, while more pixels with a fine granularity may be required for fine scale features. Multi-scale decomposition allows for efficient processing of features of different scales. Examples of multi-scale transforms include wavelet transforms, Fourier transforms, and various pyramid decompositions.

[0055] The "viewing axis" of an image refers to the direction from which the sample is viewed in the image. For imaging using a beam incident on the sample, the viewing axis is the axis of the incident beam.

[0056] A "region" is a contiguous portion of an image.

[0057] D. Sample

[0058] A "sample" is a physical object on which imaging, milling, or other analysis or manufacturing processes are performed. Common samples may include biological samples, multilayer electrical or electronic structures, or other material structures. Biological samples may include cells, proteins, DNA, RNA, viruses, or fragments thereof. Electrical or electronic samples may include semiconductor devices, such as logic gates, memory cells, optoelectronic devices, passive components (including interconnects), transistors, and may be at various levels of manufacturing or disassembly. Material samples may include microelectromechanical systems (MEMS) or nanostructures. The disclosed techniques may be applied during sample preparation, sample characterization, or during manufacturing. For example, the disclosed techniques may be applied to a sample to form one or more additional samples, such as in the form of thin slices for TEM analysis. Some biological samples may be stable at low temperatures (variously, below 120K, below 80K, below 20K, or below 10K), such as by rapid freezing. The sample may include a "matrix" on which cells, structures, or devices are deposited or manufactured prior to thin slice preparation or other analysis.

[0059] The "cut face" is the surface of the sample exposed by etching.

[0060] A "feature" is a structure or other variation in the material composition of a sample that is identifiable in an image of the sample. Example features in a biological sample may include cells, binding sites (e.g., Covid virus spikes), mitochondria or other organelles, or fluorophores or other markers applied to biomolecules. Example features in a semiconductor device sample may include components of a memory cell, transistors, or metallization patterns. Other features may include reference structures built into the sample.

[0061] A "lamella" is or is intended for use in a thin sample for imaging in a transmission electron microscope (TEM).A lamella may have a thickness in the range of 10 nm to 20 nm, 20 nm to 30 nm, 8 nm to 50 nm, or 5 nm to 200 nm.

[0062] A "nanostructure" is a structure having a range between 1 nm and 1 μm in at least two orthogonal directions.

[0063] A "reference structure" (sometimes called a "fiducial") is a structure fabricated into a sample for use in determining the location of another structure or device in the sample. A reference structure may be visible in an image of that structure and may be used as a reference for locating subsequent operations or measuring coordinates, distances, or angles in the image.

[0064] A "semiconductor device" is a device that includes one or more semiconductor regions (e.g., doped or undoped silicon, germanium, III-V semiconductors, or similar materials) configured to perform one or more electronic functions. A semiconductor device may be a finished product, a portion thereof, or a product at an intermediate stage of manufacture. Semiconductor devices typically include other materials to provide insulation or conductive paths between semiconductor regions. Non-limiting examples of semiconductor devices include transistors, memory cells, and some optoelectronic devices. Memory interface circuits may include both conductive wiring and logic gates.

[0065] E. Tools and Techniques for Sample Analysis

[0066] "Analysis" refers to operations used to characterize a sample and may include material removal, other sample preparation, imaging, probe measurement, non-contact measurement, or secondary evaluation of data obtained by any of these techniques. Analytical operations may include imaging (e.g., FIB, optical or electron microscopy), etching (e.g., ion milling), delay, electron backscatter analysis, electron microscopy, mass spectrometry, material analysis, metrology, nanoprobing, spectroscopy, or surface preparation. The equipment or instrument used to perform this operation is called an "analytical instrument," "analyzer," or "tool." Non-limiting examples of tools that can be used in conjunction with the disclosed technology include a milling machine, an etcher, an electron microscope, an electron spectrometer, an optical microscope, or an optical spectrometer. Specifically, some tools of interest herein include a plasma focused ion beam (PFIB), a scanning electron microscope (SEM), or a fluorescence microscope.

[0067] The term "beam" is a directed flow of particles or energy. Common beams of interest in the present disclosure are particle beams, such as electron beams or ion beams (including plasma focused ion beams); or optical beams, such as laser beams for fluorescence excitation. A beam may have a limited extent transverse to its main longitudinal flow direction. The line connecting the centroids of two or more cross sections of a beam is the "axis" of the beam.

[0068] An "electron beam" is a directed flow of electrons.

[0069] An "electron microscope" is a type of analytical device in which a sample is irradiated by an electron beam and the resulting particles or electromagnetic radiation are used to form a spatially resolved image. A scanning electron microscope (SEM) images a sample surface based on reflected, secondary or backscattered particles or radiation from one or more surfaces of the sample. Because the beam interaction detected by the SEM occurs at or near the surface, the SEM can operate on samples of arbitrary thickness. In contrast, a transmission electron microscope (TEM) images a sample volume based on transmitted electrons (including scattered electrons). TEM operates on samples with a thickness of about 10nm to 150nm, which can be mounted on a grid for mechanical support and thermal conduction; in turn, the grid can be held in a sample holder. TEM can provide a resolution of less than 50pm, while SEM resolution is typically limited to about 0.5nm. In terms of magnification, TEM can provide magnifications up to and exceeding 50 million, while the magnification of SEM is typically limited to about 2 million. In the present disclosure, a scanning transmission electron microscope (STEM) that performs imaging of transmitted electrons is considered to be both SEM and TEM. The electron beam in an electron microscope can be generated in an electron gun and accelerated, focused or steered toward a sample chamber through a series of stages. The disclosed techniques can be used to form one or more thin sections for subsequent TEM analysis.

[0070] "Etching" generally refers to any subtractive process used to remove material from a sample. Etching can expose one or more cut faces of a sample by removing up to and above the cut face. Non-limiting examples of etching techniques include laser etching, plasma etching, ion beam milling, and gas assisted milling. Although etching results in material removal, the etching process may include additional preparatory or intermediate operations. For example, a photoresist may be deposited onto a sample and patterned so as to obtain a patterned etch in a subsequent operation.

[0071] A "focused ion beam" ("FIB") is an ion beam whose focus can be controlled to direct the beam to a point on a surface, or can be swept over a sample in a scanning mode. A FIB can be used to analyze, deposit, or remove material at the point of incidence. Some FIBs are used for milling. Typically, a FIB includes positive element ions, such as Xe+ or Ga+, but these are not required. Ion beam materials such as Ga+ can be derived from, for example, a liquid metal ion source (LMIS), while other ion beam materials such as Xe+ can be produced in a plasma. A FIB produced with a plasma source may be referred to as a "plasma focused ion beam" ("PFIB"), and references to a FIB herein include a PFIB. Spot sizes in the range of 2nm to 100nm can be achieved, with a depth of focus typically of 20μm to 50μm.

[0072] A "local operation" is an analytical operation performed on a limited area of ​​a sample that is proximate to or encompasses an identified feature. The area may have a predefined spatial relationship to the identified feature, and a lateral extent that is smaller than the corresponding lateral extent of the sample. The analytical operation may be a sample preparation or material removal operation, such as milling, etching, or polishing; or it may be a diagnostic operation, such as imaging or spectroscopy.

