Electron microscope and its control method

Abstract

This invention provides an electron microscope and a control method therefor that allow for rapid adjustment of multiple parameters. [Solution] An electron microscope comprising an electron source that emits an electron beam to irradiate a sample, a detector that detects electrons emitted from the sample, and a control unit that acquires an observation image based on the detection signal output from the detector and controls the operation of each part, wherein the control unit calculates a texture feature quantity for each of the observation images acquired while changing a plurality of parameters, using the pixel value of the pixel of interest in the observation image and the pixel values ​​of the surrounding pixels of the pixel of interest, and sets the parameters based on the texture feature quantity.

Description

Technical Field

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

Background Art

[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 signal. In order to acquire a high-resolution observation image, it is necessary to adjust the focal length of the electron beam using an objective lens. Since adjusting the focal length requires time and effort, it is preferable that the focal length can be easily selected.

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

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in Patent Document 1, consideration regarding adjustment of parameters other than the focal length is insufficient. In an electron microscope, in addition to the objective lens, various lenses and correctors are provided, and it is necessary to adjust their parameters. Since the operator adjusts a plurality of parameters while visually observing the observation image, it takes time to set the parameters. In addition, since the adjustment is performed based on the evaluation of the observation image by the subjective judgment of the operator, it is personal and the results vary.

[0006] Therefore, the present invention aims to provide an electron microscope and a control method thereof that can adjust multiple parameters with high precision in a short amount of time. [Means for solving the problem]

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

[0008] The present invention also relates to a control method for an electron microscope comprising an electron source that emits an electron beam to irradiate 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 part, wherein the control unit calculates a texture feature quantity for each of the observation images acquired while changing a plurality of parameters, using the pixel value of the pixel of interest in the observation image and the pixel values ​​of the surrounding pixels of the pixel of interest, and sets the parameters based on the texture feature quantity. [Effects of the Invention]

[0009] According to the present invention, it is possible to provide an electron microscope and a control method thereof that can adjust multiple parameters in a short time. [Brief explanation of the drawing]

[0010] [Figure 1] A diagram showing an example of the overall configuration of the electron microscope in Example 1. [Figure 2] A diagram showing an example of the configuration of the control unit. [Figure 3] A diagram showing an example of the processing flow in Example 1. [Figure 4] This diagram illustrates the observational images obtained while changing multiple parameters. [Figure 5] This figure shows an example of an observed image obtained while changing multiple parameters. [Figure 6] This figure shows a graph illustrating an example of the relationship between multiple parameters and texture features. [Modes for carrying out the invention]

[0011] The following describes embodiments of the electron microscope according to the present invention with reference to the attached drawings. An electron microscope is a device that detects secondary electrons, backscattered electrons, and transmitted electrons generated from a sample by irradiating the sample with an electron beam, and generates an observation image based on the detected signals. Electron microscopes include scanning electron microscopes, transmission electron microscopes, and scanning transmission electron microscopes, and in the following description, a scanning transmission electron microscope will be used as an example. [Examples]

[0012] The overall configuration of the scanning transmission electron microscope of Example 1 will be explained 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 comprises a microscope body 100 and a control unit 140. The microscope body 100 is equipped with an electron source 101, an extraction electrode 102, a condenser lens 111, a condenser diaphragm 112, an axis adjustment unit 113, an aberration corrector 114, a scanning deflector 115, an objective lens 120, a sample stage 122, an objective diaphragm 123, an axis adjustment unit 124, a field diaphragm 125, an imaging lens 130, and a detector 131. The inside of 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 to irradiate the 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 near the optical axis passes. 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 the 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 near the optical axis pass. The axis adjustment unit 124 adjusts the axis of the transmitted electrons. The field aperture 125 has a hole through which transmitted electrons involved in electron diffraction pass. The imaging lens 130 images the transmitted electrons onto 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, which generates an observation image based on the detection signal transmitted from the detector 131 and controls the operation of each part provided on the microscope body 100.

