Charged particle beam device and method for adjusting image evaluation condition

Abstract

The present disclosure provides a charged particle beam device (see fig. 2B) that executes: a process for acquiring a plurality of reference image data corresponding to a plurality of observation conditions for searching for an optimization observation condition in order to present a user (operator) with an easy-to-understand relationship between a wide variety of observation conditions and image evaluation values; a process for displaying, for the plurality of reference image data, an optimum image evaluation value map for displaying a plurality of zones corresponding to a Pareto optimal image evaluation value; a process for calculating an observation condition corresponding to a zone, and displaying a correspondence observation condition map in the same display format as the indication of the optimum image evaluation value map for each of the observation conditions; a process for displaying a location matching a selected condition on the plurality of corresponding observation condition maps when the Pareto optimum condition has been selected from the optimum image evaluation value map; and a process for displaying the numerical value of each observation condition included in the selected Pareto optimum condition as a selection observation condition indication.

Description

Charged particle beam device and image evaluation condition adjustment method

[0001] The present disclosure relates to a charged particle beam device and an image evaluation condition adjustment method, and relates to, for example, a technique for supporting observation using a charged particle beam device.

[0002] Charged particle beam devices operate by directing a narrow beam of charged particles in a vacuum onto the surface of a sample, detecting the signal emitted from the sample, and creating a two-dimensional image of the sample surface. Obtaining a two-dimensional image with the desired image evaluation values ​​(e.g., sharpness, SNR (Signal-to-Noise Ratio), etc.) requires the setting of many observation conditions (e.g., accelerating voltage, current, etc.).

[0003] However, it is known that the image evaluation values ​​fluctuate in a trade-off manner for a single observation condition (for example, increasing the lens current improves resolution but worsens SNR). In addition, the extent to which each image evaluation value fluctuates depends on the observed sample. Therefore, it is not easy to set observation conditions to acquire an image in which each image evaluation value reaches the target value.

[0004] Regarding the optimization of the settings of a plurality of observation conditions, for example, Patent Document 1 discloses that condition setting is supported by calculating a plurality of operation factors and output factors for input factors through Pareto optimization.

[0005] JP 2015-132916 A

[0006] The evaluation value of an SEM image often has a trade-off relationship with variations in observation conditions, making it difficult for an observer to understand how the image evaluation value varies with the observation conditions when acquiring an image of an observed sample. Therefore, a function that clearly displays the relationship between the observation conditions and the image evaluation value is required, but it is difficult to concisely express the relationship between various observation conditions and evaluation values. Even if the technology disclosed in Patent Document 1 is applied, this problem cannot be solved. In view of this situation, the present disclosure proposes a technology that clearly presents the relationship between various observation conditions and image evaluation values ​​to a user (operator).

[0007] In order to solve the above problems, the present disclosure proposes a charged particle beam device comprising: an irradiation control unit that controls a charged particle beam according to observation conditions when irradiating a sample with a charged particle beam to observe the sample; a storage device that stores a condition adjustment program that adjusts the relationship between multiple conditions, namely, the observation conditions and evaluation conditions based on evaluation values ​​for evaluating an image; and a processor that reads and executes the condition adjustment program from the storage device, wherein the processor performs the following processes: acquiring multiple reference image data corresponding to multiple observation conditions for searching for optimized observation conditions; displaying an optimal image evaluation value map that displays multiple divisions according to the Pareto-optimal image evaluation values ​​for the multiple reference image data; calculating observation conditions corresponding to the divisions and displaying a corresponding observation condition map in the same display format as the optimal image evaluation value map for each observation condition; when a Pareto-optimal condition is selected from the optimal image evaluation value map, displaying locations that match the selected condition on the multiple corresponding observation condition maps; and displaying the numerical values ​​of each observation condition included in the selected Pareto-optimal condition as a selected observation condition display.

[0008] Further features related to the present disclosure will become apparent from the description of this specification and the accompanying drawings. Also, aspects of the present disclosure are achieved and realized by the elements and combinations of various elements and the aspects of the following detailed description and the appended claims. The description of this specification is merely exemplary and does not limit the scope or application of the claims of the present disclosure in any way.

[0009] According to the technology of the present disclosure, a user (operator or measurer) can check at a glance the relationship between a wide variety of observation conditions and image evaluation values.

[0010] FIG. 1 is a diagram showing an example of the overall configuration of a scanning electron microscope 1 according to an embodiment of the present disclosure. FIG. 1 is a diagram showing an example of the configuration of a window (condition optimization setting screen) 201, which is a GUI (Graphical User Interface) displayed on a display unit 113 controlled by a control unit 111. FIG. 2 is a diagram showing an example of the configuration of a window (map or the like display screen) 202, which is a GUI (Graphical User Interface) displayed on a display unit 113 controlled by a control unit 111. FIG. 3 is a flowchart for explaining optimization processing of observation conditions in a charged particle beam device. FIG. 4 is a diagram showing an example of the configuration of a condition selection screen 401 for selecting observation conditions to be optimized. FIG. 5 is a diagram showing an example of the configuration of a necessary measurement point list screen 501 for displaying necessary measurement points. FIG. 6 is a flowchart for explaining predicted SEM image generation processing when reference image data corresponding to a selected combination of observation conditions and a combination of image evaluation values ​​is not in the selected image list 206. FIG. 7 is a diagram showing an example of the configuration of a window 202 according to a second embodiment.