[0073] "Milling" is the process of removing material over a path guided by the movement (sometimes called a "sweep") of a milling tool relative to the sample being milled. The milling operation can expose one or more "cut faces" of a sample by removing material up to one side of the cut face. In an example, milling can be performed using a focused ion beam (FIB). Milling is a form of "etching", which more generally refers to any subtractive process used to remove material from a sample. Diffuse chemical etching may not be considered milling. However, ion beam-assisted chemical etching can be site-specific at the location of the ion beam and can be considered milling. A milling machine (or "milling tool") is a device capable of milling.

[0074] A "particle beam" is a beam comprising a directed stream of particles. A "particle" is a distinct, undivided unit of a stream of matter. Particles of common interest in the present disclosure include charged particles, such as electrons or ions (e.g., Ga, Xe, or protons), but particles are not limited thereto. Although some ions contain bound electrons, the electrons in an electron beam are considered different from the ions in an ion beam.

[0075] "Throughput" refers to the number of samples that can be successfully processed per unit of total time, including sample preparation, sample handling, equipment setup, and individual handling of samples. Eliminating steps such as the association between SEM and FIB perspectives can reduce the time used for single sample processing, and can also increase throughput by improving the accuracy of thin slice positioning, thereby advantageously improving throughput in two aspects. In addition, composite focus stacked images can facilitate rapid feature selection (interactively or through automated software), and the accompanying depth map can provide accurate placement of thin slice milling patterns, accompanied by an increase in throughput. As a general consideration, performing equipment setup and certain sample operations together for the entire microscopy grid or matrix, and thus providing more sample sites within the field of view of the analysis beam on a single grid or matrix (e.g., using a grazing FIB) can also lead to improved throughput.

[0076] Example Analyzer

[0077] Figure 1 is a diagram 100 of an example device by which the disclosed technology may be implemented. The device may be used to perform imaging and milling operations on a sample for thin section preparation or other applications. The device shown may be an analyzer.

[0078] As shown, the wall 110 defines a chamber 152 within which the sample 120 can be analyzed. The electron beam column 106 can be operated as a SEM, the ion beam column 140 can be operated as a FIB, and optionally, the optical beam 150 can excite fluorescence within the sample 120. The sample 120 can be mounted on a stage 124, which can change the position of the sample 120 with multiple degrees of freedom (translation or rotation) using corresponding actuators (not shown). The sample stage 124 can be a microcontroller.

[0079] The vertically mounted SEM 106 includes an electron source 102, a focusing lens coil 104, an objective lens coil 108, and may include additional beam control components such as a scan coil and an aperture (not shown). In the case where the scan coil is not powered, the electron beam axis 130 can be used as the polar axis of the analyzer as a whole as described. Local coordinates can also be defined for individual subsystems or for the sample 120.

[0080] The FIB column 140 may be mounted at an angle, where, for example, the ion beam axis 132 is at an angle of 52° (or within the range 40°–60°) to the electron beam axis 130. The FIB tool 140 may include an ion source 134, electrostatic or electromagnetic beam control elements 136, 138, and may include additional beam control elements not shown, such as an aperture or deflector for scanning.

[0081] The SEM 106 or FIB 140 can be controlled to set a desired working depth and further controlled to scan laterally. Typically, the working depth can be set so that the immediate ion beam or electron beam is focused at the surface of the sample 120.

[0082] The sample stage 124 can be configured to position the major surface of the sample 120 at a shallow incidence of about 154θ=10° (or in the range of 5°–30°) for oblique observation as described herein. The detector 128 can collect signals that can form an image of the sample 120. For illustration, when the electron beam 130 or the ion beam 132 scans through the sample 120, the detector 128 can receive secondary or backscattered electrons or ions. Exemplary secondary particle detectors include Everhart Thornley detectors and microchannel plates. In a variation, the secondary particle detector 128 can be annular around the beam axis 130 or 132. As another example, the detector 128 can be a camera for recording fluorescence from the sample 120. In some examples, multiple detectors 128 (e.g., a detector 128 for fluorescence and a detector 128 for receiving electrons or ions) can be provided. Other tools can also be provided within the device shown, generally represented as a probe 126. In some applications, a gas delivery tool may be included.

[0083] As shown, the controller 114 may control or monitor signals from the detector 128 or the tool 126. The same or another controller may similarly control or monitor signals from the SEM 106, the FIB 140, or the stage 124. The controller 114 may be provided with a memory 112 to store programs executed by the controller 114 and also to store data input to or output from these programs.

[0084] Pump 116 may maintain chamber 152 at approximately 10 -9 Up to 10 -4 Any exhaust gases are exhausted at a pressure of 100 torr as shown by arrow 118. A combination of turbomolecular pumps, mechanical pumps, or ion pumps may be used.

[0085] Figure 1The device may have a series of additional auxiliary components not shown, such as support structures, low-voltage or high-voltage power supplies, control electronics or instruments. The device is shown for illustrative purposes only. The disclosed technology can be easily applied to other devices with more, fewer or different components.

[0086] Example Installation

[0087] Figure 2 is a diagram 200 showing a mounting configuration for a biological cell sample. In this configuration, the cells are provided with mechanical support with minimal interference with any analytical beam that may be applied to the cells.

[0088] Grid 210 provides overall mechanical support and can be handled by manual or robotic laboratory tools. Grid 210 can be directly mounted on Figure 1 As shown in the figure, the grid 210 has an outer diameter of about 3 mm and a gold wire pattern of 250 meshes, forming a solid ring 214 with a diameter of about 100 μm. 2 The line pattern may block approximately 20% to 50% of the area within the ring 214, leaving the majority of the remaining area unblocked and available for TEM imaging.

[0089] The grid 210 can be covered with a sheet 220 to provide cell support over the openings 212. As shown, the sheet 220 is a continuous carbon film about 20 nm thick, which is strong enough to support a cell sample over the 100 μm grid openings 212, yet thin enough to allow TEM imaging through the sheet 220. Other thicknesses (e.g., 10 nm to 100 nm) and materials can also be used. Perforated membranes, sometimes referred to as "porous" membranes, can also be used.

[0090] Cells 230 can be deposited on the membrane 220, either intentionally placed in corresponding grid openings 212, or scattered on the membrane 220. The cells 230 have variable shapes and are typically about 2 μm to 5 μm in size. Even without intentional positioning, many cells can eventually be properly positioned for TEM imaging on the grid openings 212 because the beam obstruction caused by the grid lines is relatively low. The surface density of cells 230 can be selected based on cell size and application requirements. In order to increase throughput, multiple TEM samples can be prepared on a single grid opening 212.

[0091] Example beam and sample configuration

[0092] Figure 3 is a diagram 300 of an example beam and sample configuration illustrating the applicability of the disclosed techniques. Figure 3It is shown how factors such as glancing ion beam, topographical features at the sample surface, and ion beam focal depth combine to adversely affect lamella milling accuracy and thereby yield and throughput.

[0093] As shown, a cell 330 is supported on a thin film substrate 320. As indicated by arrow 351, the FIB is directed toward the cell 330. The FIB has a beam axis 354, and a transverse reference plane 356 can be used as a reference for depth values ​​(e.g., Z=0). The cell 330 forms topographic features on the substrate 320, with the result that the true depth 361 can be significantly less than the depth 371 that would be expected without any height changes.

[0094] The disclosed technique enables the depth 361 to be accurately determined by focally stacking a series of images over a range of working depths Z.

[0095] With the correct depth 361, milling can be precisely configured as shown by the dashed beam profile 363, for example with an offset equal to the beam waist radius, to precisely mill the cell 330 starting at point A 368 or at any other desired starting position.

