Methods and systems of electron diffraction

Abstract

To provide methods and systems of electron diffraction.SOLUTION: A method of producing an electron diffraction pattern comprises directing an electron beam to be incident upon a sample, and detecting, by a particle detector with an array of pixels, electrons scattered from the sample. The detecting comprises, for each detected electron at a pixel, measuring an energy value that is proportional to the energy of the electron. The measured energy value of each electron and an identifier of the pixel that has detected the electron are sent to a processing device. An energy-weighted contribution value from each energy value is calculated by the processing device using an energy-dependent function. The energy-dependent function produces energy-weighted contribution values that vary with electron energy. An energy-weighted electron diffraction pattern is then generated using pixel positions associated with each pixel identifier, and the energy-weighted contribution values.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method and system for electron diffraction, and in particular to a method and system for energy-weighted electron diffraction. [Background technology]

[0002] When electrons incident on a sample interact with atoms in the sample, some of the electrons are scattered from the sample at various angles. Some of the electrons are backscattered by the atoms. If the sample is crystalline or polycrystalline, the electrons can be diffracted according to Bragg's law, which depends on the energy of the electrons, the angle of incidence with respect to the crystal planes, and the spacing of the crystal planes. The electrons scattered from the sample, including the diffracted electrons, can be detected using a particle detector. The signal provided by the detector can be processed to generate an electron backscatter diffraction (EBSD) pattern, which can be used to characterize the sample structure. This diffraction pattern appears as a series of lines that form Kikuchi bands, providing information about the sample's crystal lattice and orientation in space. By analyzing the relationships between the diffraction patterns of a sample, further information about the sample can be determined, such as grain size, texture, phase distribution, and strain.

[0003] Conventional electron particle detectors may operate in a "counting" mode, in which they count the number of electrons that strike a pixel over a time window. Furthermore, conventional particle detectors may use a predefined energy threshold such that only electrons above the threshold are counted and those below the threshold are discarded. The electrons counted at each pixel are provided in a single frame readout of all pixels, which are then used in diffraction pattern processing. For example, the counted electron numbers may be mapped to a display value, such as a color or grayscale value, and an image is presented that maps the display values ​​to pixel locations within the image.

[0004] In one embodiment, US Patent No. 8 890 065 describes an apparatus for detecting one or each of the Kikuchi and Kossel diffraction patterns. The apparatus comprises an electron column adapted in use to provide an electron beam directed towards the sample, the electron beam having an energy in the range of 2 keV to 50 keV. The apparatus further comprises a particle detector for receiving and counting electrons from the sample resulting from the interaction of the electron beam with the sample. The detector comprises an array of pixels and has a count rate capability of processing at least 1000 electrons per second for each pixel. The particle detector is adapted to provide electron energy filtering of the received electrons in order to count only those received particles that represent the diffraction pattern.

[0005] The particle detectors described above operate in a cumulative manner, summing up the number of electron impacts over a time frame before reading out. Other particle detectors operate in a data-driven mode, reading out electronic data each time the detector records an electron impact. For example, European Patent Publication No. EP3761016 describes a method of imaging a sample, which includes repeatedly scanning a region of interest (ROI) of the sample by directing a charged particle beam toward multiple impact points in the ROI and detecting particles scattered from the multiple impact points during the repeated scans, calculating a signal quality for each of the multiple impact points, and updating the signal quality of the ROI based on the signal quality of each impact point after each single scan of the ROI, where the signal quality of a particular impact point is calculated based on the detected particles scattered from the impact point, terminating the repeated scans in response to the signal quality of the ROI being higher than a threshold signal quality, and forming a structural image of the ROI based on the particles detected during the repeated scans. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] U.S. Patent No. 8,890,065 [Patent Document 2] European Patent Publication No. EP3761016 Summary of the Invention [Problem to be solved by the invention]

[0007] In electron diffraction, high energy electrons often provide the most information about the sample. However, it can also be useful to fully utilize the electron energy spectrum, for example to improve the signal-to-noise statistics. In conventional electron diffraction methods, a set energy threshold is applied and only electrons above / below the threshold are accepted. However, this loses information from electrons whose energy does not meet the threshold. [Means for solving the problem]

[0008] In a first aspect, there is a method of generating an electron diffraction pattern. The method includes directing an electron beam to be incident on a sample and detecting electrons scattered from the sample with a particle detector having an array of pixels. The detecting includes measuring an energy value for each electron detected at a pixel, the energy value being proportional to the energy of the detected electron. For example, measuring the energy value may include measuring the time that an induced charge in the pixel struck by the electron exceeds a detector threshold level. This measured time is proportional to the charge induced in the pixel, and therefore also proportional to the actual energy of the detected electron as measured by the particle detector.

[0009] The method further includes transmitting to a processing device the measured energy value of each detected electron and an identifier of the pixel that detected the electron. These measured energy values ​​are transmitted each time an electron strikes the pixel array such that information regarding the electron's energy is provided to the processing device in real time. The pixel identifier is an identifier that enables the processing device to determine the location within the pixel array where the electron was detected. For example, the identifier may identify the pixel location within the array of pixels where the electron was detected.

[0010] The method further includes calculating, by the processing device, an energy-weighted contribution value from each measured energy value using an energy-dependent function, where the energy-dependent function varies with electron energy such that the contribution of each detected electron depends on its energy, and thus electron energy can be used as an additional discrimination parameter in generating the diffraction pattern.

[0011] The method further includes generating, by the processing device, an energy-weighted electron diffraction pattern using the pixel locations and the energy-weighted contribution values ​​associated with each pixel identifier.

