WO2025022875A1 - 画像処理方法、プログラム、画像処理装置、および走査型プローブ顕微鏡 - Google Patents

画像処理方法、プログラム、画像処理装置、および走査型プローブ顕微鏡 Download PDF

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WO2025022875A1
WO2025022875A1 PCT/JP2024/021980 JP2024021980W WO2025022875A1 WO 2025022875 A1 WO2025022875 A1 WO 2025022875A1 JP 2024021980 W JP2024021980 W JP 2024021980W WO 2025022875 A1 WO2025022875 A1 WO 2025022875A1
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image
sample
corrected
image processing
correction
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English (en)
French (fr)
Japanese (ja)
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啓 阿久津
隆二 澤田
健太 足立
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Shimadzu Corp
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Shimadzu Corp
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Priority to CN202480058982.1A priority patent/CN121844213A/zh
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
    • G01Q30/00—Auxiliary means serving to assist or improve the scanning probe techniques or apparatus, e.g. display or data processing devices
    • G01Q30/04—Display or data processing devices
    • G01Q30/06—Display or data processing devices for error compensation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
    • G01Q60/00—Particular types of SPM [Scanning Probe Microscopy] or microscopes; Essential components thereof
    • G01Q60/10—STM [Scanning Tunnelling Microscopy] or apparatus therefor, e.g. STM probes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01Q—SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS OF SCANNING-PROBE TECHNIQUES, e.g. SCANNING PROBE MICROSCOPY [SPM]
    • G01Q60/00—Particular types of SPM [Scanning Probe Microscopy] or microscopes; Essential components thereof
    • G01Q60/24—AFM [Atomic Force Microscopy] or apparatus therefor, e.g. AFM probes

Definitions

  • This disclosure relates to an image processing method, a program, an image processing device, and a scanning probe microscope, and more specifically to the processing of images obtained by measurements using a scanning probe microscope.
  • a scanning probe microscope observes the unevenness of a sample surface with high resolution by bringing a sharpened probe close enough to the sample to be observed and scanning the sample surface horizontally with the probe while raising and lowering the height of the probe so that the physical quantity acting on the tip of the probe and the surface of the sample is constant.
  • SPM is a general term for microscopes that observe the unevenness of a sample surface using the above operating principle.
  • Representative SPMs include the scanning tunneling microscope (STM), which detects the current flowing between the probe and the sample as an interaction, and the atomic force microscope (AFM), which detects the atomic force acting between the probe and the sample as an interaction.
  • the resolution in the surface height direction is high, and it is difficult to set the sample surface horizontally at that level of resolution. Therefore, it is common to perform tilt correction on the measurement data acquired by the SPM so that the inclined surface is corrected to be horizontal. By being provided with an image created based on the corrected measurement data, the user can more accurately recognize the surface condition of the sample.
  • Patent Document 1 discloses a technique for extracting at least a portion of the area other than the edges in the image data as a reference plane area, and correcting the height of the measurement data based on the height information of three points belonging to the reference plane area.
  • Patent Document 2 discloses a technique for correcting errors in the measurement data caused by changes in the measurement environment by making the average height of all main scan lines in the image data equal.
  • Patent Document 3 discloses a technique for obtaining measurement results with less distortion by correcting the measurement data based on a histogram of the height distribution obtained from the measurement data.
  • Patent No. 6627903 JP 2009-58479 A Japanese Patent Application Publication No. 11-142416
  • the gradation values of pixels in areas corresponding to the unevenness of the sample surface are averaged with the gradation values of pixels in other areas of the sample, and it may not be possible to measure the height of the surface structure of the sample relative to the substrate surface with sufficient accuracy from the corrected image.
  • the information on the surface structure of the sample obtained from the corrected image may deviate from the actual surface structure of the sample. Therefore, there is a need for a correction processing method that can measure the height of the surface structure of the sample relative to the substrate surface with sufficient accuracy in images of SPM measurement data acquired with the sample tilted.