[0096] In existing methods using, for example, dynamic focusing, the depth may be estimated based on a known angle of incidence 358 relative to the surface normal 327 of the substrate 320. A beam pattern 373 having a waist at point B 378 may be significantly erroneous at the actual depth 361.

[0097] Figure 3 This is merely illustrative. An actual slice milling pattern may not start at point A 338, but rather start away from that point. However, the principle still applies, and accurate knowledge of depth 361 through the disclosed techniques can directly improve milling accuracy, slice yield, and overall throughput.

[0098] It should be noted that although Figure 3 The illustration is not to scale and is exaggerated in some respects, but the tolerance requirements for 10nm to 200nm thick lamellae are very tight and for a FIB with 20μm depth of focus even a 1μm error in depth may result in a 10nm to 100nm error in the lateral position of the milled edge, which may be detrimental to the successful production of the lamellae.

[0099] exist Figure 3Also shown in FIG. 3 is a set of planes 381 at corresponding working depths Z. In an example of the disclosed technology, a series of images may be acquired at each of the planes 381 and used as input to a focal stacking process as described herein. For clarity, the planes 381 are shown offset from the matrix 320 and cells 330. In implementations, the images in these planes may be extended to include the matrix 320 and cells 330 in the field of view.

[0100] Example Pattern Milling

[0101] Figure 4 is a photograph 400 of an artist's model showing pattern milling of a cell according to the disclosed technique. The photograph 400 shows a cell 430 fixed on a perforated membrane carrier 420. The photograph 400 has a viewing axis tilted about 20° from the surface normal of the membrane 420.

[0102] like Figure 4 As shown, a slice 457 containing a section of a cell nucleus 432 is prepared. Dark areas 453, 455 are FIB milled patterns. The FIB beam axis is indicated by arrow 451. Pattern 453 has been milled at the position shown and also below the slice 457. Pattern 455 has been milled above the slice 457. Both patterns 453, 455 penetrate the cell 430 and the support membrane 420.

[0103] First example application

[0104] Figure 5 is a collection of images 501-505 illustrating a first example application of the disclosed technology in a cryo-lamella preparation workflow. Initially, cells may be deposited on a support structure (e.g., similar to Figure 2 and fixed at low temperatures, for example by rapid freezing.

[0105] SEM image 501 is obtained using Figure 1 The same structure shown is an electron microscope image taken from above. Many cells 512 are identifiable and the background pattern 514 reflects a pattern similar to Figure 2 The support film of 220 has a pattern of holes punched in it.

[0106] From similar to Figure 1 5. With the FIB and sample configuration having a tilted viewing axis, FIB image 502 shows a substantially similar scene to image 501. Cells 522, punch pattern 524, and impurities 526 are identifiable.

[0107] The FIB image 503 is a copy of the image 502 on which a feature 531 (e.g., the centroid of a cell) has been selected for lamella preparation and two milling patterns 533, 535 have been positioned. Because the same FIB can be used for imaging and milling, the correlation between the respective viewing angles of the different tools can be avoided and the placement of the milling patterns 533, 535 can be performed efficiently.

[0108] FIB image 504 shows the same scene as images 502-503 after partial milling of patterns 543, 545. Pattern 543 has been milled cleanly through the support film, while pattern 545 still has material beyond the milling depth. The bright outlines of patterns 543, 545 are imaging artifacts showing the locations of the edges of the milled patterns 543, 545.

[0109] Finally, after milling and polishing are complete, FIB image 505 shows the same scene as the previous images 502-504. A thin sheet 557 (about 10 nm thick) remains between the patterns 553, 555. The thin sheet 557 and the patterns 553, 555 can be similar to those in Figure 4 The thin sheet 457 and milling patterns 453, 455 are described in the context of.

[0110] Example Method

[0111] Figure 6 is a flow chart depicting example methods according to the disclosed techniques. In these methods, local operations are performed on a sample based on the output of a focus stacking technique from images of the sample applied to different focal depths.

[0112] The first method begins at process box 610, where multiple images of a sample can be acquired at corresponding focal depths. In an example, a FIB or fluorescence microscope can be used to acquire images, but this is not required and other imaging modalities can be used. Images can be acquired over a predetermined set of working depths, which can be acquired in sequence (e.g., 0μm, 20μm, 40μm, 60μm, ... relative to an arbitrary starting depth) or with irregular depth scans (e.g., 0μm, 60μm, 80μm, 20μm, 100μm, ...). In some examples, an initial image can be acquired using an autofocus mode of the imager, where successive images are acquired by subsequent adjustments to the working depth from the configuration of the autofocus. In order to mitigate beam drift or other temporal variations, multiple images can be acquired at each working depth setting and the multiple images can be combined, for example, by registration and averaging of one or more benchmarks.

[0113] At block 620, focus stacking may be performed on the images acquired at block 610 to produce a composite image 630 that may have a depth range in focus. For illustration, where a single image is acquired at a working depth ranging from z1 to z2, the composite image 630 may have features at all depths [z1, z2] in focus. The square bracket notation [z1, z2] represents a closed interval including the two endpoints z1, z2. Illustrative examples of focus stacking are further described herein.

[0114] The first method proceeds to process block 640, where an indication of a position selection may be received. In different examples, the position selection at block 640 may be performed interactively or by automated software. In the former case, a user may select a point or region of the composite image 630 on a display using a mouse or similar pointing device, thereby selecting a corresponding position on the physical sample. In the latter case, the automated software may apply machine vision techniques to identify edges, shapes, or objects on the composite image. The selected position may correspond to a recognizable feature in the composite image, which may be a representation of an object (such as a cell, fluorophore, structure, or pattern shape) or an attribute of such an object (e.g., an edge, a centroid, an extreme value of image intensity, or a vertex of a bounding polygon).

[0115] The first method follows arrow 645 from box 640 to box 670, where local operations can be performed based on the selected location. A wide range of operations can be performed at the selected location, over an area containing the selected location, at a location with a predetermined offset from the selected location, or over an area with a predetermined spatial relationship to the corresponding location. The predetermined offset can be defined with reference to beam coordinates (e.g., a lateral offset from an observation axis passing through the selected location) or with reference to sample coordinates (e.g., a lateral offset from a surface normal passing through the selected location). Non-limiting examples of such operations can include: marking the location with, for example, milling indentations, crosses, or other marks; pattern milling, such as for lamella formation; other etching operations; electron or optical spectroscopy; or other metrology operations. The local operation can be part of a workflow for manufacturing a lamella introduced into the selected location. The local operation can also include subsequent TEM analysis or other microscopy performed on a lamella prepared at the selected location. The local operation can be performed by a suitable tool, non-limiting examples of which can include a miller, an etcher, an electron microscope, an electron spectrometer, an optical microscope, or an optical spectrometer.

