KR101349447B1 - Method for measuring surface roughness of deposition film and apparatus thereof - Google Patents

Abstract

The present invention relates to a method and an apparatus for measuring the surface roughness of a deposited film. The method for measuring the surface roughness of a deposited film according to the present invention comprises the following steps of: receiving surface images of a deposited film taken by an optical microscope, wherein the optical microscope takes each surface image when each source power is applied to the optical microscope; obtaining cumulative distributions of the number of particles which indicate the cumulative number of particles at each grayscale value using the number of pixels corresponding to each grayscale value in the image; searching for a grayscale value just before the grayscale value at which each of the cumulative number of particles in more than two distributions, among the cumulative distributions of the number of particles for each image, overlap for the first time; and obtaining the cumulative number of particles of each image at the searched grayscale value, and calculating the surface roughness of the deposited film for each image. The method and the apparatus for measuring the surface roughness of a deposited film according to the present invention can easily measure characteristics of the surface roughness by using the characteristics of the number of particles in a deposited film image taken by an optical microscope. [Reference numerals] (AA) Start; (BB) End; (S310) Receive each surface image of a deposited film taken by on optical microscope when each source power is applied to the optical microscope; (S320) Obtain cumulative distributions of the number of particles at each grayscale value using the number of pixels corresponding to each grayscale value in the image; (S330) Search for a grayscale value just before the grayscale value at which each of the cumulative number of particles in more than two distributions, among the cumulative distributions of the number of particles for each each image, overlap for the first time; (S340) Obtain the cumulative number of particles of each image at the searched grayscale value, and calculate the surface roughness of the deposited film for each image

Description

Method for measuring surface roughness of deposition film and apparatus

The present invention relates to a method and apparatus for measuring surface roughness of a deposited thin film, and more particularly, to a method and apparatus for measuring surface roughness of a deposited thin film capable of measuring the surface roughness of a deposited thin film without using an atomic microscope.

In general, fine thin films are used in the manufacture of electronic devices, the surface roughness of the thin film affects the quality of the subsequent process and the efficiency of the device. Surface roughness in nano and micro units is mainly measured using atomic force microscopy (AFM). Conventional techniques for measuring the surface roughness of a thin film using an atomic force microscope are disclosed in Korean Patent Application Laid-Open No. 2011-0014978.

An atomic force microscope measures surface roughness using the cantilever which scans the surface of a sample, and the probe (probe) at the end of a cantilever. As the probe approaches the sample surface, a pull and push force acts between the atoms at the tip of the probe and the atoms at the sample surface, causing the cantilever to bend upward and downward. Therefore, the atomic force microscope uses the principle of determining the structure of the atomic unit by measuring the bending degree of the cantilever to make an image.

In this case, when using an atomic force microscope, a separate probe is required, and according to the shape and material of the probe, an interference phenomenon may occur on the surface of the sample and may cause distortion of an image. Accordingly, there is a need for the development of a method or apparatus for measuring surface roughness without the use of a probe.

SUMMARY OF THE INVENTION An object of the present invention is to provide a method and apparatus for measuring surface roughness of a deposited thin film that can easily measure surface roughness using an image captured by a deposited thin film photographed with an optical microscope.

According to an embodiment of the present invention, an image of a deposition thin film photographed using an optical microscope is received, and the image of each of the plurality of source powers applied to the optical microscope is respectively input. Acquiring a cumulative particle number distribution representing a cumulative particle number at each gray scale value by using the number of pixels corresponding to the gray scale value; and among at least two cumulative particle number distributions obtained for each of the captured images, Searching for a gray scale value immediately before a gray scale value at which an overlap of cumulative particle numbers first occurs in a distribution chart, and acquiring the cumulative particle number from the found gray scale values for each of the captured images and depositing the deposited thin film. Calculating a surface roughness of each of the captured images; It provides ware measurements.

Here, the cumulative particle number may be a particle number accumulated from 0, which is a minimum gray scale value, to the selected gray scale value in the captured image.

In the calculating of the surface roughness of the deposited thin film, the surface roughness of the deposited thin film may be calculated by multiplying the cumulative particle number by a scale factor.

In addition, the optical microscope may acquire the captured image using only an object beam of a reference beam and an object beam.