  • the present disclosure has been devised in light of the above-mentioned circumstances, and its purpose is to provide a correction processing technique for improving the accuracy of images generated based on SPM measurements.
  • a first aspect of the present disclosure is a method for processing an object image generated based on measurement of a sample using a scanning probe microscope, comprising the steps of: estimating a first background image of the object image generated by the inclination between a plane on which the sample is placed and a specified plane during measurement using an algorithm that calculates a regression line by removing outliers from data that includes outliers; and generating a first corrected image in which the inclination of the object image is corrected based on the first background image.
  • a second aspect of the present disclosure is a program that, when executed by one or more processors, causes the one or more processors to implement the image processing method described in the first aspect.
  • a third aspect of the present disclosure is an image processing device comprising one or more processors and a storage device storing a program that, when executed by the one or more processors, causes the one or more processors to implement the image processing method described in the first aspect.
  • a fourth aspect of the present disclosure is a scanning probe microscope comprising: a sample stage on which a sample is placed; a cantilever having a probe at its tip; a moving device that changes the distance between the sample stage and the probe; an optical system that irradiates the cantilever with laser light and detects the laser light reflected by the cantilever; a measurement unit that measures the height of the sample based on the displacement of the cantilever obtained by the change in the position of the laser light detected by the optical system; an image generation unit that generates an object image based on the measurement; an image processing unit that executes a correction process for the object image; and a display unit that displays the corrected image in which the object image has been corrected by the image processing unit, the correction process including the steps of estimating a background image of the object image generated by the inclination between the plane on which the sample is placed and a specified plane during measurement using an algorithm that calculates a regression line by removing outliers from data including outliers; and generating a corrected image in which the
  • the present disclosure provides a correction processing technique for improving the accuracy of images generated based on SPM measurements.
  • 1 is a schematic configuration diagram of a scanning probe microscope according to an embodiment; 1 is an example of an image of a sample generated based on a scanning probe microscope measurement. 3 is an example of a first background image estimated based on the image shown in FIG. 2 . 3 is an example of a first corrected image obtained by performing a first correction process on the image shown in FIG. 2 . 11 is a flowchart of an example of a first correction process performed in the scanning probe microscope. 5 is an example of a first corrected image different from the first corrected image shown in FIG. 4 . 7 is an example of a second background image estimated based on the image shown in FIG. 6 . 7 is an example of a second corrected image obtained by performing a second correction process on the image shown in FIG. 6 . 13 is a flowchart of an example of a second correction process performed in the scanning probe microscope.
  • [Schematic configuration of a scanning probe microscope] 1 is a schematic diagram of a scanning probe microscope according to an embodiment.
  • An example of the scanning probe microscope is an AFM.
  • the scanning probe microscope may be another type of scanning probe microscope (for example, an STM).
  • the scanning probe microscope 1 includes a piezoelectric scanner 111, a sample stage 112, a cantilever 113, a displacement detection mechanism 120, a feedback signal generator 131, a computer 132, a scanning signal generator 133, a storage device 134, and a display unit 135.
  • the scanning probe microscope 1 measures the shape of the surface of a sample 110 placed on the sample stage 112.
  • the height direction of the sample 110 is the Z-axis direction
  • the directions perpendicular to the Z-axis direction are the X-axis direction and the Y-axis direction.
  • the sample 110 is placed on the sample stage 112.
  • the sample stage 112 is placed on the piezoelectric scanner 111.
  • the piezoelectric scanner 111 is a moving device for changing the relative positional relationship between the sample 110 and a probe 114 provided at the tip of a cantilever 113.
  • the piezoelectric scanner 111 includes an XY scanner 111xy and a Z scanner 111z.
  • the XY scanner 111xy has a piezoelectric element that deforms based on the voltage values Vx and Vy applied from a scanning signal generating unit 133.
  • the XY scanner 111xy moves the sample stage 112 in the X-axis direction and the Y-axis direction using this piezoelectric element.
  • the Z scanner 111z has a piezoelectric element that deforms based on the voltage value Vz applied from a feedback signal generating unit.