[0116] Also refer to Figure 6 The second method is described in Figure 6640. The second method may follow the first method from block 610 to block 640. However, for the second method, the focus stacking at block 620 may also generate a depth map 650 to accompany the composite image 630. In particular, the depth map 650 may provide a depth value for any pixel of the composite image 630. In some examples, the depth map 650 may be represented as an image such that each first pixel in the composite image 630 has a corresponding second pixel in the depth map 650, whose value (pixel intensity) represents the depth to the sample surface at the location of the first pixel. The second pixel of the depth map image 650 may have a 1:1 relationship with the first pixel of the composite image 630, but this is not required, and the depth map 650 may be downsampled to a smaller representation with fewer pixels than the composite image 630. In addition, other representations of the depth map 630 may be employed, such as a contour map or a lookup table. Because the granularity of the depth values ​​may be coarser than the granularity of the pixels in the composite image 630, run-length coding or another compression scheme may advantageously reduce the size of the depth map 650. In some examples, depth map 650 may store depth values ​​relative to a viewing plane that is orthogonal to the viewing axis of the imaging beam (e.g., for Figure 3 In some examples, the depth values ​​in the graph 650 may be relative to a reference plane that is generally parallel to the "top" major surface of the sample. In the latter case, a completely smooth surface will have uniform depth values ​​across the field of view, and variations in depth reflect variations in the topography of the sample surface.

[0117] Thus, the second method may use the selected location from block 640 and ascertain the corresponding depth value from the depth map 650. At block 660, the depth value may be used to set the working depth of the tool that will perform the local operation 670. The second method then proceeds to block 670 where, similar to the first method, the local operation may be performed.

[0118] In some examples, the working depth may be equal to the depth value at the selected location, but this is not required and one or more offsets may be applied. To illustrate, a FIB with a 40 μm focal depth set to a working depth of Z μm may provide milling with an acceptable tolerance from Z-20 μm to Z+20 μm. Thus, if the depth value to the sample surface is 80 μm, milling may be started with the working depth set to 100 μm, requiring fewer FIB adjustments as milling proceeds. As another illustration, a milling operation may be performed at a lateral displacement of 10 μm from the selected location, such as, for example Figure 5. Thus, the depth value may be read from the depth map 650 at an offset from the selected location rather than at the selected location itself. In addition, the four corners of the pattern mill may have depths of 60 μm, 80 μm, 90 μm, 70 μm, and these gap depths may be averaged together to set the working depth to an average of (60+80+90+70) / 4=75 μm. Multiple such offsets may be applied together. Offsets, use of tilted reference surfaces, or other scaling may be achieved by applying a linear transformation to the depth values ​​stored in the depth map 650.

[0119] Many variations and extensions may be implemented within the scope of the disclosed technology, some of which are described above in the context of the first and second methods, some of which are described immediately below, and others are described elsewhere in this document.

[0120] In an example, automated selection of locations in a composite image may be performed by software trained by machine learning, such as using a training dataset of images and selected feature locations.

[0121] In an example, local operations may be performed using one or more beams, such as an ion beam, a plasma focused ion beam, an electron beam, or an optical beam. Such beams may be directed to the sample along a beam axis with an angle of incidence in any range: 70°-85°, 60°-87°, 0°-15°, 15°-40°, or 40°-60°. In different examples, local operations may be performed at a selected position, at a predetermined lateral offset from the selected position, at a predetermined depth offset from the selected position, over an area including the selected position, or over an area having a lateral offset or depth offset from the selected position. The lateral offset may be in beam coordinates or sample coordinates. When performing local operations at block 670, the working depth set at block 660 may be dynamically adjusted, with or without additional sample imaging.

[0122] The image may be acquired by or from an imager, which may include an ion microscope, an electron microscope, an optical microscope, a secondary electron detector, a secondary ion detector, or a photodetector. The imager may illuminate the sample using one or more beams (e.g., an ion beam, a plasma focused ion beam, an electron beam, or an optical beam). Such a beam may be directed toward the sample along a beam axis with an angle of incidence in any range: 70°-85°, 60°-87°, 0°-15°, 15°-40°, or 40°-60°.

[0123] Samples of interest may include cryogenic samples, rapidly frozen samples, biological samples, semiconductor samples, or nanostructures.

[0124] Example focus stacking

[0125] Figure 7 is a flow chart of a focus stacking method that can be used with the disclosed technology. The method begins with a collection of source images focused at different depths. For ease of description, the term "slice" may be used to refer to each imaged working depth, the corresponding source image, and the associated processing. The set of source images is processed to obtain (i) a composite image in which features at different depths are all in focus, and (ii) a depth map indicating depth values ​​of features in the composite image. Figure 7 One exemplary method of focal stacking using a Laplacian pyramid technique is shown, however use of this technique is not required and other focal stacking methods may be used as part of the disclosed techniques.

[0126] The illustrated technique can be conceptually considered to include input-side processing, intermediate processing, and output-side processing, all of which are Figure 7 Initially, a plurality of source images 701 may be acquired at respective working (focal) depths.

[0127] Typically, the group of images 701 includes information in the form of features. The information may be distributed in three spatial dimensions (two dimensions in the plane of each image 701, and the third dimension is the depth across slices 770A-770N), and may also have a variable specification length, which may not be known a priori. When analyzing images with high resolution, coarse-grained features may appear as smoothly varying pixel intensities, and coarse features may be missed. On the contrary, when analyzing images with coarse resolution, fine-grained features may be lost due to smoothing. In order to overcome such problems, decomposition can separate the information in the original image 701 according to the length specification, and each length specification can be processed with appropriate resolution. Intermediate results of various length specifications can be combined to obtain a composite image, and corresponding depth maps can be optionally obtained.

[0128] A. Input side processing

[0129] The input side processing performs a decomposition, shown for the first slice in dashed outline 770A, with other slices indicated after slice 770A. That is, each slice 770A-770N has a corresponding source image 701, which can be decomposed into a set of images 711-716.

[0130] As shown, a low pass filter (LPF) 801 is applied to image 701, and the output is subtracted from image 701 by subtractor block 811, leaving 711 containing information in the highest spatial frequency band (referred to as an "octave") of source image 701. Because low pass filtering removes high frequency content from source image 701, the output of LPF 801 may be decimated or downsampled by block 821 to generate a smaller image 702. Illustratively, a 1024×1024 source image 701 may be downsampled by 1 / 2 to obtain a 512×512 image 702.

[0131] This process may be applied iteratively to obtain successively lower frequency bands of information in high pass images 711-715, and successively lower frequency versions of the source image 701 in down sampled images 702-706. The repeated details of several iterations are omitted, except that it should be noted that the LPF blocks 802-805, the subtraction blocks 812-815 and the down sampler blocks 822-825 operate in a similar manner to the above blocks 801, 811, 821, although the images are smaller. The final down sampled image 706 may remain intact as an image 716 including all the residual lower frequency information. In the illustrated example, the decomposition may produce images of sizes 1024×1024 (711), 512×512, 256×256, 128×128, 64×64, and 32×32 (716).

[0132] By appropriate choice of the cutoff spatial frequency of the LPF 801, any residual high-pass information in its output may be insignificant, and the downsampling at block 821 may be substantially lossless, meaning that the decomposition of the source image 701 into the image pair 711, 702 may also be lossless, and similar for subsequent stages of decomposition. That is, the images 711-716 may be combined (with appropriate upsampling) to restore the source image 701 with a high degree of accuracy.

[0133] This decomposition with 5 levels / 2 downsampling is merely illustrative. Fewer or more levels may be used. Downsampling by smaller or larger factors may be used. Different levels may use different downsampling factors.

[0134] The described decomposition may be replicated across all slices 770A-770N to complete input-side processing.

[0135] B. Intermediate Processing

[0136] The description turns to the intermediate processing that performs fusion across slices. Input side processing is organized by slice (depth), while intermediate processing can be organized by specification length or spatial frequency band. That is, all images of slice 711 are processed together, and similarly for image 712, image 713, etc.