In addition, the present invention is a step of receiving a captured image of the surface of the deposited thin film imaged using an optical microscope, and the x-axis or y-axis of the captured image using the gray scale value of each pixel constituting the captured image It provides a method of measuring the surface roughness of the deposited thin film comprising the step of obtaining a profile of the surface roughness with respect to the direction.

In addition, the obtaining of the surface roughness profile may be obtained by using the following equation.

는 상기 j번째 픽셀층 상에 x축 방향으로 존재하는 m개의 픽셀들 중 i번째 픽셀에 대한 그레이 스케일 값,

는

의 최대 그레이 스케일 값(255)을 통해 수정된 그레이 스케일 값,

는 상기 j번째 픽셀층 상에 x축 방향으로 존재하는 m개의 픽셀에 대한 그레이 스케일 값의 평균을 의미한다.Here, R _j is the surface roughness value for the j-th pixel layer in the y-axis direction in the captured image,

Is a gray scale value for the i th pixel among m pixels present on the j th pixel layer in the x-axis direction,

The

The gray scale value modified by the maximum gray scale value (255) of,

Denotes an average of gray scale values for m pixels existing in the x-axis direction on the j-th pixel layer.

In addition, the present invention is an image input unit for receiving a captured image of the surface of the deposited thin film imaged by using an optical microscope, the image input unit for each of the plurality of source power applied to the optical microscope, and in the captured image A particle number distribution obtaining unit obtaining a cumulative particle number distribution indicating a cumulative particle number at each gray scale value by using the number of pixels corresponding to each gray scale value, and a cumulative particle number distribution obtained for each captured image; Among them, a scale value selection unit for searching for a gray scale value immediately before the gray scale value at which the overlap of the cumulative particle number first occurs in at least two distribution charts, and the cumulative particle number in the searched gray scale value. Acquire the picked-up image by image to calculate the surface roughness of the deposited thin film for each shot image It provides a surface roughness measuring apparatus of the deposited thin film including a surface roughness calculating unit.

The present invention provides an image input unit for receiving an image captured on a surface of a deposition thin film photographed using an optical microscope, and an x-axis or y of the image captured using the gray scale values of the pixels constituting the captured image. Provided is a surface roughness measuring apparatus of a deposited thin film including a profile obtaining unit obtaining a surface roughness profile in an axial direction.

According to the method and apparatus for measuring the surface roughness of the deposited thin film according to the present invention, there is an advantage that the surface roughness characteristic can be easily measured by using the particle number characteristic from the captured image of the deposited thin film photographed using the optical microscope.

1 is a schematic configuration diagram of an optical microscope for an embodiment of the present invention.
2 is a block diagram of an apparatus for measuring surface roughness of a deposited thin film according to a first exemplary embodiment of the present invention.
3 is a flowchart illustrating a method of measuring surface roughness using FIG. 2.
4 is an example of a captured image according to step S310 of FIG. 3.
FIG. 5 illustrates a cumulative particle number distribution of the captured image for each source power obtained in operation S310 of FIG. 3. 6 is a graph showing the correlation between the surface roughness actually measured using the conventional ATM and the cumulative particle number according to the first embodiment of the present invention for each source power.
7 is a block diagram of an apparatus for measuring surface roughness of a deposited thin film according to a second exemplary embodiment of the present invention.
8 is a flowchart of a measuring method using FIG. 7.
FIG. 9 is an example of a captured image acquired at step S910 of FIG. 8.
10 is a configuration example of a captured image for explaining operation S920 of FIG. 8.
FIG. 11 illustrates an image (size 50 × 50 pixels) having the same shape as that of FIG. 9 for each source power applied to a source power applied to an optical microscope in this embodiment, and applies Equation 5 to the prepared captured image. The number of particles in pixels is calculated.
FIG. 12 illustrates a change in the gray scale value in the j = 1 th pixel layer in the captured image of FIG. 9.
FIG. 13 illustrates a result of profiling of surface roughness with respect to the y-axis direction in the captured image of FIG. 9.

DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention.

1 is a schematic configuration diagram of an optical microscope for an embodiment of the present invention. An optical microscope consists of a laser, a beam splitter, a microscope lens, and a charge coupled device (CCD) sensor. A microlens with a magnification of 100 times (x100) or more is used for the measurement of the electron number distribution, and the distance between the sample (deposited thin film) and the microlens placed on the stage is adjusted to within 1 mm.