  • the Z scanner 111z moves the sample stage 112 in the Z-axis direction using this piezoelectric element. Note that the XY scanner 111xy and the Z scanner 111z are not limited to having piezoelectric elements.
  • the cantilever 113 is formed in the shape of a leaf spring.
  • the free end of the cantilever 113 is arranged to face the sample 110.
  • the cantilever 113 is arranged above the sample 110 in the Z-axis direction.
  • the cantilever 113 has a surface that faces the sample 110 and a back surface opposite the front surface.
  • a probe 114 is arranged on the surface of the tip of the free end of the cantilever 113 so as to face the sample 110.
  • the back surface of the tip is configured to reflect light.
  • the tip of the cantilever 113 is displaced in the Z-axis direction by a physical quantity (e.g., atomic force) acting between the probe 114 and the sample 110.
  • a physical quantity e.g., atomic force
  • the displacement detection mechanism 120 includes a laser diode 115 and a photodetector 116.
  • the displacement detection mechanism 120 detects the displacement of the cantilever 113.
  • the laser light emitted from the laser diode 115 is reflected by the back surface of the cantilever 113, and the reflected light is received by the photodetector 116.
  • the cantilever 113 bends like a leaf spring, changing the reflection direction of the reflected light. Therefore, the amount of bending of the cantilever 113 can be detected by observing the light receiving position of the photodetector 116.
  • the feedback signal generating unit 131 receives a detection signal from the photodetector 116, and calculates the amount of deflection of the cantilever 113 based on the detection signal.
  • the feedback signal generating unit 131 controls the Z-direction position of the sample 110 so that the atomic force between the probe 114 and the surface of the sample 110 is always constant.
  • the feedback signal generating unit 131 calculates a voltage value Vz of the control command value in the Z-axis direction for the piezo scanner 111 based on the amount of deflection of the cantilever 113, and outputs it to the Z scanner 111z.
  • the scanning signal generating unit 133 calculates the voltage values Vx and Vy of the control command values in the X-axis and Y-axis directions, respectively, so that the sample 110 moves relative to the probe 114 in the XY plane according to a predetermined scanning pattern, and outputs them to the XY scanner 111xy.
  • a signal reflecting the amount of feedback in the Z-axis direction (i.e., the voltage value Vz, which is the voltage applied to the scanner, and the deviation signal Sd) is also sent to the computer 132 and stored in the storage device 134.
  • the computer 132 calculates the amount of surface displacement due to the unevenness of the sample 110 from the voltage value Vz based on correlation information indicating the relationship between the voltage value Vz previously stored in the storage device 134 and the corresponding amount of surface displacement due to the unevenness of the sample 110.
  • the calculated amount of displacement is a value reflecting a value indicating the position of the sample 110 in the Z-axis direction.
  • the computer 132 creates measurement data representing the shape of the surface of the sample 110 by calculating the amount of displacement of the sample 110 in the Z-axis direction at each position in the X-axis and Y-axis directions in the scanning range.
  • the measurement data is matrix data of M ⁇ N pieces of height information (amount of displacement in the Z-axis direction) for a specified rectangular area on the surface of the sample 110.
  • the row direction and column direction in the recorded matrix data generally correspond to either the main scanning direction or the sub-scanning direction of the probe 114 of the scanning probe microscope 1.
  • the computer 132 causes the display unit 135 to display information such as the shape of the surface of the sample 110.
  • the shape of the surface of the sample 110 is displayed by an image in which the amount of displacement in the Z-axis direction is expressed by a gradation value.
  • Computer 132 includes at least one processor, and storage device 134 stores the program executed by the processor in a non-volatile (non-temporary) manner.
  • Computer 132 can read out the measurement data stored in storage device 134 at any time and display it on display unit 135. Furthermore, computer 132 can correct the measurement data as necessary and display it on display unit 135.
  • Computer 132 can perform at least two types of correction, a first correction and a second correction, for the image obtained by SPM measurement.
