Electron microscope and control method for same

Abstract

Provided is an electron microscope in which a plurality of parameters can be adjusted in a short amount of time, and a method for controlling the electron microscope. The electron microscope comprises: an electron source that emits an electron beam with which a sample is irradiated; a detector that detects electrons emitted from the sample; and a control unit that acquires observation images on the basis of detection signals output from the detector and controls the operation of each part, the electron microscope characterized in that, for each observation image acquired while changing a plurality of parameters, the control unit calculates a texture feature amount by using a pixel value of a pixel of interest in the observation image and pixel values of peripheral pixels of the pixel of interest, and sets the parameters on the basis of the texture feature amount.

Description

Electron microscope and its control method

[0001] The present invention relates to an electron microscope used for observing a sample, and more particularly to setting parameters of the electron microscope.

[0002] An electron microscope is a device that detects secondary electrons, reflected electrons, and transmitted electrons generated in a sample irradiated with an electron beam and acquires an observation image of the sample based on the detection signals. To acquire a high-resolution observation image, it is necessary to adjust the focal length of the electron beam using an objective lens. However, since adjusting the focal length is time-consuming, it is preferable that the focal length can be easily selected.

[0003] Patent Document 1 discloses a method of determining contrast evaluation values ​​of each observation image acquired at different focal lengths, calculating an approximation curve showing the relationship between the contrast evaluation values ​​and the focal lengths, and selecting a focal length at which the differential value of the approximation curve changes from negative to positive.

[0004] JP 2017-10764 A

[0005] However, Patent Document 1 does not adequately consider the adjustment of parameters other than focal length. Electron microscopes are equipped with various lenses and correctors in addition to objective lenses, and the parameters of these lenses and correctors must be adjusted. Since the operator adjusts multiple parameters while visually observing the observed image, parameter setting takes time. Furthermore, adjustments are made based on the operator's subjective evaluation of the observed image, which can lead to variations in results depending on the individual.

[0006] SUMMARY OF THE INVENTION It is therefore an object of the present invention to provide an electron microscope capable of adjusting a plurality of parameters with high precision in a short time, and a control method thereof.

[0007] In order to achieve the above object, the present invention provides an electron microscope comprising an electron source that emits an electron beam to be irradiated onto a sample, a detector that detects electrons emitted from the sample, and a control unit that acquires an observation image based on a detection signal output from the detector and controls the operation of each unit, wherein the control unit calculates texture features for each of the observation images acquired while changing a plurality of parameters, using a pixel value of a pixel of interest in the observation image and pixel values ​​of pixels surrounding the pixel of interest, and sets the parameters based on the texture features.

[0008] The present invention also provides a control method for an electron microscope comprising an electron source that emits an electron beam to be irradiated onto a sample, a detector that detects electrons emitted from the sample, and a control unit that acquires an observation image based on a detection signal output from the detector and controls the operation of each unit, wherein the control unit calculates texture features for each of the observation images acquired while changing a plurality of parameters, using a pixel value of a pixel of interest in the observation image and pixel values ​​of pixels surrounding the pixel of interest, and sets the parameters based on the texture features.

[0009] According to the present invention, it is possible to provide an electron microscope capable of adjusting a plurality of parameters in a short time, and a control method thereof.

[0010] FIG. 1 is a diagram showing an example of the overall configuration of an electron microscope according to a first embodiment; FIG. 2 is a diagram showing an example of the configuration of a control unit; FIG. 3 is a diagram showing an example of a processing flow according to a first embodiment; FIG. 4 is a diagram explaining an observation image acquired while changing a plurality of parameters; FIG. 5 is a diagram showing an example of an observation image acquired while changing a plurality of parameters; and FIG. 6 is a diagram showing a graph as an example of the relationship between a plurality of parameters and texture feature amounts.

[0011] An embodiment of an electron microscope according to the present invention will be described below with reference to the accompanying drawings. An electron microscope is a device that detects secondary electrons, reflected electrons, and transmitted electrons generated from a sample by irradiating the sample with an electron beam, and generates an observation image based on the detection signals. Electron microscopes include scanning electron microscopes, transmission electron microscopes, and scanning transmission electron microscopes, and the following description will use a scanning transmission electron microscope as an example.