[0015] An example configuration of the control unit 140 will be explained using Figure 2. The control unit 140 includes a processor 201, memory 202, auxiliary storage unit 203, input unit 204, output unit 205, communication interface 206, and bus 207. The processor 201 is, for example, a CPU (Central Processing Unit) and executes programs related to the generation of observation images and operation control. The memory 202 is, for example, RAM (Random Access Memory) and stores the currently running program and data used by the program temporarily. The auxiliary storage unit 203 is, for example, an HDD (Hard Disk Drive) or SSD (Solid State Drive) and stores programs and various data. The input unit 204 is, for example, a keyboard, mouse, or touch panel and is used when the operator inputs commands or data. The output unit 205 is, for example, a liquid crystal display or printer and displays or prints various images such as observation images and the execution results of programs. The communication interface 206 is an interface for connecting to an external network. 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 interface 206, enabling them to communicate with each other.

[0016] As explained using Figure 1, the microscope body 100 of a scanning transmission electron microscope is equipped with various lenses, correctors, diaphragms, and adjustment parts. To improve the image quality of the observed image, it is necessary to properly adjust each of these parts, and since the operator adjusts the parameters of each part while visually observing the observed image, it takes a long time to complete the adjustment.

[0017] Therefore, in Example 1, every time a plurality of parameters are changed, an observation image is acquired, and based on the texture feature amounts calculated for each observation image, the parameters of each part are set. The texture feature amount is, for example, a quantification of a regular pattern or structure that appears in a local region within the observation image, and is calculated using the pixel value of the pixel of interest in the observation image and the pixel values of the surrounding pixels that are pixels located around the pixel of interest. For calculating the texture feature amount, for example, GCLM (Gray-Level Co-occurrence Matrix) is used. In addition to GCLM, GLRLM (Gray-Level Run-Length Matrix), GLSZM (Gray-Level Size-Zone Matrix), NGTDM (Neighboring Gray-Tone Difference Matrix), GLDM (Gray-Level Dependence Matrix), etc. may also be used for calculating the texture feature amount.

[0018] Using FIG. 3, an example of the processing flow of Example 1 will be described for each processing step.

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

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

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

[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 along with the parameters used when acquiring the observation image used to calculate the texture features.

[0023] (S305) The control unit 140 determines whether the termination condition has been met. If the termination condition is met, the process proceeds to S308; otherwise, the process returns to S304 via S306 and S307. In other words, the parameter change in S306, the acquisition of the observed image in S307, and the calculation of texture features in S304 are repeated until the termination condition is met.

[0024] The termination conditions include, for example, acquiring all observed images within a predetermined parameter range, or the time required for repeating steps S306, S307, and S304 exceeding a predetermined time. Alternatively, the termination condition may be when the operator issues a command to terminate the repetition of steps S306, S307, and S304.

[0025] (S306) The control unit 140 changes the parameters of various lenses, correctors, apertures, and adjustment units provided on the lens body 100. The parameters may be changed using predetermined increments. Alternatively, the parameters may be changed under predetermined conditions in accordance with the amount of change in the texture feature quantity.

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

[0027] Using Figure 4, we will explain the observation images acquired by repeatedly changing multiple parameters through steps S306, S307, and S304. Figure 4 shows 63 observation images obtained by changing parameter A in 9 ways (A1 to A9) and parameter B in 7 ways (B1 to B7). Each square in Figure 4 represents one observation image, and parameters A and B are, for example, the intensity of lens A and the intensity of lens B. Observation image 401 is obtained when the parameter combination is A1 and B1, observation image 402 when it is A1 and B4, and observation image 403 when it is A5 and B4.

[0028] Using Figure 5, we will explain examples of observation images acquired while changing multiple parameters. Figure 5 illustrates observation images 401 to 403 from Figure 4. The sample 121 has six convex parts extending in the Y direction. Evaluation regions 501 to 503 are set for each of the observation images 401 to 403, and average pixel value profiles 511 to 513 are created for each of the evaluation regions 501 to 503. 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 in the Y direction of the pixel value at the X coordinate.