  • the first correction is a correction for reducing the effect on the gradation value due to the inclination between a predetermined plane and the plane on which the sample is actually placed during measurement with the SPM in the image obtained by SPM measurement.
  • the second correction is a correction for correcting the difference in gradation value between main scan lines in the image data obtained by the first correction.
  • the specified plane is assumed to be the plane on which the user wishes the sample 110 to be placed during SPM measurement.
  • it is a plane parallel to the surface of the sample stage 112.
  • the user is measuring the surface structure of the sample based on an image obtained by SPM measurement, it can also be a plane parallel to the flat substrate surface of the sample.
  • the sample 110 to be measured is, for example, a substrate on which a structure is placed.
  • a substrate is a mica plate.
  • An example of a structure is nanoparticles or nanofibers of a biological sample. Note that these are merely examples, and the samples that the scanning probe microscope 1 targets are not limited to these.
  • the structure is not limited to structures that are intentionally placed on a substrate, and the structure may refer to unevenness on a substrate.
  • the sample may not include a substrate, and may be nanoparticles and nanofibers.
  • the displacement in the Z-axis direction caused by the tilt of the sample may be greater than the unevenness of the sample's surface, and the user may not be able to correctly recognize the structure of the sample's surface from the original image.
  • a correction processing method for example, a method has been used in which a plane is estimated from three arbitrary points specified on the original image, and the original image is corrected based on the estimated plane.
  • the estimated plane differs depending on how the three points on the original image are selected, so even if the same original image is used for correction, the resulting image may differ depending on the correction process, and the reproducibility of the correction process may be low.
  • a correction process can also be implemented in which correction is automatically performed using the average or median of the gradation values of the pixels along the X-axis or Y-axis direction of the original image.
  • the pixels in the area corresponding to the structure on the sample and the area corresponding to the substrate surface are not distinguished and are used as data when calculating the average or median, and the gradation values originating from the structure on the sample and the gradation values originating from the substrate surface may be averaged. If the accuracy of the corrected image is not sufficient in this way, there is a risk that the information on the surface structure of the sample obtained from the corrected image will differ from the actual surface structure of the sample.
  • an algorithm is used to calculate a regression equation by removing outliers from data including outliers.
  • the algorithm estimates a background image (hereinafter referred to as a first background image) in the original image, which is generated by the inclination between a predetermined plane and a plane on which the sample is actually placed. Based on the estimated first background image and the original image, a first corrected image is created. In the first correction, the same correction process is performed on both original images, so that the reproducibility of the correction process is improved.
  • the pixels in the area corresponding to the structure on the sample in the original image are removed as outliers to estimate the background image, so that the gradation values derived from the structure on the sample and the gradation values derived from the substrate surface are prevented from being averaged during the correction process, and the accuracy of the image obtained after the correction process can be improved.
  • the user can recognize the surface state of the sample from the image after the first correction.
  • the original image may be referred to as a target image
  • the image after the first correction may be referred to as a first corrected image.
  • the first background image is an image corresponding to the variation in the gradation value of each pixel caused by the inclination between a predetermined plane in the original image and the plane on which the sample is actually placed, and the first background image allows the user to visually recognize how the predetermined plane in the original image is inclined relative to the plane on which the sample is actually placed.
  • the computer 132 estimates a first background image caused by the tilt between a specified plane and the plane on which the sample 110 is actually placed, and generates a first corrected image in which the tilt has been corrected based on the estimated first background image and the original image.
  • FIG. 2 is an example of an original image of a sample 110 generated based on the measurement of the scanning probe microscope 1.
  • the sample 110 corresponding to the image IM10 shown in FIG. 2 four structures P1 to P4 are arranged on a flat substrate.
  • the four structures are arranged two-dimensionally in two rows in the X-axis direction and two rows in the Y-axis direction, i.e., a 2x2 array.
  • An example of a substrate is a mica plate.
  • An example of a structure is a nanoparticle or nanofiber. Note that these are merely examples, and the samples (substrates, structures) that the scanning probe microscope 1 targets are not limited to these.