[0012] The overall configuration of the scanning transmission electron microscope of Example 1 will be described using Figure 1. The vertical direction is defined as the Z direction, and the horizontal directions are defined as the X and Y directions. The scanning transmission electron microscope includes a microscope body 100 and a control unit 140. The microscope body 100 is provided with an electron source 101, an extraction electrode 102, a condenser lens 111, a condenser aperture 112, an axis adjustment unit 113, an aberration corrector 114, a scanning deflector 115, an objective lens 120, a sample stage 122, an objective aperture 123, an axis adjustment unit 124, a field aperture 125, an imaging lens 130, and a detector 131. The microscope body 100 is evacuated by a vacuum pump or the like, and a sample 121 is held on the sample stage 122.

[0013] The electron source 101 emits an electron beam that is irradiated onto a sample 121. The extraction electrode 102 extracts the electron beam from the electron source 101. The condenser lens 111 shapes the electron beam into a parallel beam. The condenser aperture 112 has a hole through which the electron beam passes near the optical axis. The axis adjustment unit 113 adjusts the axis of the electron beam. The aberration corrector 114 adjusts the cross-sectional shape of the electron beam. The scanning deflector 115 deflects the electron beam so that an observation area of ​​the sample 121 is scanned. The objective lens 120 focuses the electron beam onto the sample 121. The sample stage 122 holds the sample 121 and has a hole through which transmitted electrons, which are electrons that have passed through the sample 121, pass. The objective aperture 123 has a hole through which transmitted electrons pass near the optical axis. The axis adjustment unit 124 adjusts the axis of the transmitted electrons. The field aperture 125 has a hole through which transmitted electrons related to electron diffraction pass. The imaging lens 130 forms an image of the transmitted electrons on the detector 131. The detector 131 detects the transmitted electrons and transmits a detection signal to the control unit 140.

[0014] The control unit 140 is, for example, a computer, and generates an observation image based on the detection signal transmitted from the detector 131 , and controls the operation of each unit provided in the microscope body 100 .

[0015] An example configuration of the control unit 140 will be described using FIG. 2 . The control unit 140 includes a processor 201, a memory 202, an auxiliary storage unit 203, an input unit 204, an output unit 205, a communication I / F 206, and a bus 207. The processor 201 is, for example, a central processing unit (CPU) that executes programs related to generating observation images and controlling operations. The memory 202 is, for example, a random access memory (RAM) that expands running programs and temporarily stores data used by the programs. The auxiliary storage unit 203 is, for example, a hard disk drive (HDD) or a solid state drive (SSD) that stores programs and various data. The input unit 204 is, for example, a keyboard, mouse, or touch panel that is used by an operator to input commands and data. The output unit 205 is, for example, an LCD display or printer that displays or prints various images such as observation images and program execution results. The communication I / F 206 is an interface for connecting to an external network. The bus 207 is a signal line that connects the processor 201, memory 202, auxiliary storage unit 203, input unit 204, output unit 205, and communication I / F 206, and enables mutual communication among them.

[0016] 1, the scanning transmission electron microscope body 100 is provided with various lenses, correctors, diaphragms, and adjustment units. To improve the image quality of the observed image, each of these units must be appropriately adjusted, and since the operator adjusts the parameters of each unit while visually observing the observed image, it takes a long time to complete the adjustment.

[0017] Therefore, in the first embodiment, an observation image is acquired each time a plurality of parameters are changed, and parameters for each part are set based on texture features calculated for each observation image. The texture features are, for example, a quantification of regular patterns or structures appearing in a local region within the observation image, and are calculated using the pixel value of a pixel of interest in the observation image and the pixel values ​​of surrounding pixels that are pixels located around the pixel of interest. The texture features are calculated using, for example, a gray-level co-occurrence matrix (GCLM). Other than the GCLM, other methods such as a gray-level run-length matrix (GLRLM), a gray-level size-zone matrix (GLSZM), a neighboring gray-tone difference matrix (NGTDM), or a gray-level dependency matrix (GLDM) may also be used to calculate the texture features.