[0012] Since the energy-dependent function varies with electron energy, the energy-dependent function may increase or decrease the contribution of a detected electron to the generated electron diffraction pattern depending on the energy of that detected electron. For example, the energy-dependent function may increase the contribution of detected electrons with high energy relative to detected electrons with low energy. The energy-weighted contribution value may be higher for detected electrons than the energy-weighted contribution energy value for all detected electrons with low energy. The energy-dependent function may generate energy-weighted contribution values ​​that increase with increasing energy values. For example, the energy-dependent function may generate energy-weighted contribution values ​​that increase linearly with increasing electron energy values. The energy-dependent function may further be user-configurable.

[0013] As part of this method, the detecting step may further include, for each detected electron, measuring a detection time of the detected electron. The detection time may be the arrival time of the electron at the pixel, or may be the time at which the signal is transmitted to a processing device. The transmitting step may further include transmitting the measured detection time of the detected electron to a processing device. The measured detection time may then be used in generating an energy-weighted electron diffraction pattern.

[0014] Generating an energy-weighted electron diffraction pattern using the pixel locations and the energy-weighted contribution values ​​may include generating an energy-weighted electron diffraction pattern that includes an array of image pixels arranged to follow the array of pixels of the particle detector, with each image pixel displayed with a display value proportional to a combination of the energy-weighted contribution values ​​of electrons detected to have impinged on the corresponding pixel of the particle detector. For example, the display value may be a sum of all of the energy-weighted contribution values ​​of electrons detected to have impinged on the corresponding pixel of the particle detector.

[0015] The generated energy-weighted electron diffraction pattern may be a color image and the displayed values ​​are values ​​on a color gradient, or the generated energy-weighted electron diffraction pattern may be a grayscale image and the displayed values ​​are intensities.

[0016] The quality of the generated energy-weighted electron diffraction pattern is affected by background signals, and to improve this quality, the method may further include performing background subtraction by detecting electrons received by the particle detector when the electron beam is not incident on the sample, and subtracting, for each image pixel, from the display value of the corresponding image pixel, an amount proportional to the electrons counted for each pixel of the detector.

[0017] The method may further include applying an energy filter to the measured energy values ​​or the energy-weighted contribution values. For example, the energy filter may be either a predetermined energy band, one or more electron energy ranges, or a predetermined energy threshold. Applying an energy filter may be effective to discard undesired results and reduce processing demands.

[0018] The method may further include displaying the generated energy-weighted electron diffraction pattern.

[0019] The method may further include interpreting the generated energy-weighted electron diffraction pattern to derive one or more structural properties for the sample. The one or more structural properties may include crystallographic orientation, crystallographic phase, phase composition, texture, and strain locations.

[0020] In a second aspect, there is an electron diffraction system. The system includes an electron source configured to provide a beam of electrons to or toward a sample holder. The sample holder is configured to hold a sample. The system further includes a particle detector configured to detect electrons scattered from the sample, and a processing device communicatively coupled to the particle detector. The processing device is configured to perform all of the method steps described above.

[0021] In a third aspect, there is a computer program including computer program instructions that, when executed by a computer processing system, cause the computer processing system to perform a method of generating an energy-weighted electron diffraction pattern. The method includes receiving data including measured energy values ​​of detected electrons along with an identifier of a pixel that detected the detected electron, the data being obtained by directing an electron beam to be incident on the sample. The method further includes detecting electrons scattered from the sample with a particle detector having an array of pixels, the detecting including measuring, for each electron detected at a pixel, an energy value proportional to the energy of the detected electron. The method further includes calculating an energy-weighted contribution value from each measured energy value using an energy-dependent function, the energy-dependent function varying with electron energy. The method further includes generating an energy-weighted electron diffraction pattern using the pixel locations associated with each pixel identifier and the energy-weighted contribution value.

[0022] Optionally, the energy-dependent function increases or decreases the contribution of a detected electron to the generated energy-weighted electron diffraction pattern depending on the energy of that detected electron. The energy-dependent function may increase the contribution of detected electrons with high energy relative to detected electrons with low energy. The energy-weighted contribution value may be higher for a detected electron than the energy-weighted contribution value for all detected electrons with low energy. The energy-dependent function may generate energy-weighted contribution values ​​that increase with increasing energy values. The energy-dependent function may generate energy-weighted contribution values ​​that increase linearly with increasing energy values. The energy-dependent function may be user-configurable.

[0023] Optionally, the detecting further includes, for each detected electron, measuring a detection time of the detected electron, and the transmitting further includes transmitting the measured detection time of the detected electron to a processing device.

[0024] Optionally, measuring the energy value comprises measuring the time that a charge induced in a pixel struck by the electron remains above a detector threshold level.

[0025] Optionally, generating an energy-weighted diffraction pattern using the pixel locations and the energy-weighted contribution values ​​includes generating a diffraction pattern including an array of image pixels arranged to follow the array of pixels of the particle detector, each image pixel being displayed with a display value proportional to a combination of the energy-weighted contribution values ​​of electrons detected to have impinged on a corresponding pixel of the particle detector.

[0026] Optionally, the energy-dependent electron diffraction pattern is a colour image and the displayed values ​​are values ​​on a colour gradient, or alternatively, the energy-dependent electron diffraction pattern is a greyscale image and the displayed values ​​are intensities.

[0027] Optionally, the computer program instructions, when executed by a computer processing system, may include causing the computer processing system to perform background subtraction by detecting electrons received by the particle detector when the electron beam is not incident on the sample, and for each image pixel, subtracting an amount proportional to the electrons counted for each pixel of the detector from the display value of the corresponding image pixel.