[0011] Hereinafter, an embodiment of a charged particle beam device according to the present disclosure will be described with reference to the accompanying drawings. Hereinafter, a scanning electron microscope (SEM) that detects secondary electrons emitted from a sample will be described as an example of a charged particle beam device. In the accompanying drawings, functionally identical elements may be denoted by the same numerals. Note that while the accompanying drawings show specific embodiments in accordance with the principles of the present disclosure, these are intended to aid in understanding the present disclosure and are in no way intended to limit the interpretation of the present disclosure.

[0012] Although the present embodiment has been described in sufficient detail to enable those skilled in the art to practice the present disclosure, it should be understood that other implementations and forms are possible, and that changes in configuration and structure and substitutions of various elements are possible without departing from the scope and spirit of the technical ideas of the present disclosure. Therefore, the following description should not be interpreted as being limited thereto.

[0013] 1 is a diagram showing an example of the overall configuration of a scanning electron microscope 1 according to an embodiment of the present disclosure. The scanning electron microscope 1 includes an electron source 101, a focusing lens 102, a deflector 103, an objective lens 104, a sample stage 106, a detector 107, a control unit 111, an input unit 112, and a display unit 113. Each unit will be described below.

[0014] The electron source 101 is a device that generates an electron beam to be irradiated onto the sample 100 by emitting and accelerating electrons. The electron beam generated by the electron source 101 travels along the optical axis (Z axis), is focused by a focusing lens 102, is deflected by a deflector 103, and is then focused by an objective lens 104. Deflection by the deflector 103 causes the electron beam to scan the surface of the sample 100 (XY plane) two-dimensionally.

[0015] The sample stage 106 is a device on which the sample holder 105 for holding the sample 100 is placed and which moves the sample holder 105 in the horizontal direction (XY plane) and the vertical direction (Z-axis direction). The sample holder 105 and the sample stage 106 function as a sample holding unit for holding the sample 100. The detector 107 is a device for detecting secondary electrons emitted from the surface of the sample 100 scanned by the electron beam, and transmits a detection signal to the control unit 111.

[0016] The control unit 111 is a device including an irradiation control unit 114 that controls the electron source 101, the focusing lens 102, the deflector 103, the objective lens 104, and the sample stage 106. The control unit 111 may be configured, for example, by a general-purpose computer (including a storage device, not shown). The control unit 111 includes a processor such as a central processing unit (CPU) and memories such as random access memory (RAM) and read-only memory (ROM). The control unit 111 also includes a condition adjustment unit 115 that performs processes such as generating an observation image of the sample 100 based on a detection signal transmitted from the detector 107 and calculating a new image using multiple observation images. The processing in the control unit 111 may be realized by the processor executing a program loaded in an internal memory, not shown. Note that a portion of the control unit 111 may be configured by hardware, such as a dedicated circuit board.

[0017] The control unit 111 is connected to an input unit 112 and a display unit 113. The input unit 112 is a device that allows an operator to input imaging conditions for an observation image, and is composed of, for example, a keyboard, a mouse, a touch panel, etc. The display unit 113 is a device that displays the imaging conditions and the observation image, and is composed of, for example, a liquid crystal display, etc.

[0018] Charged particle beam devices such as the scanning electron microscope 1 are suitable for detailed observation of a sample 100, and are used, for example, to closely observe foreign particles or defects on the sample surface detected under an optical microscope. In this case, a control unit 111 controls each component of the charged particle beam device (scanning electron microscope 1) according to observation conditions and the like input by a user, and controls the display of the observation results. The control unit 111 also includes an irradiation control unit 114 that controls the charged particle beam according to the observation conditions when irradiating the sample with the charged particle beam and observing the sample, and a condition adjustment unit 115 that adjusts the relationship between multiple conditions, including the observation conditions and evaluation conditions based on evaluation values ​​for evaluating the image. The irradiation control unit 114 and the condition adjustment unit 115 constitute an irradiation control device for the charged particle beam device.

[0019] 2A and 2B are diagrams showing an example of the configuration of a GUI (Graphical User Interface) displayed on the display unit 113 controlled by the control unit 111. The GUI includes a window 201 and a window 202. Fig. 2A is a diagram showing an example of the configuration of the window (condition optimization setting screen) 201. Fig. 2B is a diagram showing an example of the configuration of the window (map or other display screen) 202.

[0020] Window 201 is a window in which the operator inputs conditions related to the search for optimal observation conditions. Window 201 is configured with, for example, an optimized observation condition and search range 203, a condition selection button 204, a required measurement point display button 205, a selected image list 206, an image selection button 207, an optimization evaluation value 208, an evaluation value selection button 209, and an optimal condition search execution button 210.