[0018] An example of the processing flow of the first embodiment will be described for each processing step with reference to FIG.

[0019] (S301) The sample 121 is attached to the sample stage 122 and set in the microscope body 100 of the scanning transmission electron microscope. When the sample 121 is set in the microscope body 100, the inside of the microscope body 100 is evacuated to a vacuum.

[0020] (S302) The control unit 140 acquires an observation image of the sample 121. Note that initial values ​​are set for the parameters of various lenses, correctors, diaphragms, and adjustment units.

[0021] (S303) An evaluation area is set for the observation image acquired in S302. The setting of the evaluation area for the observation image may be performed by the operator via the input unit 204, or may be performed by the control unit 140 using template matching processing. Alternatively, a designated area stored in advance in the auxiliary storage unit 203 may be read out and set as the set area.

[0022] (S304) The control unit 140 calculates texture features in the evaluation region set in S302. The calculated texture features are stored in the auxiliary storage unit 203 together with parameters used when the observation image was acquired and used to calculate the texture features.

[0023] (S305) The control unit 140 determines whether or not the termination condition is satisfied. If the termination condition is satisfied, the process proceeds to S308. If not, the process returns to S304 via S306 and S307. That is, the parameter change in S306, the acquisition of the observation image in S307, and the calculation of the texture feature amount in S304 are repeated until the termination condition is satisfied.

[0024] The termination condition may be, for example, that all observation images have been acquired within a predetermined parameter range, that the time required to repeat steps S306, S307, and S304 exceeds a predetermined time, or that the operator issues a command to terminate the repetition of steps S306, S307, and S304.

[0025] (S306) The control unit 140 changes parameters of the various lenses, correctors, apertures, and adjustment units provided in the mirror body 100. The parameters may be changed using a predetermined interval. The parameters may also be changed under predetermined conditions in accordance with the amount of fluctuation in the texture feature amount.

[0026] (S307) The control unit 140 acquires an observation image of the sample 121 using the parameters changed in S306.

[0027] Using Fig. 4, we will explain observation images acquired while changing multiple parameters by repeating S306, S307, and S304. Fig. 4 illustrates 63 observation images acquired while changing parameter A in nine ways (A1 to A9) and parameter B in seven ways (B1 to B7). Note that each square in Fig. 4 represents one observation image, and parameters A and B represent, for example, the strength of lens A and the strength of lens B. Observation image 401 is acquired when the parameter combination is A1 and B1, observation image 402 is acquired when the parameter combination is A1 and B4, and observation image 403 is acquired when the parameter combination is A5 and B4.

[0028] An example of an observation image acquired while changing a plurality of parameters will be described using FIG. 5. FIG. 5 illustrates the observation images 401 to 403 of FIG. 4. The sample 121 has six convex portions extending in the Y direction. Evaluation areas 501 to 503 are set in the observation images 401 to 403, respectively, and average pixel value profiles 511 to 513 are created in the evaluation areas 501 to 503, respectively. The horizontal axis of the average pixel value profiles 511 to 513 is the coordinate in the X direction, and the vertical axis is the average value of the pixel values ​​in the Y direction at the X coordinate.

[0029] 5, the image quality of the observation image changes as the parameters are changed, and the presence of the convex portions is unclear in the observation image 401, whereas the presence of the convex portions is clear in the observation image 402. Also, the edges of the convex portions are unclear in the observation image 402, whereas the edges of the convex portions are clear in the observation image 403. Returning to the explanation of FIG.

[0030] (S308) The control unit 140 creates a relationship between texture features and parameters using the data stored in the auxiliary storage unit 203 by repeating S306, S307, and S304, and outputs the relationship to the output unit 205. For example, a graph showing the relationship between texture features and parameters is displayed on a liquid crystal display, which is the output unit 205.

[0031] A graph showing an example of the relationship between a plurality of parameters and texture features will be described using Fig. 6. Fig. 6 shows a graph of texture features calculated for each of 63 observation images acquired using parameters A1 to A9 and parameters B1 to B7. The graph in Fig. 6 shows that the texture feature is maximized when parameter A is A5 and parameter B is B4, and that A5 and B4 are optimal values. Note that the parameter that minimizes the texture feature may also be the optimal value.