[0028] Optionally, the computer program instructions, when executed by a computer processing system, cause the computer processing system to apply an energy filter to the measured energy values ​​or the energy weighted contribution values, which may be either a predefined energy band, one or more electron energy ranges, or a predefined energy threshold.

[0029] Optionally, the computer program instructions, when executed by a computer processing system, cause the computer processing system to display the generated energy dependent electron diffraction pattern.

[0030] Optionally, the computer program instructions, when executed by a computer processing system, cause the computer processing system to interpret the generated energy-dependent electron diffraction pattern to derive one or more structural properties for the sample, which may include crystallographic orientation, crystallographic phase, phase composition, texture, and strain location.

[0031] The computer program may include computer program instructions which, when executed by a computer processing system, cause the computer processing system to perform any and all aspects and variations of the method of the first aspect described above, which will not be repeated here for the sake of brevity.

[0032] In a fourth aspect, there is a computer readable medium having stored thereon the computer program described above.

[0033] In order that the invention may be more readily understood, reference will now be made, by way of example only, to the accompanying drawings, in which: [Brief description of the drawings]

[0034] [Figure 1] FIG. 1 illustrates an exemplary electron diffraction system. [Diagram 2] FIG. 1 illustrates a comparison of counting mode and time over threshold (TOT) mode of a particle detector. [Diagram 3] 1 illustrates an exemplary electron diffraction method. [Figure 4] FIG. 1 illustrates a histogram of electron energy measured over a time period. [Diagram 5] FIG. 1 illustrates the substantial contribution of energy-weighted contributions to an electron diffraction pattern compared to non-energy-weighted contributions. [Figure 6] FIG. 2 illustrates an image of an exemplary energy-weighted electron diffraction pattern of a sample generated using the exemplary systems and methods described above. [Figure 7] FIG. 13 illustrates a comparison of band contrast showing the effect of energy weighted contributions compared to unweighted contributions. [Figure 8] FIG. 1 illustrates an exemplary offline electron diffraction method. [Figure 9] FIG. 1 illustrates an exemplary online electron diffraction method. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0035] Electron Diffraction System FIG. 1 illustrates an exemplary electron diffraction system 100 for generating electron backscatter diffraction (EBSD) patterns that can be used in the characterization of a sample of interest.

[0036] The system 100 includes an electron beam source 105 that provides a monochromatic or substantially monochromatic beam along a beam axis 115 toward the sample 110. The electrons may have an energy selected from a range of energies, for example, 3-50 keV. The electron source 105 may be included in an electron beam column 120 as part of a scanning electron microscope (SEM) setup. The electron beam column 120 is configured to condition the electron beam to focus at a point on the sample 110 and scan the electron beam across the entire surface of the sample 110, for example, line-by-line or section-by-section. A controller 125 in communication with the electron beam column 120 controls the movement of the electron beam, starting / stopping the electron beam. The controller 125 may be part of an SEM computer 130.

[0037] Chamber 135, typically a vacuum chamber, houses sample holder 140. Sample holder 140 may be part of a manipulator 145 that is used to translate and rotate sample 110 to a desired position and orientation. In this manner, sample 110 may be positioned at a selectable angle relative to beam axis 115, for example, an angle of 70 degrees relative to beam axis 115. In other embodiments, the angle may be between 0 and 80 degrees. Electron beam column 120 is aligned with aperture 150 of chamber housing 155 so that a beam of electrons can pass through chamber 135 and contact sample 110.

[0038] The system 100 further includes a particle detector 160 disposed within the chamber 135 configured to detect electrons scattered from the sample 110. That is, the particle detector 160 detects electrons that have interacted with the sample 110. Since electron diffraction methods and systems are described herein and it is electrons that are detected, the particle detector 160 described herein is an electron detector 160. Data regarding the scattered electrons is collected by the particle detector 160 and sent to a processing device for processing into a diffraction pattern. In this embodiment, the processing device comprises a computer system including a computer 165 and / or a field programmable gate array 185. In the following description, the processing device is described as including a computer 165.

[0039] The particle detector 160 includes a front end 170 configured to process the collected data. The front end 170 pre-processes the data before sending it to a computer 165 for further processing. The front end 170 also provides power to the particle detector 160 and handles cooling, if necessary. The particle detector 160 also includes a feedthrough 175 through which a communication link is routed to enable data transfer from the chamber 135 to the computer 165.

[0040] The particle detector 160 also includes a particle detector chip 180, which comprises a semiconductor sensor chip (e.g., a silicon chip) bonded to an electronic readout chip. The semiconductor sensor chip includes an array of pixels, e.g., 256x256 pixels, or 512x448 pixels. Examples of such particle detector chips 180 include the Timepix3 (see https: / / kt.cern / technologies / timepix3) and Timepix4 (see https: / / indico.cern.ch / event / 591299 / contributions / 2423187 / attachments / 1393307 / 2123191 / Timepix4_specs.pdf and https: / / cds.cern.ch / record / 2825271) available from ADVACAM (https: / / advacam.com / ), ASI (https: / / www.amscins.com / index.html), and Quantum Detectors (https: / / quantumdetectors.com / ).

[0041] Conventional particle detectors operate in a "counting" mode where each electron that strikes a pixel is counted as 1. In most detectors, the counting mode is frame-based, meaning that the counts for each pixel are collected over a defined time interval called a frame, and the counts for each pixel are read out at the end of the frame. The readout from each pixel therefore describes the number of electrons that were counted in the time frame over which the readout was collected.

[0042] The Timepix3 and Timepix4 particle detectors 160 can also operate in a data driven mode, in which information about detected electrons is read out for each detected electron at the same time and as the particle detector 160 detects an electron striking the pixel, rather than accumulating a total count for each pixel that must be read out at the end of a frame.