[0021] Window 202 is a window that displays, for example, the search results for optimal conditions. Window 202 is composed of an optimal image evaluation value map 211, a corresponding observation condition map 212, a selected evaluation value display 213, selected observation conditions 214, and a predicted SEM image for the selected conditions 215. Details of windows 201 and 202 will be explained together with the processing contents according to the flowchart in FIG. 3.

[0022] 3 is a flowchart for explaining the optimization process of the observation conditions in the charged particle beam device. The optimization process of the observation conditions will be explained below, with the control unit 111 acting as the subject of operations in each step.

[0023] (i) Step S301 First, in order to optimize the viewing conditions, it is necessary to calculate the optimal combination of image evaluation values ​​and the viewing conditions for each combination. As a method for these calculations, a response surface of image evaluation values ​​for the viewing conditions is estimated. The response surface of image evaluation values ​​is estimated using data of image evaluation values ​​for known viewing conditions. For this reason, reference image data is acquired, which serves as data of image evaluation values ​​for known viewing conditions. In addition, the range of viewing conditions is set when estimating the response surface.

[0024] The operator performs settings using the condition selection button 204 to display the contents to be set in the optimized observation conditions and search range 203. When the operator presses the condition selection button 204, the control unit 111 displays the condition selection screen 401 of FIG. 4. When the operator sets the type of observation conditions and the range of observation conditions to be searched for on the condition selection screen 401 and closes the condition selection screen 401 using the setting button 402, the control unit 111 reflects the settings of each observation condition in the optimized observation conditions and search range 203.

[0025] Furthermore, when the measurer clicks the required measurement points display button 205, the control unit 111 displays the required measurement points list screen 501 shown in FIG. 5 . The required measurement points list screen 501 displays a list of the minimum combinations of observation conditions required for estimating the response surface. When the required measurement points display button 205 is clicked, the control unit 111 derives the specific values ​​for each condition in this list of observation condition combinations by executing a well-known design of experiments using the types and value ranges of the observation conditions set in the display area for the optimized observation conditions and search range 203. In the search process for the optimized observation conditions, the control unit 111 uses the image data acquired under the conditions displayed in the list of observation conditions as reference image data and estimates the response surface of the image evaluation values ​​for each observation condition. In this embodiment, the observation conditions to be optimized are set as shown in FIG. 4 : "acceleration voltage (kV)," "working distance (mm)," "current (μA)," "scan speed," and "detector." FIG. 5 shows a list of observation conditions calculated by the design of experiments based on the types and ranges of the observation conditions set in FIG. 4 .

[0026] Furthermore, the measurer acquires reference image data based on the list of observation conditions displayed on the required measurement point list screen 501. The acquired reference image data is selected using the image selection button 207 of the selected image list 206, and a list of file paths for the selected image is displayed. The method of acquiring reference image data is not limited to manual acquisition by the measurer; the control unit 111 may acquire the observation conditions displayed on the required measurement point list screen 501 and acquire the image data using an automatic observation condition setting function and an automatic imaging function. Furthermore, when image data is acquired automatically, the control unit 111 automatically outputs the file path of the acquired image data to the selected image list 206. This sets the reference image data to be used for estimating the response surface of the combination of image evaluation values ​​for the combination of observation conditions.

[0027] Furthermore, when the measurer selects the type of image evaluation value to be optimized using the evaluation value selection button 209, the control unit 111 outputs the selected image evaluation value to be optimized to the optimization evaluation value 208. In this embodiment, "sharpness," "contrast," and "SNR (Signal-to-Noise Ratio)" are selected as the image evaluation values ​​to be optimized (texture may be included as other evaluation value items). After selection, when the measurer clicks the optimal condition search execution button 210, the control unit 111 executes estimation of a response surface for combinations of image evaluation values ​​for combinations of observation conditions. The control unit 111 creates a combination of observation conditions (a combination of values ​​that provides a good balance between multiple evaluation values, such as sharpness, SNR, and contrast) based on the optimization observation conditions and the type and range of observation conditions indicated by the search range 203. The combination of image evaluation values ​​is the combination of image evaluation values ​​indicated by the optimization evaluation value 208 for images acquired under each combination of observation conditions. When estimating each image evaluation value, the control unit 111 calculates each image evaluation value of the reference image data shown in the selected image list 206 and, using these values ​​as reference data (reference standard data), estimates a combination of image evaluation values ​​for each combination of optimized observation conditions within the range of observation conditions set in the search range 203. The control unit 111 then extracts, from the response surface of the combination of image evaluation values ​​for each combination of estimated observation conditions, a Pareto-optimal combination of image evaluation values ​​and a combination of observation conditions corresponding to that combination of image evaluation values.

[0028] (ii) Step S302 After extracting the Pareto-optimal combination of image evaluation values ​​and the viewing conditions corresponding to that combination of image evaluation values, the control unit 111 displays the optimal image evaluation value map 211 and the corresponding viewing condition map 212.