[0137] Dashed outline 793 shows the intermediate processing of image 713. Within each image 713, the variance can be calculated at each pixel to obtain a variance matrix 723. As shown, there is a variance matrix 723 for each slice 770A-770N. The variance can be calculated over a 5×5 pixel area centered on the instant pixel of image 713, for example. For illustration, the variance of the pixel at (row, column)=(10,20) can be calculated over the pixel area in rows 8 to 12 and columns 18 to 22. Near the edge of image 713, the variance can be calculated over those pixel positions within the image. For illustration, the variance of pixel (2,1) can be calculated over the pixel area in rows 1 to 4 and columns 1 to 3.

[0138] Then, at each pixel location, the variances across slices can be compared and weights can be assigned accordingly. The slice 723 with the highest variance can have the most information and can be assigned the highest weight, while the slice 723 with a gradually decreasing variance can be assigned a gradually decreasing weight. In some examples, the weight can be equal to the variance, while in other examples, other monotonic functions of the variance can be used, such as power laws, exponentials, piecewise linear or other functions. The weights can be stored in a weight matrix 733 having the same organization as the image 713 and the variance matrix 723. As shown, there is a weight matrix 733 for each slice 770A-770N. Optionally, the weights can be normalized so that for any given pixel, the sum of the weights 733 across all slices is 1. Assuming that B and W represent the decomposed image 713 and the weight matrix 733, respectively, the weight W of the NxM pixel area with respect to the pixel (r, c) can be calculated using the following formula, where M / 2 and N / 2 are understood as the integer part, respectively.

[0139]

[0140] Next, the variance matrix 723 and the weight matrix 733 may be combined across slices. The partial image 743 may be computed as a weighted sum over the decomposed image slices 713. Let E denote the partial image 743, where s, r, c denote slices, pixel rows, and pixel columns, respectively, and assuming the weights to be normalized, the equation is

[0141]

[0142] It can be used to calculate the pixel values ​​of the partial image E 743.

[0143] The depth Z of the corresponding slice can be used s , the partial depth map F 763 is calculated using an equation such as

[0144]

[0145] In a variation, the index s can be used instead of Z s , a linear transformation from slice index to physical depth is later performed. As indicated by processing modules 791, 796 shown in dashed outline, partial images E (e.g., 741, 746) and partial depth maps F (e.g., 761, 766) may be similarly calculated for other specifications from corresponding decomposed images (e.g., 711, 716).

[0146] In order to combine several partial images 741-746 into a single composite image, an information matrix 753 may be derived and used to assign weights to pixels of the partial image 743. In some examples, the elements of the information matrix 753 may be the maximum values ​​of the corresponding elements of the variance matrix 743 over all slices. If the variance matrix 743 is set to V and the information matrix 753 is set to G, the following equation can be used.

[0147] G(r,c)=max s (V s (r,c))

[0148] In other examples, other formulas may be used. As an illustration, the variance value V s (r,c) can be fitted to Z s A Gaussian function (or another bell-shaped curve) of is fitted, and the peak value of the fitted curve can be used as the information value G(r,c).

[0149] The information matrix for other specifications (eg, 751, 756) may be calculated in a similar manner.

[0150] With the partial images E 741-746, the partial depth maps F 761-766 and the weights for combining them in the form of the partial information matrix G 751-756, the intermediate processing stage is completed.

[0151] C. Output side processing

[0152] The description continues with output-side processing, where the results of the intermediate processing may be fused across length scales to obtain a composite image and optionally its corresponding depth map.

[0153] As indicated by multiplier blocks 841-846, the intermediate results may be partially combined at each scale and then added with appropriate upsampling at block 848 to obtain the composite image 730. Letting the subscript L denote a number of length scales of the intermediate processing modules 791-796 (e.g., the length scales of the decomposed images 711-716), (r', c') denote upsampled pixel coordinates matching the source image 701, U denote the upsampling function from (r, c) to (r', c'), and H denote the composite image 730, the entire calculation may be represented by an equation such as

[0154]

[0155] In this equation, the inner multiplication may be performed by multipliers 841 - 846 , while the upsampling and summing may be performed by block 848 .

[0156] A similar process may produce depth map 750. Denoting depth map 750 as J, the overall calculation may be represented by an equation such as

[0157]

[0158] Figure 7 All or part of the techniques of can be used to implement focus stacking in various methods and applications described herein. However, the disclosed technology is not limited thereto and can also be used Figure 7 variations or extensions of .

[0159] D. Explanation of focus stacking results

[0160] Figure 8 is a diagram illustrating the generation of a focused image and a corresponding depth map. In the figure, a set of three images is combined to obtain a composite image having a focused representation of an object from each of the three images and a depth map indicating the depth of the object in each image. The annotations are provided for clarity and are not part of the images shown.

[0161] On the left, image set 800 includes images 881-883 acquired using the working depths Z1, Z2, and Z3 shown, respectively. The imaged scene includes three geometric shapes: a cross, a square, and a rectangle. For clarity, each shape is annotated with an "F", "F" indicating that it is in focus, or annotated with an "X", "X" indicating that the shape is out of focus. Therefore, the rectangle, square, and cross are in focus at working depths Z1, Z2, and Z3, respectively. As described herein, focus stacking can be applied to image set 800 to obtain a composite image 830 and a depth map 850. In composite image 830, all three shapes appear in focus, as indicated by the "F" annotation. Depth map 850 stores a depth Z1 corresponding to image 881, where the rectangular shape is in focus, as indicated by the annotation "Z1". Similarly, the depth values ​​"Z2", "Z3" stored for the square and cross indicate the depths at which these shapes are in focus in image set 800.

[0162] Second example application

[0163] Fig. 9is a collection of images 920, 930, 940 illustrating a second example application of focus stacking. In this example, focus stacking is applied to a collection of source images to produce a composite image with which a pattern for milling a sheet is placed.

[0164] Initially, image 920 is one of a set of source images acquired by the imager at a particular working depth. As shown, the foreground is in focus, while the background is not. Rectangle 921 represents the other source images of the set, each source image being in focus at a corresponding working depth.

[0165] Through the application of focus stacking as described herein, a composite image 930 may be obtained in which all features from the foreground to the background are seen to be in focus.

[0166] Image 940 is a copy of composite image 930 on which four FIB milling patterns 941-944 have been marked. Execution of these milling patterns may form a thin slice between patterns 942, 944, as in Figure 5 The image 505 and Figure 4 described in the context of.

[0167] Third example application

[0168] FIG. 10A to FIG. 10B A collection of images 1011-1017, 1030, and 1050 showing a third example application of focus stacking is provided together. Images 1011-1017 are a collection of FIB images for corresponding working depths, and in which corresponding zones are in focus. As a visual aid, exemplary zones 1021-1027 have been marked in images 1011-1017, respectively, to show the features of focus in several images. As the working depth increases from images 1011 to 1017, it can be seen that the focus zones 1021-1027 gradually recede from the foreground to the background. Image 1011 has the lowest working depth value in images 1011-1017, and in zones 1021-1027, the focus zone 1021 is the most in the foreground. In addition, it can be seen that zone 1021 deteriorates in the focus from image 1012 to image 1017. Image 1017 has the maximum working depth, and background zone 1027 is in focus. Region 1027 is not in focus in image 1011.