[0043] When operating in a data-driven mode, these particle detectors 160 are able to measure the electron energy deposited at each pixel by each incident electron. When an electron strikes a pixel, the particle detectors 160 measure a value proportional to the energy of the incident electron. This measured energy value is further referred to as the "time over threshold" (TOT) value E. Particle detectors 160 measuring this time over threshold value operate in a "time over threshold" mode. A threshold can be used to discriminate electrons according to their energy, i.e. electrons with an energy below the threshold are not counted. Both the electron detection event and this measurement are read out from the pixel and used in further processing.

[0044] More specifically, when an electron strikes a pixel, a cascade of electron-hole pairs is generated. The number of electron-hole pairs is proportional to the energy released by the electron, and a charge is imparted to the pixel proportional to the number of electron-hole pairs. The time it takes for the pixel to discharge back to the detector threshold level after the electron strike is the time-over-threshold value, which is proportional to the charge imparted to the pixel. This time is therefore also proportional to the energy of the detected electron as measured by the particle detector 160 (i.e., the actual electron energy is the time-over-threshold value multiplied by a factor). In other words, this measured time-over-threshold value is the time that the signal output by the particle detector 160 remains above the detector threshold level, or the number of time periods (internal clock times) that the charge of the pixel remains above the detector threshold level. The detector threshold level is adjustable and can be set prior to use and changed during use to obtain a desired level of electron energy discrimination.

[0045] 2 illustrates a comparison 200 of the counting and time over threshold modes of a particle detector. The top graph displays the charge accumulated in a pixel versus time. The descending curve 205 is the charge signal recorded by the particle detector as the pixel gradually discharges over time to the detector threshold level 210.

[0046] The middle graph displays counting mode 215. When an electron strikes a pixel with enough energy to charge the pixel above the detector threshold level 210, as shown by points X and Z, a count is measured by a pixel counter in particle detector 160. When an electron strikes a pixel with insufficient energy to charge the pixel above the detector threshold level 210, as shown by point Y, no count is measured by the pixel counter.

[0047] The bottom graph displays counting of the time over threshold mode 220. When an electron with energy above the detector threshold level 210 strikes a pixel, the pixel counter counts the number of time periods that the pixel charge is still above the detector threshold level 210. This is the measured time over threshold value E mentioned above. An event at X is a high energy electron strike which results in a longer discharge time and a time over threshold value E of 4. An event at Z is a low energy electron strike which results in a shorter discharge time and a time over threshold value E of 2. An event at Y is below the detector threshold level 210 and therefore has a time over threshold value of zero.

[0048] At the same time as measuring this time over threshold value E, particle detector 160 may also measure the detection time of the detected electrons, e.g., the arrival time t of the detected electron, i.e., the timestamp of when the electron hits the pixel. These measurement data may be included in a vector (x, t, E), which also includes, for each detected electron, the pixel location x of the pixel that detected the electron.

[0049] Measurement data for each detected electron is transmitted from particle detector 160 to computer 165 for processing. Either the pixel location of each detected electron can be transmitted, or an identifier of the pixel can be transmitted from which the location can be determined in a next processing step. As particle detector 160 is operating in a data-driven mode, these data are transmitted after each electron impact, i.e., information about each detected electron, such as the vector (x, t, E), is provided to computer 165 in real time. This is in contrast to a counting mode where information is only transmitted as a full frame readout after a set measurement time.

[0050] The computer 165 is in communication with the particle detector 160 via the feedthrough 175 and receives the collected electronic data for processing to generate an electron diffraction pattern. The computer 165 includes a processor and a memory for storing computer readable instructions. The computer 165 may be any suitable computer configured to run software for generating an electron diffraction pattern. Optionally, a field programmable gate array (FPGA) 185 may be in communication with the particle detector 160 and the computer 165 for processing the received signals. In some embodiments, the computer 165 is configured to perform the method steps described below. In other embodiments, the FPGA 185 is configured to perform the method steps. Additionally, the controller 125 may be in communication with the computer 165 and / or the FPGA 185 to control processing. The computer 165 may be a separate computer from the SEM computer 130 or may be part of the SEM computer 130.

[0051] Electron Diffraction Figure 3 illustrates an exemplary electron diffraction method 300 that can be performed using the system 100 described above as shown in Figure 1. The method 300 can also be performed using other suitable electron diffraction setups, including the above-threshold time-mode particle detector 160, as previously described.

[0052] The diffraction conditions for the experiment can be set up first. For example, the sample of interest 110 is loaded into the sample holder 140 in the chamber 135 and set in a desired position and orientation by the manipulator 145. The electron beam column 120 is configured to move the electron beam to a predetermined position under the control of the controller 125 to align the electron beam with the sample 110 at a certain angle. In addition, other setup conditions can be selected and applied, such as the electron beam voltage, electron beam current, sample-to-detector distance, map size, and acquisition time.

[0053] At 305, the controller 125 initiates the electron beam, which is directed to be incident on the specimen 110. The controller 125 initiates data collection and synchronizes the collected data with the beam scan in order to know the position of the electron beam on the specimen 110 for each scattered electron detected. More specifically, since the time of data acquisition and the position of the beam are known, the measured value of the arrival time t can be used to determine which electron was scattered from which region of the specimen 110. This can be done either in a single pass (i.e., taking one picture) or in multiple passes (taking multiple pictures, appending the second picture to the first, etc.).