  • the gradation value of a pixel in image IM10 represents the height in the Z-axis direction on sample 110.
  • the substrate of sample 110 is flat, so in order for the user to recognize the structure of sample 110, it is desirable for the gradation values of the pixels corresponding to the substrate portion in image IM10 to be uniform.
  • the gradation values of the area corresponding to the substrate are non-uniform.
  • One reason for this is that the measurement was performed when the specified plane and the plane on which the sample was actually placed were tilted and not parallel to each other.
  • the first correction process will be explained using image IM10.
  • a first background image is estimated by the algorithm used in the first correction, based on the gradation values corresponding to each pixel of image IM10, which is the original image.
  • the first correction uses an algorithm that calculates a regression line by removing outliers from data that includes outliers.
  • algorithms that calculate a regression line by removing outliers from data that includes outliers include RANSAC (RANdom SAmple Consensus), Quantile regression, and TheilSen regression.
  • the algorithm used for the first correction is not limited to the above example, and may be any algorithm that calculates a regression line by removing outliers from data that includes outliers. In correction using this algorithm, the parameters used for correction are not changed for each correction process, so that the same processing is performed on all original images, improving the reproducibility of the correction process.
  • the gradation values of all pixels included in the first background image may be adjusted by adding, subtracting, multiplying, or dividing a predetermined value.
  • the gradation values may be adjusted so that the average or median of the overall gradation values becomes an appropriate value while maintaining the magnitude relationship of the gradation values of each pixel, or the maximum and minimum values of the pixels included in the image become appropriate values. Adjusting the gradation values improves the visibility of the image for the user.
  • FIG. 3 shows a first background image estimated based on image IM10.
  • Image IM20 shown in FIG. 3 is obtained in the process of performing a first correction process on image IM10.
  • the gradation value increases in the positive direction on the X-axis and Y-axis. Therefore, it can be inferred from image IM20 that when measuring the SPM, the sample 110 was placed so that the positive directions of the X-axis and Y-axis were lower in the height direction relative to a specified plane.
  • the algorithm used for the first correction can improve the accuracy of the image after the first correction by adjusting variables according to the characteristics of the sample.
  • the characteristics of the sample are, for example, the ratio of the structures to the substrate surface in the sample, the particle diameter size, and the height of the structures to the substrate surface.
  • the variables of the algorithm include, for example, the number of trials, the residual threshold, and the order of the plane. The preferred values of the above variables are determined by theory and/or experiment.
  • the number of trials of the algorithm used for the first correction is the number of times random sampling is repeated, and is, for example, 3000 times.
  • the number of trials may be more or less than 3000.
  • increasing the number of trials improves the accuracy of the image after the first correction, but the amount of calculations increases and the processing speed may slow down.
  • decreasing the number of trials reduces the amount of calculations and can speed up the processing speed, but the accuracy of the image after the first correction may decrease. Therefore, the user selects the number of trials as appropriate based on the relationship between the image accuracy desired by the user and the processing time required for correction.
  • the residual threshold of the algorithm used for the first correction indicates the maximum residual of a data sample to be classified as normal, and a larger value increases the range of samples that the algorithm accepts as normal.
  • the residual threshold is, for example, 10, but can be greater or less than 10.
  • the order of the plane of the algorithm used for the first correction is user-variable and may be, for example, 2 or 3.
  • the order represents the order of the function to be fitted and may be a value greater than 1 or 3.
  • a first corrected image is generated, which is an image obtained by correcting the original image based on the first background image estimated by the algorithm used for the first correction. Specifically, the tone value of the pixel at the same coordinates in the matrix as the pixel in the original image is subtracted from the tone value of the pixel in the original image to obtain the tone value of the pixel at that coordinate in the first corrected image. Alternatively, the tone value of the pixel at that coordinate in the first corrected image may be obtained by multiplying the value obtained by dividing the tone value of the pixel in the original image by the tone value of the pixel in the first background image at the same coordinates in the matrix as the pixel in question, and multiplying the result by a predetermined value.