[0029] The optimal image evaluation value map 211 is a two-dimensional map (a collection of evaluation values) that holds the values ​​of the Pareto-optimal combinations of image evaluation values ​​extracted for each pixel. The optimal image evaluation value map 211 is a map in which the extracted Pareto-optimal combinations of image evaluation values ​​are sorted on a two-dimensional map using principal component analysis and a self-organizing algorithm. In this case, the closer two Pareto-optimal combinations of image evaluation values ​​tend to be in the optimal image evaluation value map 211, the closer the image evaluation values ​​are sorted on the two-dimensional map. The optimal image evaluation value map 211 is also configured by dividing the Pareto-optimal combinations of image evaluation values ​​into four major sections on the two-dimensional map. For example, FIG. 2B shows an optimal image evaluation value map 211 in which the Pareto-optimal combinations of image evaluation values ​​are roughly divided into four sections.

[0030] The corresponding observing condition map 212 is composed of multiple two-dimensional maps, one for each type of observing condition set in the window 201. In one corresponding observing condition map 212, the values ​​of each observing condition corresponding to each combination of Pareto-optimal image evaluation values ​​in the optimal image evaluation value map 211 are mapped to the same location on the two-dimensional map as the corresponding combination of Pareto-optimal image evaluation values ​​in the optimal image evaluation value map 211. One observing condition map in the corresponding observing condition map 212 is provided with a division display that is the same as the division of the optimal image evaluation value map 211. The observing condition map also reflects the division display of the optimal image evaluation value map 211 and provides a legend (selected observing condition 214) of the quantitative magnitude of the observing condition. Furthermore, the corresponding observing condition map 212 displays the magnitude of the condition according to the legend (selected observing condition 214) using a gradation. The location matching the selected observing condition is displayed at the selected position in the corresponding observing condition map 212. The condition magnitude display may be a gradation or a step-by-step display of the magnitude.

[0031] (iii) Step S303: When the measurer selects any pixel (image evaluation value) in the optimal image evaluation value map 211, the control unit 111 highlights and displays the selected pixel (for example, displays it as a white circle as shown in FIG. 2B ). At this time, the control unit 111 selects pixels of the viewing conditions corresponding to the selected Pareto-optimal combination of image evaluation values ​​in each viewing condition map of the corresponding viewing condition map 212, and also highlights and displays them.

[0032] Furthermore, the control unit 111 displays, as a selected evaluation value display 213, the values ​​of each image evaluation value within the combination of image evaluation values ​​held by the pixel selected in the optimal image evaluation value map 211. In other words, multiple image evaluation values ​​are displayed for each Pareto-optimal combination of image evaluation values. By changing the selected position within the optimal image evaluation value map 211, the measurer can easily grasp the tendency of quantitative changes in the observation conditions and the relative changes in the evaluation values.

[0033] Furthermore, the control unit 111 displays the values ​​of each viewing condition held by the pixel selected in the corresponding viewing condition map 212 as selected viewing conditions 214. At this time, if the measurer moves the Pareto-optimal combination of image evaluation values ​​selected in the optimal image evaluation value map 211 in a certain direction by a certain number of pixels, the control unit 111 also moves the pixels selected in each viewing condition map in the corresponding viewing condition map 212 in the same direction and by the same number of pixels. The control unit 111 also acquires the pixel positions to which each pixel has been moved, and changes the values ​​of each image evaluation value displayed as selected evaluation value display 213 and the value displayed as selected viewing condition 214 to the data for each pixel position in the optimal image evaluation value map 211 and the corresponding viewing condition map 212.

[0034] (iv) Step S304 The control unit 111 displays a predicted SEM image based on the combination of image evaluation values ​​selected by the measurer and the selected values ​​of each observation condition as a predicted SEM image for selected conditions 215. The image displayed in the predicted SEM image for selected conditions 215 displays reference image data corresponding to the combination of observation conditions and the combination of image evaluation values ​​selected by the measurer, among the reference image data displayed in the selected image list 206. Note that if the selected image list 206 does not contain reference image data corresponding to the selected combination of observation conditions and the selected combination of image evaluation values, the control unit 111 executes a process for generating a predicted SEM image for the selected conditions. This predicted SEM image generation process will be described later (see FIG. 6 ).

[0035] (v) Step S305: The control unit 111 determines the validity of the image under the selected conditions in response to the confirmation result regarding the validity of the predicted SEM image input by the operator (for example, input of the result of visual confirmation). If the control unit 111 receives input from the operator that the predicted SEM image is valid (YES in step S305), the control unit 111 ends the observation condition optimization process. On the other hand, if the control unit 111 receives input from the operator that the predicted SEM image is invalid (NO in step S305), the control unit 111 transitions the process to step S303.

[0036] More specifically, while checking the predicted SEM image 215 for the selected conditions, the operator searches for the desired combination of image evaluation values ​​displayed in the selected evaluation value display 213 and the corresponding observation conditions displayed in the selected observation conditions 214 area. The control unit 111 accepts input as to whether the image for the selected conditions is appropriate. If the predicted SEM image 215 for the selected conditions displayed in step S304 is judged appropriate by the operator's visual inspection (YES in step S305), the control unit 111 accepts termination as the image has been optimized for the observation conditions. On the other hand, if the predicted SEM image 215 for the selected conditions displayed in step S304 is judged inappropriate by the operator's visual inspection (NO in step S305), the control unit 111 returns the process to step S303 and executes the processes of steps S303 and S304 again.