[0054] The electron beam targets a first region of interest on the sample 110. For example, the surface of the sample 110 can be divided into grid-like sections, with each region of interest being one or more sections of the grid. When the electron beam contacts the first region of the sample 110, a portion of the electrons from the beam are scattered at various angles towards the particle detector 160. The scattered electrons strike pixels of the particle detector 160, which measures 310 the energy E of each detected electron (where E is a time-over-threshold value proportional to the energy of the electron). Additionally, the particle detector 160 can measure the arrival time t of each detected electron. In response to each detected electron, these data for the detected electrons are transferred to the computer 165 for processing at 320, for example as a vector (x, t, E) that also stores the pixel location x for each detected electron, and transmitted for processing. As each electron is successively detected, particle detector 160 continues to transfer the detected electron data to computer 165 (particle detector 160 is operating in a data-driven mode). This is in contrast to the counting mode where data is transmitted as a full frame readout, and therefore in the data-driven mode, the total data acquisition time is not limited by the frame readout time.

[0055] After scanning the first region for a set period of time, for example 0.5 ms, the electron beam is moved by the controller 125 to a second region of interest on the sample 110, and data is collected while the electron beam is scanned across the second region until the entire, or at least a portion, of the sample 110 has been scanned.

[0056] In electron diffraction, high energy electrons are often the most useful electrons since they provide the most information about the sample 110. An exemplary histogram of an electron energy spectrum 400 illustrating the variation in the expected energies of electrons is shown in FIG. 4. It is desirable to increase the contribution of high energy electrons when generating an electron diffraction pattern. In conventional diffraction methods, an energy filter can be applied to discard electrons below a set threshold energy. However, such methods operate in a counting mode, so conventional particle detectors can only distinguish the number of electrons counted above a threshold. There is no way to know how much above this threshold the energy of each counted electron is, or even how much each electron contributes to the diffraction pattern.

[0057] It is important to be able to include low energy electrons in the diffraction pattern, since they store information about the sample 110 and background signals detected by the particle detector 160. To better distinguish between high energy and low energy electrons, the energy of the detected electrons can be used as a discrimination parameter to generate an energy-weighted diffraction pattern. As seen in step 330 of FIG. 3, the weighting is obtained using the measured energy / time over threshold value E of the measured electron data collected by the particle detector 160 in time over threshold mode, or a value derived from the measured energy / time over threshold value. This is in contrast to conventional techniques where each electron is simply counted as the same value (1 count). This weighting includes using the measured energy value, or a value that depends on the measured energy value, e.g., after scaling. In any case, the value used in generating the electron diffraction pattern is referred to herein as an energy-weighted contribution value calculated using an energy-dependent function. If the measured energy value is used directly as the energy-weighted contribution value, the energy-dependent function is effectively the measured energy value multiplied by 1. Using the calculated energy-weighted contribution values, each detected electron has a weighted-based contribution to the generated diffraction pattern 340 that depends on the energy of the detected electron.

[0058] Weighting correlates the measured energy / time over threshold values ​​with the actual energy of each detected electron (higher energy electrons are considered more important since they carry the most information). Assigning a weight (and therefore importance) to each and every detected electron improves the discriminability of the diffraction pattern. For example, weighting the data increases the amount of information that can be extracted from the diffraction pattern by increasing the useful signal to background ratio and improving the contrast of the diffraction pattern compared to conventional electron counting.

[0059] Using energy-weighted contribution values, some measured energy / time over threshold values ​​are weighted (or influenced) more heavily in the generated electron pattern compared to other values, thus reflecting the contribution of electron energy to the diffraction pattern. The energy-dependent function may change the nature of this energy-dependent contribution of each electron to the electron diffraction pattern. For example, the energy-dependent function may strengthen the contribution of high energy electrons, making them contribute more to the diffraction pattern, and reduce the contribution of low energy electrons.

[0060] The energy dependent function can be any suitable function that increases or decreases the contribution of detected electrons depending on the energy of the electrons. For example, the energy dependent function can be proportional or inversely proportional to the electron energy. The energy dependent function can be linear or non-linear, such as exponential or sinusoidal. The energy dependent function is user selectable. Any suitable function or functions can be user selected to apply to the particle detector data.

[0061] In one embodiment, the energy dependence function is F(E)=0.5E 2 where E is the measured energy value (e.g., in keV) collected by particle detector 160 operating in time over threshold mode. In another embodiment, the energy dependence function is F(E)=2E-10. To favor electrons above 10 keV and discriminate against electrons below 10 keV (for a 20 keV beam), the energy dependence function may take the form F(E)=2E-20.

[0062] The effect of using energy-weighted contribution values ​​is shown in FIG. 5. FIG. 5 illustrates the net contribution 500 to an electron diffraction pattern using energy-weighted contribution values ​​(solid line) that increase with electron energy 510 compared to a contribution that does not change with electron energy 520 (dashed line). On the x-axis, the energy of the scattered electrons is shown as a percentage of the electron beam energy. The y-axis shows the net contribution of the electrons to the generated electron diffraction pattern. As can be seen in FIG. 5, using energy-weighted contribution values ​​(solid line) increases the net contribution based on the energy of the electron, so that high energy electrons contribute more to the generated electron diffraction pattern than low energy electrons. As mentioned above, in the time-over-threshold mode, the particle detector 160 measures a time-over-threshold value E for each detected electron. Since this time depends on the energy of the electron (i.e., the more energetic the electron, the longer it takes for the pixel to discharge because the electron deposits more charge (electron-hole pairs) on the pixel upon impact), this measured time corresponds to a value proportional to the actual energy of the electron.