[0169] Applying focus stacking to images 1011-1017 may generate a composite image 1030 and a depth map 1050. Composite image 1030 (including FIG. 10A to FIG. 10B1030 shows all areas in focus from foreground to background. Depth map 1050 shows the topographical fluctuations of features visible in composite image 1030 relative to a reference plane. Generally speaking, cells visible in image 1030 that are above the perforated membrane also have lower depth values ​​than their areas in the absence of cells (e.g., imaging the perforated membrane alone). This reduction in depth appears as lighter areas in depth map 1050. For visual effect, depth map 1050 uses a reference plane that is slightly tilted relative to the major surface of the bare support membrane, which is responsible for the systematic shading changes from darker foreground to lighter background.

[0170] Fourth example application

[0171] FIG. 11A to FIG. 11C Together, a collection of images 1101-1120 and 1130 are provided which illustrate focal stacking applied to fluorescence imaging, as may be seen in Figure 1 150 or in a configuration discussed elsewhere herein. Images 1101-1120 are a collection of fluorescence images for corresponding working depths and in which corresponding zones are in focus. As a visual aid, exemplary zones 1142, 1145, 1149, 1151, and 1153 are shown as being in focus on corresponding images in the collection of images 1101-1120. In other images several slices away from the marked images, these features can be seen to be out of focus. For illustration, zone 1142 is out of focus in image 1105 and zone 1153 is out of focus in image 1115.

[0172] Applying focus stacking to images 1101 - 1120 may produce a composite image 1130 in which all regions may be seen to be in focus.

[0173] Additional Embodiments

[0174] The following numbered paragraphs describe additional examples of the disclosed technology.

[0175] Embodiment 1 is a device comprising: a tool configured to perform a local operation on a sample using a first light beam; an imager configured to acquire multiple images of the sample using a second light beam; and a controller configured to: focally stack the multiple images to obtain a composite image; receive an indication of a selected position in the composite image; and cause the tool to perform the local operation based on the selected position.

[0176] Embodiment 2 includes the subject matter of embodiment 1, and further specifies that the controller is further configured to: generate a depth map from the plurality of images; use the depth map to determine a working depth from a selected location; and set a tool for local operation based on the working depth.

[0177] Embodiment 3 includes the subject matter of any of Embodiments 1-2, wherein the controller is further configured to form a sheet introduced into the selected location based at least in part on the local operation.

[0178] Embodiment 4 includes the subject matter of any of Embodiments 1-3, wherein the tool comprises one or more of the following: a milling machine, an etcher, an electron microscope, an electron spectrometer, an ion microscope, an optical microscope, or an optical spectrometer.

[0179] Embodiment 5 includes the subject matter of any of Embodiments 1-4, wherein the local operation comprises one or more of: milling, etching, microscopy, or spectroscopy.

[0180] Embodiment 6 includes the subject matter of any of Embodiments 1-5, wherein the first light beam comprises one or more of: an ion beam, a plasma focused ion beam, an electron beam, or an optical beam.

[0181] Embodiment 7 includes the subject matter of any of Embodiments 1-6, wherein the first light beam is directed toward the sample along a beam axis oriented between 60°-87° from a surface normal of the sample.

[0182] Embodiment 8 includes the subject matter of any of Embodiments 1-7, wherein the imager comprises one or more of: an ion microscope, an electron microscope, an optical microscope, a secondary electron detector, a secondary ion detector, or a photodetector.

[0183] Embodiment 9 includes the subject matter of any of Embodiments 1-8, wherein the second light beam is directed toward the sample along a viewing axis oriented between 60°-87° from a surface normal of the sample.

[0184] Embodiment 10 includes the subject matter of any of Embodiments 1-9, wherein the second beam comprises one or more of: an ion beam, a plasma focused ion beam, an electron beam, or an optical beam.

[0185] Embodiment 11 includes the subject matter of any of Embodiments 1-10, wherein the first light beam and the second light beam are generated: in a common beam column; or

[0186] In different beam columns.

[0187] Embodiment 12 includes the subject matter of any of Embodiments 1-11, wherein the focal stacking is performed using one or more of: a multi-scale transform, an image pyramid, a Gaussian image pyramid, a Laplacian image pyramid, or a wavelet transform.

[0188] Embodiment 13 includes the subject matter of any of Embodiments 1-12, wherein the location is selected by a user or automated software.

[0189] Embodiment 14 includes the subject matter of any of Embodiments 1-13, wherein the location corresponds to a feature in the sample.

[0190] Example 15 includes the subject matter of any of Examples 1-14, wherein the local operation is performed at a selected location, at a predetermined lateral offset from the selected location, at a predetermined depth offset from the selected location, over an area including the selected location, or over an area having a lateral offset or depth offset from the selected location.

[0191] Embodiment 16 includes the subject matter of any of Embodiments 2-15, and further specifies that the working depth is equal to or is a predetermined linear transformation of: a first value stored in the depth map for the selected location; a first average of values ​​stored in the depth map in a first zone including the selected location; a second value stored in the depth map at a predetermined lateral offset from the selected location; or a second average of values ​​stored in the depth map in a second zone having a lateral offset from the selected location.

[0192] Embodiment 17 is a computer-implemented method comprising: acquiring multiple images of a sample at corresponding depths of focus; and focally stacking the multiple images to obtain a composite image; receiving an indication of a selected position in the composite image; and performing a local operation on the sample based on the selected position.

[0193] Embodiment 18 includes the subject matter of embodiment 17, and further specifies that the focal stacking further produces a depth map of the sample, and the method further includes: using the depth map to determine a working depth from the selected location; setting a tool based on the working depth; and causing the tool to perform the local operation.

[0194] Embodiment 19 includes the subject matter of any of Embodiments 17-18, and further comprises forming a sheet introduced into the selected location, wherein the forming comprises causing the local operation to be performed.

[0195] Embodiment 20 includes the subject matter of any of Embodiments 17-19, and further specifies that the images are acquired using one or more of: an ion microscope, an electron microscope, an optical microscope, a secondary electron detector, a secondary ion detector, or a photodetector.

[0196] Embodiment 21 includes the subject matter of any of Embodiments 17-20, and further specifies that imaging is performed by directing a first light beam toward the sample, the first light beam comprising: an ion beam, a plasma focused ion beam, an electron beam, or an optical beam.

[0197] Embodiment 22 includes the subject matter of any of Embodiment 21, and further specifies that the first light beam is directed toward the sample along a beam axis oriented between 60°-87° from a surface normal of the sample.

[0198] Embodiment 23 includes the subject matter of any of Embodiments 17-22, and further specifies that the sample comprises: a cryogenic sample, a cooled frozen sample, a biological sample, a semiconductor sample, or a nanostructure.

[0199] Embodiment 24 includes the subject matter of any of Embodiments 17-23, and further specifies that the sample has a surface height variation of 1 μm to 2 μm, 2 μm to 5 μm, 5 μm to 10 μm, or 10 μm to 20 μm over a surface lateral range of 2 μm, 5 μm, 10 μm, 20 μm, 50 μm, or 100 μm.

[0200] Embodiment 25 includes the subject matter of any of Embodiments 17-24, and further specifies that focal stacking is performed using one or more of: a multi-scale transform, an image pyramid, a Gaussian image pyramid, a Laplacian image pyramid, or a wavelet transform.

[0201] Embodiment 26 includes the subject matter of any of Embodiments 17-25, and further specifies that the location is selected by a user or an automated program.