  • the tone values of all pixels included in the image after the first correction may be adjusted by adding, subtracting, multiplying, or dividing a predetermined value.
  • the tone values may be adjusted so that the average or median of all tone values becomes an appropriate value while maintaining the magnitude relationship of the tone values of each pixel, or the maximum and minimum values of the pixels included in the image become appropriate values. Adjusting the tone values improves the visibility of the image for the user.
  • FIG. 4 shows an image after the first correction.
  • the non-uniformity of the gradation values of the pixels in the area corresponding to the substrate surface in the image IM10 in FIG. 2 has been reduced.
  • the gradation value of a pixel in image IM30 is obtained, for example, by subtracting the gradation value of a pixel in image IM20 that corresponds to the same coordinates from the gradation value of a pixel in image IM10 that has the same coordinates as the pixel in question.
  • FIG. 5 is a diagram showing a flowchart of an example of a process performed for a first correction of an image obtained by measurement by the scanning probe microscope 1.
  • the process in Fig. 5 is performed by the processor of the computer 132 executing a given program.
  • the scanning probe microscope 1 is an example of an image processing device.
  • the scanning probe microscope 1 may receive from the user specifications regarding the number of trials, the residual threshold, and the order of the plane in the algorithm used for the first correction.
  • step S10 the scanning probe microscope 1 measures the sample 110 and acquires a target image.
  • An example of the image acquired here corresponds to image IM10 in FIG. 2.
  • the original image, which is the observation result, is an example of a "target image.”
  • step S12 the scanning probe microscope 1 estimates a first background image based on the target image using an algorithm that calculates a regression line from data that includes outliers and removes the outliers.
  • the first background image may be estimated using some of the pixels included in the target image.
  • step S14 the scanning probe microscope 1 generates a first corrected image based on the target image acquired in step S10 and the first background image estimated in step S12.
  • An example of the first corrected image generated here corresponds to image IM30 in FIG. 4.
  • the image after the first correction is an example of the "first corrected image.”
  • step S16 the scanning probe microscope 1 displays the first corrected image generated in step S14 on the display unit 135. After that, the scanning probe microscope 1 ends the process of FIG. 5.
  • the scanning probe microscope 1 may display the estimated first background image on the display unit 135.
  • the above-mentioned correction process adjusts the deviation in the pixel gradation values of the original image that occurs when the specified plane and the plane on which the sample 110 is actually placed are not parallel, improving the accuracy of the image obtained compared to the accuracy of the image after the correction process in the comparative example, and improving the accuracy of the height information of the structure on the substrate obtained from the corrected image.
  • a second correction which is a correction different from the first correction, is performed on the image after the first correction, so that the user can recognize the surface state of the sample 110 more accurately.
  • the second correction is a correction such that the representative value (for example, the average value) of the gradation values of the pixels in each row that coincides with the main scanning direction of the probe 114 in the image after the first correction is the same for each row.
  • the second correction can reduce noise along the main scanning direction in the image after the first correction.
  • FIG. 6 is a diagram showing an example of an image after the first correction, which differs from image IM30 shown in FIG. 4.
  • image IM40 in FIG. 6 stripes appear along the X-axis direction.
  • the stripes are noise that appears along the main scanning direction of probe 114, and no structure corresponding to the stripes actually exists on sample 110. Details of the second correction will be explained below using image IM40 shown in FIG. 6 as an example. Note that in the following explanation, the main scanning direction of probe 114 when measuring sample 110 with scanning probe microscope 1 corresponds to the X-axis direction, and the sub-scanning direction corresponds to the Y-axis direction.
  • computer 132 performs a second correction on image IM40, which is the image after the first correction.
  • the image after the first correction is data in which pixels are arranged in the X-axis direction and the Y-axis direction in a matrix, with each pixel having a gradation value as height information.
  • a representative value is calculated for each row that coincides with the main scanning direction of the probe 114.