[0063] At 340, an electron diffraction pattern can be generated as follows: After the detected electron data is collected and the energy-weighted contributions are calculated, a matrix of the same size as the pixel array of particle detector 160, e.g., a 256×256 matrix, is created. This matrix represents where each electron was detected in the pixel array of particle detector 160, e.g., pixel location x i,j is the matrix element M i,j Each energy-weighted contribution is added to the appropriate matrix element based on the pixel location where the electron was detected, e.g., pixel location x i,j The energy-weighted contributions calculated for the matrix element M i,jThe energy-weighted contributions associated with each pixel are added together to give a cumulative sum of the energy-weighted contributions of all electrons detected by any particular pixel of the particle detector 160. Because the energy-weighted contributions take into account the energy of each and every electron, electrons contribute different counts to the corresponding matrix element, e.g., 0.5, 3, 0.1, etc., depending on their measured energy E (and the form of the energy-dependent function F(E)). This gives more importance to certain portions (or portions) of the electron energy spectrum.

[0064] In conventional counting mode, each incident electron always contributes a value of one count to the corresponding matrix element. Thus, the electron diffraction pattern is formed by simply summing each of these single counts. The method described herein, on the other hand, does not use the sum of the single counts. Instead, it calculates the sum of the contribution values ​​from each electron impact. Moreover, these contribution values ​​are energy-weighted contribution values, whose values ​​change with the energy of the electrons and whose change is determined by an energy-dependent function. Using energy-weighted contribution values ​​in this manner means that matrix elements of a pixel that has been subjected to multiple low-energy electron impacts may have a lower cumulative value than matrix elements of a pixel that has been subjected to only one high-energy electron impact.

[0065] As above, the energy-weighted contributions are calculated before being put into the matrix. In other embodiments, the energy-weighted contributions can be calculated (in the same manner) after the measured energy values ​​E are put into the matrix.

[0066] This matrix forms an energy-weighted electron diffraction pattern in its raw state. Each element of this matrix can be mapped to a display value, e.g., a color gradient value, or a grayscale intensity. An image can then be generated from these display values ​​to illustrate the energy-weighted electron diffraction pattern as a color / grayscale map. An example of such an energy-weighted electron diffraction pattern is shown in FIG. 6. The generated energy-weighted electron diffraction pattern can then be interpreted to derive one or more structural properties of the sample 110, as described in more detail below. For example, the crystal lattice of the sample and its orientation in space can be derived from the generated electron pattern. From this information and a comparison of diffraction patterns obtained across the entire surface of the sample 110 (i.e., across multiple regions / sections of interest on the surface), further properties of the sample 110 can be determined.

[0067] Further processing can be carried out to further improve the quality of the energy-weighted electron diffraction pattern.

[0068] For example, the data collected by particle detector 160 includes a background signal that can affect the quality of the energy-weighted electron diffraction pattern. The background signal corresponds to a "dark" signal generated by particle detector 160 in the absence of an electron beam incident on sample 110. Thus, the background can be known from data read out from particle detector 160 before the electron beam is incident on sample 110 and data is actually measured. This background signal can be subtracted / subtracted from each newly generated electron diffraction pattern.

[0069] Other methods to further improve the quality of the pattern include outlier removal and contrast stretching. For example, to remove outliers, the algorithm searches for matrix element values ​​that are much higher or much lower than the median value and replaces them with the median value. In contrast stretching, the lowest and highest matrix element values ​​are found, and then all of the values ​​are transformed (i.e. stretched) into a wider range of available display values, thus improving the contrast, for example, improving the contrast in the color / grayscale image shown in FIG. 6.

[0070] The images generated (see, for example, FIG. 6) show Kikuchi bands characteristic of a particular sample, providing information about the structure and orientation of the sample. FIG. 7 illustrates band profile heights 700 of two flat field patterns taken from the same sample grain. This shows a comparison of band contrasts and demonstrates the effect of using energy-weighted contributions compared to simply counting electron hits. The solid line 710 is the band profile obtained when weighting each detected electron depending on its energy. The dashed line 720 is the band profile without weighting each detected electron. By weighting, and therefore weighting, each electron, the band contrast and therefore visibility can be increased. This increases the reliability and produces more accurate results when solving the diffraction pattern using indexing algorithms. Higher band contrast makes the bands more visible to the indexing algorithm, and therefore the band peaks can be determined with greater accuracy. This allows energy-weighted patterns to be obtained in less time compared to unweighted patterns. For example, using the methods described above, energy-weighted electron diffraction patterns can be indexed after 0.7 ms of data collection, whereas using unweighted patterns would require a much longer period of data collection, e.g., 1 ms or more, to produce the same indexing success / reliability.

[0071] To aid in the interpretation of the energy-weighted electron diffraction pattern, a Hough transform can be applied to help identify bands. Bands form bright peaks (i.e., the peaks with the highest intensity) in Hough space. These minima / maxima are seen in Figure 7 and are the points located in Hough space. Other standard processing methods can also be used to identify bands. As mentioned earlier, the electron beam is moved across the region of interest of the sample, for example to provide grid-like coverage for each section of the sample surface. At each region of interest, measured data is collected and weighted, and this data is used to generate an energy-weighted electron diffraction pattern.

[0072] Hundreds to hundreds of thousands of such patterns are generated and then indexed to obtain information about the structural properties of the sample 110. For example, the patterns can be interpreted to obtain information about the crystal orientation (usually expressed in Euler angles), the crystal phase (e.g., whether the crystal phase of a particular pixel is cubic, hexagonal, orthorhombic, etc.), and the quality of the pattern (proportional to the surface quality or deformation of the sample). From such information, one can then calculate the phase composition (e.g., percentage cubic phase, percentage hexagonal phase, etc.), texture (average grain size, how the average grain is oriented toward a particular axis, etc.), as well as the orientation relationships between adjacent phases, and strain locations (e.g., determining the deformation of the sample based on the crystallographic misorientation of adjacent patterns).