[0037] The measurer searches for viewing conditions by repeatedly executing steps S303, S304, and S305. At this time, the measurer can check the trend of changes in the optimal combination of image evaluation values ​​for the viewing conditions by comparing the optimal image evaluation value map 211 and the corresponding viewing condition map 212. By enabling the measurer to check the trend of changes in the optimal image evaluation values, the number of times the processes of steps S303, S304, and S305 are repeated can be reduced.

[0038] (vi) Technical Effects Expected from the Viewing Condition Optimization Process The control unit 111 (condition adjustment unit 115) displays an optimal image evaluation value map 211 that displays divisions according to the Pareto-optimal image evaluation values. The condition adjustment unit 115 also calculates viewing conditions corresponding to the divisions and displays them as a corresponding viewing condition map 212 for each viewing condition in the same display format as the optimal image evaluation value map 211. When the measurer himself selects desired Pareto-optimal conditions from the optimal image evaluation value map 211, the control unit 111 (condition adjustment unit 115) displays locations that match the selected conditions in multiple corresponding viewing condition maps 212 and displays the numerical values ​​of each selected viewing condition as selected viewing conditions 214. This allows the measurer to set conditions while checking the corresponding viewing condition map 212 and to confirm at a glance the trends in the image evaluation values ​​and the corresponding trends of each viewing condition, making it easy to search for potentially optimal viewing conditions using multiple image evaluation values.

[0039] <Predicted SEM Image Generation Process> FIG. 6 is a flowchart for explaining the predicted SEM image generation process when the selected image list 206 does not contain reference image data corresponding to the selected combination of observation conditions and image evaluation values.

[0040] (i) Step S601 The control unit 111 extracts, from the reference image data displayed in the selected image list 206, reference image data that belongs to the same category displayed in the optimal image evaluation value map 211 and has a combination of image evaluation values ​​closest to the combination of image evaluation values ​​held by the pixel selected in the optimal image evaluation value map 211 (closest distance from the selected pixel: smallest difference in evaluation values). Note that the optimal image evaluation value map 211 may display not only plots (classifications) of image evaluation values ​​corresponding to the reference image data, but also plots (classifications) of image evaluation values ​​between different reference image data.

[0041] (ii) Step S602: The control unit 111 applies image processing to the extracted reference image data so that the combination of image evaluation values ​​held by the selected pixels approaches that of the selected pixel. For example, if the SNR in the extracted reference image data is better than the desired SNR, image processing is performed to add noise, so that the combination approaches the desired evaluation value.

[0042] The control unit 111 performs repeated image processing on the extracted closest image data, including step S602 and subsequent steps S603 and S604, to achieve processing that approaches the selected conditions. The control unit 111 executes this repeated processing, that is, processing (step S602) to generate image data that is close to the selected conditions. Finally, the control unit 111 generates and displays a predicted SEM image in step S605.

[0043] More specifically, the control unit 111 calculates the error as the difference between each image evaluation value of the reference image data extracted in step S601 and each image evaluation value held by the selected pixel. The control unit 111 selects image processing to be applied to the reference image data for each image evaluation value based on the sign of the error of each calculated image evaluation value. As an example, if the image evaluation value of the SNR held by the selected pixel is larger than the image evaluation value of the SNR of the extracted reference image data, the control unit 111 selects image processing that removes noise. Conversely, if the image evaluation value of the SNR held by the selected pixel is smaller than the image evaluation value of the SNR of the extracted reference image data, the control unit 111 selects image processing that increases noise. After selecting image processing for each image evaluation value, the control unit 111 executes the selected image processing on the extracted reference image data. In this way, multiple image data that have been subjected to image processing are generated so that each image can be compared.

[0044] (iii) Step S603 The control unit 111 calculates each image evaluation value of the image data that has been subjected to image processing in step S603, and re-calculates the difference between this and the combination of image evaluation values ​​held by the pixel selected in the optimal image evaluation value map 211 as an error.

[0045] (iv) Step S604: The control unit 111 repeatedly applies image processing to the extracted reference image data until the error is minimized. The determination of whether the error is minimal is made by comparing the error with the previous iteration, and if the error begins to increase, by setting the image data from the previous iteration as the condition for the minimum error. If the difference is determined to be minimal (YES in step S604), the process proceeds to step S605. On the other hand, if the difference is determined not to be minimal (NO in step S604), the process proceeds to step S602.

[0046] (v) Step S605 The control unit 111 displays the reference image data that has been subjected to image processing when the error (difference) is minimized in step S604 as a predicted SEM image corresponding to the image evaluation value of the selected condition in the display area of ​​the predicted SEM image 215 of the selected condition.

[0047] In the first embodiment, a method was described in which each numerical value in the combination of optimal image evaluation values ​​selected in the optimal image evaluation value map 211 was displayed in the selected evaluation value display 213. On the other hand, in the second embodiment, a method is described in which the magnitude relationship of each image evaluation value between the sections written in the optimal image evaluation value map 211 can be easily compared by displaying it in a slider format. Note that, since some of the configurations and functions described in the first embodiment can be applied to the second embodiment, the same reference numerals are used for similar configurations and functions, and their description will be omitted.