  • the average value of the gradation values of the pixels in each row is taken as the representative value along the X-axis direction, which is the scanning direction of the probe 114.
  • the representative value is replaced with the gradation values of all the pixels in each row to form the second background image.
  • the method for calculating the representative value is not limited to the average value, and for example, any one of the median, quartile, and mode of the gradation values may be used.
  • FIG. 7 shows image IM40, which is the second background image.
  • image IM50 in FIG. 7 the average value of each row in IM40 is used as the representative value, and the gradation values of all pixels in each row are replaced with the representative value of the pixels in that row.
  • all of the pixels in each row may be used, or only a portion of the pixels may be used. Furthermore, when only a portion of the pixels are used, the pixels in each row may be regularly sampled and used in the calculation, or they may be randomly sampled and used in the calculation.
  • the first corrected image is corrected based on the second background image to generate a second corrected image in which stripe noise occurring along the main scanning direction of the probe 114 has been corrected.
  • the tone value of a pixel in the first corrected image is subtracted from the tone value of the pixel in the second background image that is located at the same coordinates as the pixel in question on the matrix to obtain the tone value of the pixel at that coordinate in the second corrected image.
  • the tone value of the pixel at that coordinate in the second corrected image may be obtained by multiplying the value obtained by dividing the tone value of the pixel in the first corrected image by the tone value of the pixel in the second background image that is located at the same coordinates as the pixel in question on the matrix, and multiplying the result by a predetermined value.
  • the second corrected image may be referred to as the second corrected image.
  • the tone values of all pixels included in the image after the second correction may be adjusted by adding, subtracting, multiplying, or dividing a predetermined value.
  • the tone values may be adjusted so that the average or median of all tone values becomes an appropriate value while maintaining the magnitude relationship of the tone values of each pixel, or the maximum and minimum values of the pixels included in the image become appropriate values. Adjusting the tone values improves the visibility of the image for the user.
  • FIG. 8 shows an image after the second correction.
  • the stripe noise in the image IM40 in FIG. 6 has been reduced.
  • image IM60 is obtained by, for example, subtracting the gradation value of each pixel of image IM50 that corresponds to each pixel in image IM40 from the gradation value of each pixel in image IM40, and making adjustments.
  • Fig. 9 is a flowchart of an example of a process for performing the second correction on the first corrected image.
  • the process of Fig. 9 is performed by the processor of the computer 132 executing a given program.
  • the scanning probe microscope 1 is an example of an image processing device.
  • the second correction is performed on an image that has been subjected to the first correction process. Therefore, the steps in the middle of the first correction process in Fig. 9 (steps S10 to S16) are the same as the first correction process described above, and therefore a description thereof will be omitted.
  • step S20 the scanning probe microscope 1 calculates a representative value based on the first corrected image generated in step S14, and estimates a second background image.
  • step S22 the scanning probe microscope 1 generates a second corrected image based on the second background image estimated in step S20 and the first corrected image generated in step S14.
  • An example of the image generated here corresponds to image IM50 in FIG. 8.
  • the second corrected image is an example of a "second corrected image.”
  • step S24 the scanning probe microscope 1 displays the second corrected image generated in step S22 on the display unit 135. After that, the scanning probe microscope 1 ends the process of FIG. 9.
  • the user may decide whether to execute the processes from step S20 onwards based on the first correction image displayed on the display unit 135 in step S16.
  • the process may proceed to step S20 without performing the display process of step S16.
  • the scanning probe microscope 1 may appropriately display the first background image estimated in step S12 and/or the second background image estimated in step S20 on the display unit 135.
  • An image processing method is a method for processing an object image generated based on measurement of a sample using a scanning probe microscope, and may include a step of estimating a first background image of the object image generated by the inclination between a plane on which the sample is placed and a specified plane during the measurement using an algorithm that calculates a regression line by removing outliers from data that includes outliers, and a step of generating a first corrected image in which the inclination of the object image is corrected based on the first background image.
  • the image processing method described in paragraph 1 improves the accuracy of images obtained by measurements using a scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • the algorithm may include at least one of RANSAC (Random sample consensus), Quantile regression, and TheilSen regression.