[0073] Optionally, at 350, before or after weighting at 330, an energy filter may be applied to the measured data to select one or more energy ranges of interest, e.g., electrons between 10-15 keV, or only electrons above a set threshold, e.g., 10 keV. Additionally, an energy filter may be used to select energy bands. More specifically, in an implementation, an energy filter is applied to the measured time-over-threshold value E of the detected electrons. Additionally, a position filter may be applied to the pixel position x of the detected electrons, e.g., to exclude data from pixels located at or near the edge of the pixel array of the particle detector 160.

[0074] In some embodiments, the energy range of interest is unknown. Thus, an initial experiment can be performed to determine an initial electron diffraction pattern. A subsequent experiment can then be performed using weighting and filtering selected according to the initial diffraction pattern, resulting in an improved diffraction pattern result. If the energy range of interest is already known (e.g., if the sample is of a known material), an energy filter can be applied at the particle detector 160. Thus, unwanted electrons are discarded before weighting, thus saving space and increasing processing speed. Alternatively, for example, if the sample material is unknown, an energy filter can be applied after weighting. As described above, weighting can be applied at various points when performing the electron diffraction method 300. This is useful when the electron energies to be studied are not initially clear. An initial experiment can be performed, and in subsequent experiments, the energy range can be narrowed down to a region of the energy spectrum of interest. For example, if the energy-dependent function is not yet known (e.g., in the initial experiment), the energy-dependent function is applied after the measured detector data is stored, i.e., offline. If the energy dependent function is already known (e.g., in a subsequent experiment in which data has been generated that gives an idea of ​​the type of energy dependent function to be applied), the energy dependent function can be applied after the measured detector data leaves the particle detector 160 or in the FPGA 185, or the energy dependent function can be applied while the data is being stored or processed by the computer 165, i.e., online.

[0075] FIG. 8 illustrates an exemplary offline version 800 of the electron diffraction method described above. A sample is loaded into a sample holder and aligned with an electron beam column 810. At 820, experimental conditions such as electron beam voltage, electron beam current, area of ​​interest, or region of interest are set and initial data acquisition begins. Particle detector 160 records the detected electron data and generates an initial electron diffraction pattern 830 (unweighted). This initial pattern is verified for quality and if not of adequate quality, the experimental conditions are updated and the initial pattern is regenerated. Once sufficient quality is obtained, the system begins acquiring detected electron data to characterize the sample 840. At 850, an energy-dependent function is applied to the detected electron data. An energy filter may also be applied at this point. At 860, the detected electron data is reprocessed, including weighting, to generate the next energy-weighted electron diffraction pattern. At 870, the energy-weighted electron diffraction pattern is checked for quality. If the weighting improves the quality, the method ends and at 880, the experimental conditions are saved for future acquisitions. If the quality does not improve, then the energy-dependent function is adjusted (and the filtering may be adjusted, if appropriate) and reapplied to the detected electron data at 890. The detected electron data is reprocessed, again including the adjusted weights, to generate an updated energy-weighted electron diffraction pattern. This loop can be repeated until a suitable improvement is seen in the energy-weighted electron diffraction pattern.

[0076] FIG. 9 illustrates an exemplary online version 900 of the electron diffraction method described above. As with the offline method of FIG. 8, a sample is loaded into a sample holder and aligned with an electron beam column 910. At 920, experimental conditions such as electron beam voltage, electron beam current, area of ​​interest, or region of interest are set, and data acquisition begins. The particle detector 160 records the detected electron data, and an energy-dependent function is applied (and sometimes an energy filter) as the data is stored in a computer 930. At 940, the weighted electron data is processed to generate an energy-weighted electron diffraction pattern, and at 950, the quality is checked. If the weighting improves the quality, at 960, the system begins acquiring detected electron data to characterize the sample. If the quality does not improve, at 970, the energy-dependent function is adjusted (and possibly the filtering adjusted, if appropriate) and reapplied to the detected electron data. The updated weighted electron data is processed to generate an updated energy-weighted electron diffraction pattern, and quality checked again. This loop can be repeated until a suitable improvement is seen in the diffraction pattern. The experimental conditions are also updated accordingly based on the quality check. As mentioned above, an advantage of weighting the collected data is that the contrast of the diffraction pattern is increased. Another advantage of weighting the data is that the amount of data that has to be transferred from the particle detector 160 is reduced. If the particle detector 160 is operating in a data-driven mode, each electron hit is stored separately, so it is possible to discard some entries (so that they do not need to be stored). Thus, less data can be stored without compromising the quality of the results.

[0077] The energy-weighted electron diffraction patterns described herein have a variety of applications, including but not limited to scanning electron microscopes (SEM), transmission electron microscopes (TEM), scanning transmission electron microscopes (STEM), 4D STEM, transmission Kikuchi diffraction (TKD), pixelated STEM in SEM or TEM. The inclusion of electron energy provides a new dimension to the collection and analysis of information. Energy-weighted electron diffraction patterns provide better contrast and increase the amount of information that can be extracted about a sample compared to simple electron counting. The success of indexing with an electron backscatter diffraction pattern indexer at low illumination can be improved by assigning more weight to more energetic electrons. This not only provides the same spot pattern, but also provides information about the energy and arrival time of each electron. Furthermore, this can also be studied to generate energy-resolved virtual dark-field imaging that carries depth or thickness information (since electrons travel slower through heavier materials). Similarly, weighting allows for differentiation of two-dimensional material sheets (i.e., graphene) based on the intensity and energy of the acquired electrons. Furthermore, because the data is transmitted in real time, the diffraction pattern continually improves with each scan, and the arrival time t of the electrons can be used for SEM stroboscopy.