[0048] Fig. 7 is a diagram showing an example of the configuration of the window 202 according to the second embodiment. The window 202 according to the second embodiment has a slider display function for each image evaluation value. In Fig. 7, the selected evaluation value (slider) 713 is configured so that by changing the position of the slider, it is possible to change the combination of image evaluation values ​​of the pixel selected in the optimal image evaluation value map 211. The sliders are displayed for the number of types of evaluation values ​​selected in the optimization evaluation value 208, and one slider is displayed for each type of evaluation value.

[0049] For the sections marked in the optimal image evaluation value map 211, a representative combination of image evaluation values ​​for each section is extracted and displayed in the selected evaluation value 713. By comparing the extracted representative combinations of image evaluation values, it is possible to calculate the ranking of each evaluation value type among all sections. By displaying the magnitude of this ranking using a slider, it is possible to see at a glance how large each image evaluation value is among all sections. At this time, for the combination of image evaluation values ​​of the pixel selected in the optimal image evaluation value map 211, the numerical value of each image evaluation value is displayed in the upper right corner of the predicted SEM image 215 for the selected conditions.

[0050] Furthermore, by moving the slider for one of the evaluation values ​​in the selected evaluation value 713, a new pixel can be selected on the optimal image evaluation value map 211. At this time, the slider for the evaluation value that was not moved is configured to move to a position indicating the image evaluation value of the newly selected pixel. On the other hand, when the slider is moved, the selected point on the optimal image evaluation value map 211 also moves.

[0051] Furthermore, the viewing condition selected in the corresponding viewing condition map 212 is displayed so as to select the viewing condition corresponding to the newly selected pixel on the optimal image evaluation value map 211. The value of the viewing condition displayed in the selected viewing condition displays the value corresponding to the newly selected pixel on the corresponding viewing condition map 212. summary

[0052] (i) According to an example of this embodiment, the control unit (processor) 111 acquires multiple reference image data corresponding to multiple observation conditions (see FIG. 5 ) for searching for optimized observation conditions, calculates an optimal image evaluation value map 211 that displays multiple sections corresponding to the Pareto-optimal image evaluation values ​​for the multiple reference image data, and calculates the observation conditions corresponding to the multiple sections, and displays a corresponding observation condition map 212 for each observation condition in the same display format as the optimal image evaluation value map 211. When a Pareto-optimal condition is selected from the optimal image evaluation value map 211, the control unit 111 displays locations that match the selected condition in multiple corresponding observation condition maps, and displays selected observation conditions 214, which show the numerical values ​​of each observation condition included in the selected Pareto-optimal condition. When acquiring the multiple reference image data, the control unit 111 executes a design of experiments based on the range of search observation conditions that has been input and set externally (by the user). By comparing the optimal image evaluation value map 211 with the corresponding observation condition map 212, which plots the observation conditions for each data at positions corresponding to the evaluation value data, the user can see at a glance the trends in the image evaluation values ​​and the corresponding trends in each observation condition.

[0053] (ii) The control unit 111 further calculates at least one image evaluation value for each of the multiple reference image data, and estimates a combination of image evaluation values ​​for a combination of observation conditions within the range of the optimized observation conditions and the search observation conditions, using the at least one image evaluation value as reference standard data. The control unit 111 then calculates a response surface for the estimated combination of image evaluation values, and uses the response surface to extract a Pareto-optimal combination of image evaluation values ​​and a corresponding combination of observation conditions. This makes it possible to determine a combination of observation conditions that is balanced for each evaluation value.

[0054] (iii) When the user selects one image evaluation value in the optimal image evaluation value map 211, the control unit 111 displays (e.g., highlights) the pixel with the selected image evaluation value so that it can be distinguished from other pixels. The control unit 111 also displays the selected combination of image evaluation values ​​as the selected evaluation value display 213. Furthermore, when the user changes the pixel with the image evaluation value selected by the user on the optimal image evaluation value map 211, the control unit 111 changes the combination of image evaluation values ​​in the selected evaluation value display 213 in conjunction with this. In this way, the user can easily grasp the tendency of the evaluation values ​​that they are actually selecting and their specific numerical values.

[0055] (iv) When a Pareto-optimal condition is selected from the optimal image evaluation value map 211, if the control unit 111 determines that the reference image data includes reference image data having a combination of evaluation values ​​corresponding to the selected Pareto-optimal condition, the control unit 111 displays the corresponding reference image data as a predicted image (predicted SEM image). On the other hand, when a Pareto-optimal condition is selected from the optimal image evaluation value map 211 and the control unit 111 determines that the reference image data does not include reference image data having a combination of evaluation values ​​corresponding to the selected Pareto-optimal condition, the control unit 111 performs predetermined image processing on the reference image having the closest evaluation value among the acquired reference image data belonging to the same Pareto-optimal condition category as the combination of evaluation values ​​corresponding to the selected Pareto-optimal condition, generates an image corresponding to the combination of evaluation values ​​corresponding to the selected Pareto-optimal condition as a predicted image, and displays the predicted image (see FIG. 6 ). If an optimal image is found among the reference images, the control unit 111 displays the optimal image as a predicted image (predicted SEM image 215 for the selected condition), thereby efficiently presenting the predicted image. Furthermore, if there is no optimal image among the reference images, the reference image that is closest to the desired conditions is lowered (predetermined image processing) to efficiently generate a reasonable predicted image that meets the desired conditions.