[0202] Embodiment 27 includes the subject matter of any of Embodiments 17-26, and further specifies that the location corresponds to a feature in the sample.

[0203] Embodiment 28 includes the subject matter of any of Embodiments 17-27, and further specifies that the local manipulation is performed by one or more of: milling, etching, microscopy, or spectroscopy.

[0204] Embodiment 29 includes the subject matter of any of Embodiments 17-28, and further specifies that the local operation is performed by directing a second light beam toward the sample, the second light beam comprising: an ion beam, a plasma focused ion beam, an electron beam, or an optical beam.

[0205] Embodiment 30 includes the subject matter of any of Embodiment 29, and further specifies that the second light beam is directed toward the sample along a second axis oriented between 60°-87° from a surface normal of the sample.

[0206] Embodiment 31 includes the subject matter of any of Embodiments 17-30, and further specifies: performing the local operation at a selected location, at a predetermined lateral offset from the selected location, at a predetermined depth offset from the selected location, over an area including the selected location, or over an area having a lateral offset or depth offset from the selected location.

[0207] Embodiment 32 includes the subject matter of any of Embodiments 18-31, and further specifies that the working depth is equal to or is a predetermined linear transformation of: a first value stored in the depth map for the selected location; a first average of values ​​stored in the depth map in a first zone including the selected location; a second value stored in the depth map at a predetermined lateral offset from the selected location; or a second average of values ​​stored in the depth map in a second zone having a lateral offset from the selected location.

[0208] Embodiment 33 includes the subject matter of any of Embodiments 18-32, and further specifies that the tool comprises one or more of the following: a milling machine, an etcher, an electron microscope, an electron spectrometer, an ion microscope, an optical microscope, or an optical spectrometer.

[0209] Embodiment 34 is one or more computer-readable media storing instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to perform the method of any one of Embodiments 17-33.

[0210] General computer environment

[0211] Fig.12 A general example of a suitable computing system 1200 is shown in which the described examples, techniques, and methods can be implemented to apply focal stacking to a sample preparation or analysis process. The computing system 1200 is not intended to impose any limitations on the scope of use or functionality of the present disclosure, as these innovations can be implemented in a variety of general-purpose or special-purpose computing systems. The computing system 1200 can control a FIB imaging tool, a FIB milling tool, a SEM, a stage, an analytical instrument, or other similar equipment; can perform focal stacking, metrology, or other analysis on an image representing a sample or other acquired data; can control a stage, an ion beam column, or an electron beam column; or can acquire, process, output, or store measurement data.

[0212] refer to Fig.12, the computing environment 1210 includes one or more processing units 1222 and a memory 1224. Fig.12 , this basic configuration 1220 is included within the dashed line. The processing unit 1222 can execute computer executable instructions, such as for control, metering or other functions as described herein. The processing unit 1222 can be a general-purpose central processing unit (CPU), a processor in an application-specific integrated circuit (ASIC), or any other type of processor. In a multi-processing system, multiple processing units execute computer executable instructions to increase processing power. The computing environment 1210 may also include a graphics processing unit or a synergistic processing unit 1230. The tangible memory 1224 can be a volatile memory (e.g., a register, a cache, or a RAM), a non-volatile memory (e.g., a ROM, an EEPROM, or a flash memory) that can be accessed by the processing units 1222, 1230, or a combination thereof. The memory 1224 stores software 1280 that implements one or more innovations described herein in the form of computer executable instructions suitable for execution by the processing units 1222, 1230. For example, the software 1280 may include software 1281 for controlling a FIB, SEM, or other imaging tool, software 1282 for controlling a FIB or other milling tool, software 1283 for controlling a stage on which a sample is supported, software 1284 for focal stacking, software 1286 for performing metrology or other analysis on sample data, or other software 1285 (including user interfaces, host interfaces, or fault detection). The illustrations shown for the software 1280 in the storage device 1240 may similarly apply to Fig.12 Memory 1224 may also store control parameters, calibration data, measurement data, other database data, configuration data, or operational data.

[0213] The computing system 1210 may have additional features, such as one or more of a storage device 1240, an input device 1250, an output device 1260, or a communication port 1270. An interconnection mechanism (not shown), such as a bus, a controller, or a network, interconnects the components of the computing environment 1210. Typically, operating system software (not shown) provides an operating environment for other software 1280 executed in the computing environment 1210 and coordinates the activities of the components of the computing environment 1210.

[0214] The tangible storage device 1240 may be removable or non-removable and include disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other medium that can be used to store information in a non-transitory manner and that can be accessed within the computing environment 1210. The storage device 1240 stores instructions (including instructions and / or data) for the software 1280 that implements one or more innovations described herein. The storage device 1240 may also store image data, measurement data, workflow programs, reference data, calibration data, configuration data, sample data, or other databases or data structures described herein.

[0215] The input device 1250 may be a mechanical, touch-sensing, or proximity-sensing input device (such as a keyboard, mouse, pen, touch screen, or trackball), a voice input device, a scanning device, or another device that provides input to the computing environment 1210. The output device 1260 may be a display, a printer, a speaker, an optical disc writer, or another device that provides output from the computing environment 1210. Input or output may also be transmitted to / from a remote device over a network connection via the communication port 1270 (e.g., a computer such as a computer). Figure 1 described in the context of ).

[0216] Communication port 1270 enables communication with another computing entity via a communication medium. The communication medium transmits information, such as computer executable instructions, audio or video input or output, or other data in a modulated data signal. A modulated data signal is a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example and not limitation, the communication medium may use electrical, optical, RF, acoustic or other carriers.

[0217] The data acquisition system may be integrated into the computing environment 1210 as an input device 1250 or coupled to the communication port 1270 and may include an analog-to-digital converter or a connection to an instrument bus. The instrument control system may be integrated into the computing environment 1210 as an output device 1260 or coupled to the communication port 1270 and may include a digital-to-analog converter, a switch, or a connection to an instrument bus.

[0218] In some examples, computer system 1200 may also include a computing cloud 1290 in which instructions implementing all or part of the disclosed techniques are executed. Any combination of memory 1224, storage 1240, and computing cloud 1290 may be used to store software instructions and data for the disclosed techniques.

[0219] The innovation is described in the general context of computer executable instructions (e.g., those contained in program modules) that can be executed in a computing system on a target real or virtual processor. Typically, a program module or component comprises a routine, program, library, object, class, component, data structure, etc., which performs a specific task or implements a specific data type. In various embodiments, the functionality of program modules can be combined or split as needed. Computer executable instructions for program modules can be executed within a local or distributed computing system.

[0220] The terms "computing system," "computing environment," and "computing device" are used interchangeably herein. Unless the context clearly indicates otherwise, none of the terms implies any limitation on the type of computing system, computing environment, or computing device. In general, a computing system, computing environment, or computing device may be local or distributed, and may include any combination of special-purpose hardware and / or general-purpose hardware and / or virtualized hardware, as well as software that implements the functionality described herein. Virtual processors, virtual hardware, and virtualized devices are ultimately embodied in a hardware processor or another form of physical computer hardware, and therefore include both the software and the underlying hardware associated with virtualization.

[0221] General considerations

[0222] As used in this application and in the claims, the singular forms "a", "an", and "the" include plural forms unless the context clearly indicates otherwise. In addition, the term "including" means "comprising". Furthermore, the term "coupled" does not exclude the presence of intervening elements between the coupled items. Moreover, as used herein, the terms "or" and "and / or" mean any one or more combinations of the phrases.