[0078] Although an SEM system set-up has been described, the electron diffraction system could alternatively be any other suitable type of system, such as a Transmission Electron Microscope (TEM) or Scanning Transmission Electron Microscope (STEM) system.

[0079] The above description describes applying an energy dependent function to the measured energy / time over threshold values ​​E. In additional / alternative embodiments, the energy dependent function can be applied to pixel position values ​​x. For example, it may be desirable to only include measurements from central pixels. Thus, a position function can be applied to increase the contribution of central pixels compared to edge pixels. Alternatively, the band detection algorithm can be tuned to find bands in specific areas of the pattern.

[0080] The above discussion has considered the importance of high energy electrons in electron diffraction techniques, but in other embodiments low energy electrons may be of more interest, and so the energy dependence function may be selected to increase the influence of low energy electrons and discriminate against high energy electrons.

Claims

1. 1. A method for generating an energy-weighted electron diffraction pattern, comprising: directing an electron beam to be incident on a specimen; detecting electrons scattered from the sample with a particle detector having an array of pixels, said detecting including measuring, for each electron detected at a pixel, an energy value proportional to the energy of the detected electron; transmitting the measured energy value of each detected electron and an identifier of the pixel that detected the electron to a processing device; calculating, by the processing device, an energy-weighted contribution value from each measured energy value using an energy-dependent function, the energy-dependent function generating energy-weighted contribution values ​​that vary with electron energy; generating, by the processing device, an energy-weighted electron diffraction pattern using the pixel locations associated with each pixel identifier and the energy-weighted contribution values.

2. The method of claim 1 , wherein the energy-dependent function increases or decreases the contribution of the detected electrons to the generated energy-weighted electron diffraction pattern depending on the energy of the detected electrons.

3. 3. The method of claim 2, wherein the energy-dependent function increases the contribution of detected electrons of high energy relative to detected electrons of low energy, such that, optionally, the energy-weighted contribution value for a detected electron is higher than the energy-weighted contribution value for all detected electrons having low energy.

4. 4. A method according to any one of claims 1 to 3, wherein the energy-dependent function generates energy-weighted contribution values ​​that increase with increasing energy values, optionally such that the energy-dependent function generates energy-weighted contribution values ​​that increase linearly with increasing electron energy values.

5. The method according to any one of claims 1 to 4, wherein the energy-dependent function is user-configurable.

6. 6. The method of claim 1, wherein the detecting further comprises, for each detected electron, measuring a detection time of the detected electron, and the transmitting further comprises transmitting the measured detection time of the detected electron to the processing device.

7. 7. The method of claim 1, wherein measuring the energy value comprises measuring the time during which a charge induced in a pixel struck by an electron remains above a detector threshold level.

8. generating the energy-weighted electron diffraction pattern using the pixel locations and the energy-weighted contribution values, 8. A method according to claim 1, comprising generating an energy-weighted electron diffraction pattern comprising an array of image pixels arranged to follow the array of pixels of the particle detector, each image pixel being displayed with a display value proportional to a combination of the energy-weighted contribution values ​​of the electrons detected impinging on the corresponding pixel of the particle detector.

9. the energy-weighted electron diffraction pattern is a color image and the displayed values ​​are values ​​on a color gradient; or The method of claim 8 , wherein the energy-weighted electron diffraction pattern is a grayscale image and the displayed values ​​are intensities.

10. Further background difference, detecting, by the particle detector, electrons received when the electron beam is not incident on the sample; and for each image pixel subtracting from the representation value of the corresponding image pixel an amount proportional to the electrons counted for each pixel of the detector.

11. 11. The method of any one of claims 1 to 10, further comprising applying an energy filter to the measured energy values ​​or the energy weighted contribution values, optionally the energy filter being either a predefined energy band, one or more electron energy ranges, or a predefined energy threshold.

12. The method of any one of claims 1 to 11, further comprising displaying the generated energy-weighted electron diffraction pattern.

13. 13. The method of any one of claims 1 to 12, further comprising interpreting the generated energy-weighted electron diffraction pattern to derive one or more structural properties for the sample, optionally the one or more structural properties comprising crystallographic orientation, crystalline phase, phase composition, texture, and strain locations.

14. 1. An electron diffraction system comprising: an electron source configured to provide a beam of electrons to a sample holder, the sample holder configured to hold a sample; a particle detector configured to detect electrons diffracted from the sample; and An electron diffraction system comprising: a processing device communicatively coupled to the particle detector, the processing device configured to perform the method of any one of claims 1 to 13.

15. A computer program comprising computer program instructions which, when executed by a computer processing system, cause the computer processing system to perform a method for generating an energy-weighted electron diffraction pattern, the method comprising: receiving data including measured energy values ​​of the detected electrons along with an identifier of a pixel that detected the detected electrons, the data being obtained by directing an electron beam to be incident on a specimen; detecting electrons scattered from the specimen with a particle detector having an array of pixels, the detecting including measuring, for each electron detected at a pixel, an energy value proportional to the energy of the detected electron; calculating an energy-weighted contribution value from each measured energy value using an energy-dependent function, said energy-dependent function producing an energy-dependent contribution value that varies with electron energy; generating an electron diffraction pattern using pixel locations associated with each pixel identifier and the energy-weighted contribution values; or A computer program including a computer readable medium having stored thereon such a computer program.

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