[0032] (S309) The control unit 140 sets the parameters using the optimal values ​​indicated in S308. After the parameters are set in S309, an observation image of another region of the sample 121 may be captured.

[0033] According to the processing flow described with reference to FIG. 3 , texture features are calculated for each of the observation images acquired each time a plurality of parameters are changed, and each parameter is set based on the calculated texture features, so that a plurality of parameters can be adjusted in a short time.

[0034] Note that multiple texture features may be calculated in S304. When multiple texture features are calculated, the optimal parameter value may differ for each texture feature, so the optimal parameter value is determined as the parameter that maximizes or minimizes the weighted sum of the multiple texture features. The weighting coefficients for each texture feature used in the weighting sum are determined in advance. For example, the weighting coefficients for each texture feature are set according to a priority order determined by the operator.

[0035] Furthermore, based on the relationships between the multiple parameters and the texture feature values ​​obtained by repeating S306, S307, and S304, machine learning may be performed using the parameters as explanatory variables and the texture feature values ​​as objective variables to generate a model used for setting parameters when observing the sample 121. By generating a model for parameter setting, it is possible to set parameters without executing the processing flow of FIG.

[0036] The above describes an embodiment of the electron microscope of the present invention. The present invention is not limited to the above embodiment, and the components can be modified and embodied without departing from the spirit of the invention. Furthermore, multiple components disclosed in the above embodiment may be combined as appropriate. Furthermore, some components may be deleted from all the components shown in the above embodiment.

[0037] Electron microscope body 100, electron source 101, extraction electrode 102, condenser lens 111, condenser aperture 112, axis adjustment unit 113, aberration corrector 114, scanning deflector 115, objective lens 120, sample 121, sample stage 122, objective aperture 123, axis adjustment unit 124, field aperture 125, imaging lens 130, detector 131, control unit 140, processor 201, memory 202, auxiliary storage unit 203, input unit 204, output unit 205, communication I / F 206, bus 207, observation image 401, observation image 402, observation image 403, evaluation area 501, evaluation area 502, evaluation area 503, average pixel value profile 511, average pixel value profile 512, average pixel value profile 513.

Claims

1. An electron microscope comprising an electron source that emits an electron beam to be irradiated onto a sample, a detector that detects electrons emitted from the sample, and a control unit that acquires an observation image based on a detection signal output from the detector and controls the operation of each unit, wherein the control unit calculates texture features for each of the observation images acquired while changing a plurality of parameters, using the pixel value of a pixel of interest in the observation image and the pixel values ​​of pixels surrounding the pixel of interest, and sets the parameters based on the texture features.

2. An electron microscope according to claim 1, wherein the control unit uses any one of GCLM (Gray-Level Co-occurrence Matrix), GLRLM (Gray-Level Run-Length Matrix), GLSZM (Gray-Level Size-Zone Matrix), NGTDM (Neighboring Gray-Tone Difference Matrix), and GLDM (Gray-Level Dependence Matrix) to calculate the texture feature amount.

3. The electron microscope according to claim 1, further comprising an output unit that outputs the relationship between said parameters and said texture feature amounts.

4. An electron microscope according to claim 1, wherein the control unit calculates the texture feature amount in an evaluation area set for the observation image.

5. An electron microscope according to claim 1, characterized in that the control unit calculates a plurality of texture feature amounts for each of the observation images, and sets the parameters based on a weighted sum of the plurality of texture feature amounts.

6. An electron microscope according to claim 1, wherein the control unit performs machine learning using the parameters as explanatory variables and the texture features as objective variables, and generates a model used to set the parameters.

7. A control method for an electron microscope comprising an electron source that emits an electron beam to be irradiated onto a sample, a detector that detects electrons emitted from the sample, and a control unit that acquires an observation image based on a detection signal output from the detector and controls the operation of each unit, wherein the control unit calculates texture features for each of the observation images acquired while changing a plurality of parameters, using the pixel value of a pixel of interest in the observation image and the pixel values ​​of pixels surrounding the pixel of interest, and sets the parameters based on the texture features.

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