  • the image processing method described in paragraph 2 improves the accuracy of images obtained by measurements using a scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • the image processing method described in paragraph 3 improves the accuracy of images obtained by measurements using a scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • the image processing method described in any one of paragraphs 1 to 3 may further include a step of accepting an order in the algorithm from a user.
  • the image processing method described in paragraph 4 improves the accuracy of images obtained by measurements using a scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • the outlier may be a gradation value of a pixel in the target image that corresponds to at least one of the sample, a structure on the sample, and a particle on the sample.
  • the image processing method described in paragraph 5 improves the accuracy of images obtained by measurements using a scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • the target image may be an image obtained based on the operation of a probe in the scanning probe microscope, which consists of main scanning and sub-scanning, and the method may further include a step of estimating a second background image, which is light and dark noise generated along the main scanning direction of the probe, in the first corrected image, and a step of generating a second corrected image in which the noise has been corrected based on the second background image.
  • the image processing method described in paragraph 6 makes it possible to correct the light and dark noise that occurs along the main scanning direction in the image obtained after correcting the variation in gradation value caused by the tilt of the sample during measurement with a scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • the step of estimating the second background image includes a step of calculating a representative value of the gradation values of pixels included in a predetermined line of the main scan from the first corrected image, and a step of setting the representative value as the gradation value of pixels included in the predetermined line in the second background image, and the representative value may be any one of the mean, median, quartile, and mode.
  • the image processing method described in paragraph 7 makes it possible to correct the light and dark noise that occurs along the main scanning direction in the image obtained after correcting the variation in gradation value caused by the tilt of the sample during measurement with a scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • the program may be executed by one or more processors to cause the one or more processors to implement the image processing method described in any one of clauses 1 to 7.
  • the program described in paragraph 8 improves the accuracy of images obtained by measurements using a scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • An image processing device may include one or more processors and a storage device that stores a program that, when executed by the one or more processors, causes the one or more processors to implement the image processing method described in any one of clauses 1 to 7.
  • the image processing device described in paragraph 9 improves the accuracy of images obtained by measurements using a scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • a scanning probe microscope includes a sample stage on which a sample is placed, a cantilever having a probe at its tip, a moving device that changes the distance between the sample stage and the probe, an optical system that irradiates the cantilever with laser light and detects the laser light reflected by the cantilever, a measurement unit that measures the height of the sample based on the displacement of the cantilever obtained by the change in position of the laser light detected by the optical system, an image generation unit that generates an object image based on the measurement, an image processing unit that executes a correction process for the object image, and a display unit that displays the corrected image in which the object image has been corrected by the image processing unit, and the correction process may include a step of estimating a background image of the object image generated by the inclination between the plane on which the sample is placed and a specified plane during measurement by an algorithm that calculates a regression line by removing outliers from data including outliers, and a step of generating the corrected image in which the inclin
  • the scanning probe microscope described in paragraph 10 improves the accuracy of images obtained by measurements using the scanning probe microscope, allowing the user to more clearly recognize the structure of the sample surface.
  • 1 scanning probe microscope 110 sample, 111 piezo scanner, 111xy XY scanner, 111z Z scanner, 112 sample stage, 113 cantilever, 114 probe, 115 laser diode, 116 photodetector, 120 displacement detection mechanism, 131 feedback signal generator, 132 computer, 133 scanning signal generator, 134 storage device, 135 display.

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JPH06147821A (ja) * 1992-11-12 1994-05-27 Hitachi Constr Mach Co Ltd 走査型探針顕微鏡像の傾斜補正方法
JP2000230823A (ja) * 1999-02-09 2000-08-22 Olympus Optical Co Ltd 分子測長方法
JP2002092586A (ja) * 2000-09-14 2002-03-29 Olympus Optical Co Ltd 画像解析装置、画像解析方法および画像解析処理をするための処理プログラムを記録した記録媒体
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