[0056] (v) The control unit 111 provides the corresponding viewing condition map 212 with segment displays that are the same as the multiple segments of the optimal image evaluation value map 211, and reflects the segment displays within the map. This makes it easy to compare the optimal image evaluation value map 211 with the corresponding viewing condition map 212, and makes it possible to confirm the tendency of the optimal combination of image evaluation values ​​for the viewing conditions.

[0057] (vi) When a Pareto-optimal condition is selected in the optimal image evaluation value map 211, the control unit 111 displays a selected evaluation value display area (selected evaluation value 713) that displays an evaluation value corresponding to the selected Pareto-optimal condition as a slider (see Example 2: FIG. 7). Furthermore, when the user operates (changes) one of the evaluation values ​​in the slider display, the control unit 111, in conjunction with the operation, moves the position of the pixel of the selected evaluation value in the optimal image evaluation value map 211 to a pixel corresponding to the destination of the evaluation value in the slider display, and displays (e.g., highlights) the pixel of the selected evaluation value. This allows the user to easily understand the operation of the evaluation value and the impact of the operation.

[0058] (vii) The functions of this embodiment and each example can also be realized by software program code. In this case, a storage medium on which the program code is recorded is provided to a system or device, and the computer (or CPU or MPU) of the system or device reads the program code stored in the storage medium. In this case, the program code read from the storage medium itself realizes the functions of the above-mentioned embodiments, and the program code itself and the storage medium on which it is stored constitute the present disclosure. Examples of storage media for providing such program code include flexible disks, CD-ROMs, DVD-ROMs, hard disks, optical disks, magneto-optical disks, CD-Rs, magnetic tape, non-volatile memory cards, and ROMs.

[0059] Furthermore, an operating system (OS) running on a computer may perform some or all of the actual processing based on instructions in the program code, and the functions of the above-described embodiments may be realized by this processing.Furthermore, after the program code is read from a storage medium and written to memory on the computer, a CPU of the computer may perform some or all of the actual processing based on instructions in the program code, and the functions of the above-described embodiments may be realized by this processing.

[0060] Furthermore, the program code of the software that realizes the functions of the embodiments and each example may be distributed via a network and stored in a storage means such as a hard disk or memory of the system or device, or in a storage medium such as a CD-RW or CD-R, so that when used, the computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage means or storage medium.

[0061] The processes and techniques described herein are not inherently related to any specific device and can be implemented by a combination of components. Various types of general-purpose devices can also be added. A dedicated device may be constructed to perform the functions of this embodiment and each example. Various functions can also be formed by appropriately combining multiple components disclosed in this embodiment and each example. For example, some components may be omitted from all the components shown in the embodiment and each example, or components from different examples may be appropriately combined.

[0062] Although specific examples are described in this disclosure, they are in all respects for the purpose of explanation (understanding the technology of the present disclosure) and not for the purpose of limitation. Those skilled in the art will recognize that there are many combinations of hardware, software, and firmware suitable for implementing the technology of the present disclosure. For example, the software described can be implemented in a wide variety of programming or scripting languages, such as assembler, C / C++, Perl, Shell, PHP, Java (registered trademark), etc.

[0063] Furthermore, in the above-described embodiment, the control lines and information lines are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. All components may be interconnected.

[0064] In addition, other implementations of the present disclosure will be apparent to those skilled in the art from consideration of the present embodiments and examples. The specification and examples are exemplary only, with the scope and spirit of the present disclosure being indicated by the following claims.

[0065] REFERENCE SIGNS LIST 1 Scanning electron microscope 100 Sample 101 Electron source 102 Focusing lens 103 Deflector 104 Objective lens 105 Sample holder 106 Sample stage 107 Detector 111 Control unit 112 Input unit 113 Display unit 114 Irradiation control unit 115 Condition adjustment unit 201 Window (condition optimization setting screen) 202 Window (map etc. display screen) 203 Optimized observation conditions and search range 204 Condition selection button 205 Required measurement point display button 206 Selected image list 207 Image selection button 208 Optimization evaluation value 209 Evaluation value selection button 210 Optimal condition search execution button 211 Optimal image evaluation value map 212 Corresponding observation condition map 213 Selected evaluation value display 214 Selected observation conditions 215 Predicted SEM image of selected conditions 401 Condition selection screen 402 Setting button 501 Required measurement point list screen 713 Selected evaluation value (slider)