[0223] The systems, methods, and devices described herein should not be construed as limiting in any way. On the contrary, the present disclosure relates to all novel and non-obvious features and aspects of the various disclosed embodiments, whether 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. The technology from any example may be combined with one or more of the described technologies in any other example.

[0224] Although the operations of some of the disclosed methods are described in a specific sequential order for ease of presentation, it should be understood that unless the specific language stated herein requires a specific ordering, this description covers the rearrangement of the operations. 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 description sometimes uses terms such as "acquire", "apply", "average", "configure", "control", "correlate", "extract", "decompose", "determine", "guide", "downsample", "filter", "fusion", "generate", "image", "produce", "retrieve", "zoom", "select", "set", "store", "transform", or "upsample" 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 identified by those skilled in the art.

[0225] In some examples, values, procedures, or devices are referred to as "lowest," "best," "maximum," "optimal," "extreme," etc. It will be appreciated that such descriptions are intended to indicate that a selection may be made among several alternatives or many alternatives, and that such selections are not necessarily better, lesser, or otherwise preferred than other alternatives not considered.

[0226] The theories of operation, scientific principles, or other theoretical descriptions presented herein with reference to the devices or methods of the present disclosure have been provided for the purpose of better understanding for the purpose of explanation and are not intended to be limited in scope. That is, the disclosed systems, methods, and devices are not limited to such theories of operation. The appended claims are not limited to embodiments that function in the manner described by such theories of operation.

[0227] Any of the disclosed methods may be controlled or implemented as computer executable instructions or computer program products stored on one or more computer readable storage media (e.g., tangible, non-transitory computer readable storage media) and executed on a computing device (e.g., any available computing device, including a tablet, smart phone, or other mobile device including computing hardware). A tangible computer readable storage medium is any available tangible medium accessible within a computing environment (e.g., one or more optical media discs such as DVDs or CDs, volatile memory components such as DRAM or SRAM, or non-volatile memory components such as flash memory or a hard drive). By way of example, and with reference to Fig.12, the computer-readable storage medium includes memory 1224 and storage device 1240. The term "computer-readable medium" or "computer-readable storage medium" does not include signals and carrier waves. In addition, the term "computer-readable medium" or "computer-readable storage medium" does not include a communication port (e.g., 1270).

[0228] Any of the computer executable instructions for implementing the disclosed techniques and any data created and used during the implementation of the disclosed embodiments may be stored on one or more computer readable storage media. The computer executable instructions may be, for example, a dedicated software application or part of a software application accessed or downloaded via a web browser or other software application (e.g., a remote computing application). Such software may be executed using one or more network computers, for example, on a single local computer (e.g., any suitable commercially available computer) or in a network environment (e.g., via the Internet, a wide area network, a local area network, a client server network, a cloud computing network, or other such network).

[0229] For the sake of clarity, only some selected aspects of the software-based implementation are described.Other details well-known in the art are omitted.For example, it should be understood that the disclosed technology is not limited to any specific computer language or program.For example, the disclosed technology can be implemented by using Adobe Flash, C, C++, C#, Curl, Dart, Fortran, Java, JavaScript, Julia, Lisp, Matlab, Octave, Perl, Python, Qt, R, Ruby, SAS, SPSS, SQL, WebAssembly, any derivative thereof, or any other suitable programming language, or in some examples, markup languages ​​such as HTML or XML, or any combination of suitable languages, libraries, and data packets to write software.Similarly, the disclosed technology is not limited to the type of any particular computer or hardware.Some details of suitable computers and hardware are well-known and do not need to be elaborated in the present disclosure.

[0230] In addition, any of the software-based embodiments (including, for example, computer-executable instructions for causing a computer to perform any of the disclosed methods) may be uploaded, downloaded, or remotely accessed via appropriate communication means. Such appropriate communication means include, for example, the Internet, the World Wide Web, an intranet, a software application, cables (including fiber optic cables), magnetic communications, electromagnetic communications (including RF, microwave, infrared, and optical communications), electronic communications, or other such communications means.

[0231] In view of the many possible embodiments to which the principles of the disclosed subject matter may be applied, it should be recognized that the illustrated embodiments are only preferred examples and should not be taken as limiting the scope of the claims. Rather, the scope of the claimed subject matter is defined by the appended claims. We therefore claim all that falls within the spirit and scope of these claims.

Claims

1. A device, comprising: a tool configured to perform a localized operation on the sample using the first beam; an imager configured to acquire a plurality of images of the sample using a second light beam; and A controller, the controller being configured to: focally stacking the plurality of images to obtain a composite image; receiving an indication of a selected location in the composite image; and The tool is caused to perform the local operation based on the selected location.

2. The device according to claim 1, wherein the controller is further configured to: generating a depth map from the plurality of images; using the depth map to determine a working depth from a selected location; and The tool is set according to the working depth for the local operation.

3. The apparatus of claim 1, wherein the first beam comprises a plasma focused ion beam.

4. The apparatus of claim 1, wherein the first light beam is directed toward the sample along a beam axis oriented between 60°-87° from a surface normal of the sample.

5. The apparatus of claim 1, wherein the imager comprises an ion microscope.

6. The apparatus of claim 1, wherein the second light beam is directed toward the sample along a viewing axis oriented between 60°-87° from a surface normal of the sample.

7. The apparatus of claim 1, wherein the second beam comprises a plasma focused ion beam.

8. The apparatus of claim 1, wherein the second light beam comprises an optical light beam.

9. The apparatus of claim 1, wherein the first light beam and the second light beam are generated in a common light beam column.

10. The apparatus of claim 1, wherein the controller is configured to perform the focus stacking using a plurality of format transforms.

11. A computer-implemented method, the method comprising: acquiring a plurality of images of the sample at corresponding focal depths; as well as [TP385397CNSEC1] focally stacking the plurality of images to obtain a composite image; receiving an indication of a selected location in the composite image; as well as A local operation is performed on the sample based on the selected location.

12. The computer-implemented method of claim 11, wherein the focus stacking also produces a depth map of the sample, and the method further comprises: Use the depth map to determine the working depth from the selected location; Setting the tool according to the working depth; as well as The tool is caused to perform the local operation.

13. The computer-implemented method of claim 12, wherein the working depth is based on a value stored in the depth map at a predetermined lateral offset from the selected location.

14. The computer-implemented method of claim 11, further comprising forming a sheet introduced into the selected location, wherein the forming comprises causing the local operation to be performed.

15. The computer-implemented method of claim 11, wherein the sample comprises a cryogenic biological sample.

16. The computer-implemented method of claim 11, wherein the sample has a surface height variation in the range of 2 μm to 10 μm over a surface lateral extent of 5 μm to 20 μm.

17. The computer-implemented method of claim 11, wherein the location is selected by an automated program.

18. The computer implemented method of claim 11, wherein the local operation is performed by milling.

19. The computer-implemented method of claim 11, wherein the local operation is performed at a predetermined lateral and / or depth offset from the selected location.

20. One or more computer-readable media storing instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to perform operations comprising: acquiring a plurality of images of the sample at corresponding focal depths; as well as focally stacking the plurality of images to obtain a composite image; receiving an indication of a selected location in the composite image; as well as [TP385397CNSEC1] A local operation is performed on the sample based on the selected location.

Images