[0029] As illustrated in Figure 5, changing the parameters alters the image quality of the observed image. In observed image 401, the presence of the convex part is unclear, while in observed image 402, the presence of the convex part is clear. Furthermore, in observed image 402, the edge of the convex part is unclear, while in observed image 403, the edge of the convex part is clear. Returning to the explanation of Figure 3.

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

[0031] Figure 6 illustrates a graph that shows an example of the relationship between multiple parameters and texture features. Figure 6 shows a graph of texture features calculated for each of the 63 observed images obtained using parameters A1-A9 and parameters B1-B7. The graph in Figure 6 shows that the texture features are maximized when parameter A is A5 and parameter B is B4, indicating that A5 and B4 are optimal values. Note that the parameters that result in the minimum texture features may also be optimal values.

[0032] (S309) The control unit 140 sets the parameters using the optimal values ​​shown in S308. After the parameters are set in S309, observation images of other regions of the sample 121 may be acquired.

[0033] As explained using Figure 3, texture features are calculated for each observed image obtained each time multiple parameters are changed, and each parameter is set based on the calculated texture features, allowing multiple parameters to be adjusted in a short amount of time.

[0034] Note that S304 may calculate multiple texture features. When multiple texture features are calculated, the optimal parameter value may differ for each texture feature. Therefore, the parameter that yields the maximum or minimum value when the weighted sum of the multiple texture features is calculated is considered the optimal value. The weight coefficients for each texture feature used in the weighted sum are predetermined. For example, the weight coefficients for each texture feature are set according to the priority order set by the operator.

[0035] Alternatively, based on the relationship between multiple parameters and texture features obtained by repeating steps S306, S307, and S304, machine learning may be performed with the parameters as explanatory variables and the texture features as the target variable to generate a model used for setting parameters when observing sample 121. By generating a model for parameter setting, parameters can be set without executing the processing flow shown in Figure 4.

[0036] The embodiments of the electron microscope of the present invention have been described above. The present invention is not limited to the above embodiments, and the components can be modified and implemented without departing from the spirit of the invention. Furthermore, the multiple components disclosed in the above embodiments may be combined as appropriate. In addition, some components may be deleted from all the components shown in the above embodiments. [Explanation of Symbols]

[0037] Microscope body 100, electron source 101, extraction electrode 102, condenser lens 111, condenser diaphragm 112, axis adjustment unit 113, aberration corrector 114, scanning deflector 115, objective lens 120, sample 121, sample stage 122, objective diaphragm 123, axis adjustment unit 124, field diaphragm 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 irradiate a sample, a detector that detects electrons emitted from the sample, and a control unit that acquires an observation image based on the detection signal output from the detector and controls the operation of each part, The electron microscope is characterized in that the control unit calculates a texture feature for each of the observed images acquired while changing a plurality of parameters, using the pixel value of the pixel of interest in the observed image and the pixel values ​​of the surrounding pixels of the pixel of interest, and sets the parameters based on the texture feature.

2. An electron microscope according to claim 1, The electron microscope is characterized in that the control unit uses one of the following for calculating the texture features: GCLM (Gray-Level Co-occurrence Matrix), GLRLM (Gray-Level Run-Length Matrix), GLSZM (Gray-Level Size-Zone Matrix), NGTDM (Neighboring Gray-Tone Difference Matrix), or GLDM (Gray-Level Dependence Matrix).

3. An electron microscope according to claim 1, An electron microscope further comprising an output unit that outputs the relationship between the aforementioned parameters and the aforementioned texture features.

4. An electron microscope according to claim 1, The electron microscope is characterized in that the control unit calculates the texture feature quantity in an evaluation region set for the observed image.

5. An electron microscope according to claim 1, The electron microscope is characterized in that the control unit calculates a plurality of texture features for each of the observed images and sets the parameters based on a value obtained by weighting and summing the plurality of texture features.

6. An electron microscope according to claim 1, The electron microscope is characterized in that the control unit performs machine learning with the parameters as explanatory variables and the texture features as the target variable, and generates a model used for setting the parameters.

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

Images