Claims

1. A charged particle beam device comprising: an irradiation control unit that controls the charged particle beam in accordance with the observation conditions when irradiating the sample with the charged particle beam to observe the sample; a storage device that stores a condition adjustment program that adjusts the relationship between the observation conditions and a plurality of evaluation conditions based on evaluation values ​​that evaluate images; and a processor that reads and executes the condition adjustment program from the storage device, wherein the processor performs the following processes: acquiring a plurality of reference image data corresponding to a plurality of observation conditions for searching for optimized observation conditions; displaying an optimal image evaluation value map that displays a plurality of sections corresponding to the Pareto-optimal image evaluation values ​​for the plurality of reference image data; calculating observation conditions corresponding to the plurality of sections and displaying a corresponding observation condition map in the same display format as the optimal image evaluation value map for each observation condition; when a Pareto-optimal condition is selected from the optimal image evaluation value map, displaying locations that match the selected condition on a plurality of the corresponding observation condition maps; and displaying the numerical values ​​of each observation condition included in the selected Pareto-optimal condition as a selected observation condition display.

2. A charged particle beam device according to claim 1, wherein the processor executes an experimental design based on a range of search and observation conditions input and set from the outside, and acquires the plurality of reference image data.

3. A charged particle beam device according to claim 2, wherein the processor further executes the following processes: calculating at least one image evaluation value for the plurality of reference image data; estimating a combination of the image evaluation values ​​for a combination of observation conditions within the range of the optimized observation conditions and the search observation conditions, using the at least one image evaluation value as reference standard data; and determining a response surface of the estimated combination of image evaluation values, and extracting the Pareto-optimal combination of image evaluation values ​​and the corresponding combination of observation conditions using the response surface.

4. A charged particle beam device according to claim 1, wherein when one image evaluation value is selected in the optimal image evaluation value map, the processor executes a process of displaying the pixel having the selected image evaluation value so that it can be distinguished from other pixels.

5. A charged particle beam device according to claim 4, wherein the processor executes a process of displaying the selected combination of image evaluation values ​​in a selected evaluation value display area.

6. A charged particle beam device according to claim 5, wherein the processor executes a process for changing the combination of image evaluation values ​​to be displayed in the selected evaluation value display area in conjunction with a change in the pixel of the selected image evaluation value.

7. A charged particle beam device as claimed in claim 1, wherein the processor further executes a process of displaying the corresponding reference image data as a predicted image when the Pareto optimal condition is selected from the optimal image evaluation value map and it is determined that the reference image data having the combination of evaluation values ​​corresponding to the selected Pareto optimal condition is included among the plurality of reference image data.

8. A charged particle beam device according to claim 1, wherein the processor further, when it determines that, when the Pareto optimal condition is selected from the optimal image evaluation value map, the reference image data having the combination of evaluation values ​​corresponding to the selected Pareto optimal condition is not included among the plurality of reference image data, performs a predetermined image processing on the reference image having the closest evaluation value among the acquired reference image data belonging to the same Pareto optimal condition category as the combination of evaluation values ​​corresponding to the selected Pareto optimal condition, generates an image corresponding to the combination of evaluation values ​​corresponding to the selected Pareto optimal condition as a predicted image, and executes processing to display the predicted image.

9. A charged particle beam device as claimed in claim 1, wherein the processor provides a division display in the corresponding observation condition map that is the same as the division in the optimal image evaluation value map, reflects the division display in the map, and executes processing to display the corresponding observation condition map.

10. A charged particle beam device as claimed in claim 9, wherein the processor displays a legend indicating the values ​​of the selected observation conditions in the corresponding observation condition map, and displays the magnitude of the conditions corresponding to the legend in the corresponding observation condition map using a gradation or stepped size division, and displays the location that matches the condition selected in the optimal image evaluation value map as a selected position in the map.

11. A charged particle beam device as claimed in claim 1, wherein the processor, when the Pareto optimum condition is selected in the optimum image evaluation value map, displays a selected evaluation value display area that displays the evaluation value corresponding to the selected Pareto optimum condition using a slider.

12. A charged particle beam device as claimed in claim 11, wherein when any of the evaluation values ​​on the slider display is operated, the processor, in response to the operation, moves the position of the pixel of the selected evaluation value in the optimal image evaluation value map to a pixel corresponding to the destination of the evaluation value on the slider display, thereby displaying the pixel of the selected evaluation value.

13. A method for adjusting image evaluation conditions in which a processor reads and executes a condition adjustment program from a storage device to adjust the relationship between multiple conditions, namely, the observation conditions and the evaluation conditions based on evaluation values ​​for evaluating images, wherein the processor: acquires multiple reference image data corresponding to multiple observation conditions for searching for optimized observation conditions; displays an optimal image evaluation value map for the multiple reference image data, displaying multiple divisions according to the Pareto-optimal image evaluation values; calculates observation conditions corresponding to the multiple divisions, and displays a corresponding observation condition map for each observation condition in the same display format as the optimal image evaluation value map; when a Pareto-optimal condition is selected from the optimal image evaluation value map, displays locations that match the selected condition on the multiple corresponding observation condition maps; and displays the numerical values ​​of each observation condition included in the selected Pareto-optimal condition as a selected observation condition display.

Images