In such an optical microscope, unlike the conventional optical microscope, the optical microscope acquires an image using only an object beam among a reference beam and an object beam. In FIG. 1, the solid line beam divided by the beam splitter and directed horizontally is the reference beam, and the dotted line beam directed toward the CCD sensor in the stage corresponds to the object beam.

The laser split by the beam splitter penetrates downward toward the deposited thin film and then reflects upward again, so that an electron distribution image inside the deposited thin film is stored in the CCD image. In this case, when the number of electrons on the deposited thin film is high, the number of electrons imprinted on the photodiode decreases while the light enters the CCD and the captured image becomes dark. Therefore, the bright portion, that is, the portion having a large gray scale value, in the captured image is actually a portion having few electrons in the deposited thin film, whereas the dark portion, that is, the portion having a small gray scale value, corresponds to the portion having many electrons in the deposited thin film.

Although the CCD image is originally composed of the X and Y axes, which are two-dimensional planes, the object can be distinguished from the three-dimensional space by moving the two-dimensional planes to the Z-axis through reconstruction. This reconstruction technique is a well-known general method and is applied to calculate the electron distribution in a thin film. The restoration equation refers to equation (1).

Where u (x, y) is the input image and d is the distance the object has fallen. kx and ky are singular functions for making a Fresnel zone pattern. h (r, c) is divided into a real part and an imaginary part. Equation (2) represents phase and Equation (3) represents magnitude, so that it can be imaged again.

Hereinafter, an apparatus and method for calculating the surface roughness of the deposited thin film using the image captured by the optical microscope will be described in detail.

2 is a block diagram of an apparatus for measuring surface roughness of a deposited thin film according to a first exemplary embodiment of the present invention. The first embodiment is a method of calculating the surface roughness using a plurality of captured images obtained for each source power applied to the optical microscope.

The apparatus 100 according to the first embodiment of the present invention includes an image input unit 110, a particle number distribution obtaining unit 120, a scale value selecting unit 130, and a surface roughness calculating unit 140.

First, the image input unit 110 receives an image captured on the surface of the deposited thin film imaged using an optical microscope. In more detail, the captured image is input to each of a plurality of source powers applied to the optical microscope. When four source powers are used, four captured images corresponding thereto are input.

 The particle number distribution obtaining unit 120 acquires a cumulative particle number distribution indicating the cumulative particle number at each gray scale value by using the number of pixels corresponding to each gray scale value in the captured image.

The scale value selector 130 may select a gray scale value immediately before the gray scale value at which the accumulation of the cumulative particle number overlaps in at least two distribution charts among the cumulative particle number distributions obtained for each captured image. This is the part to search.

The surface roughness calculator 140 acquires the cumulative number of particles in the found gray scale value for each of the captured images and calculates the surface roughness of the deposited thin film for each of the captured images.

3 is a flowchart illustrating a method of measuring surface roughness using FIG. 2. Hereinafter, a method of calculating the surface roughness of the deposited thin film will be described with reference to FIGS. 2 and 3.

First, the image input unit 110 receives an image captured on the surface of the deposited thin film photographed using an optical microscope, and receives an image captured for each of a plurality of source powers applied to the optical microscope (S310).

4 is an example of a captured image according to step S310 of FIG. 3. FIG. 4 is a CCD image of the thin film deposited by applying a source power of 200 W to FIG. 1 under conditions of SiH4 8 sccm and NH3 22 sccm using the imaging device of FIG. 1. 4 is an example of the captured image obtained when the source power is applied at 200 W, and in addition, the captured image obtained when the source power is applied at 400 W, 600 W, and 800 W is additionally used.

Thereafter, the particle number distribution obtaining unit 120 obtains a cumulative particle number distribution indicating a cumulative particle number at each gray scale value by using the number of pixels corresponding to each gray scale value in the captured image. (S320).

Here, the number of particles means the number of particles in pixel units. The number of particles (number of particles) in units of pixels means the number of pixels having a corresponding gray scale value in the captured image, and represents the number of particles expressed in units of the number of pixels.

FIG. 5 illustrates a cumulative particle number distribution of the captured image for each source power obtained in operation S310 of FIG. 3. The horizontal axis of FIG. 5 is 256 gray scale values ranging from 0 to 255, and the vertical axis corresponds to the number of particles in units of pixels as the cumulative number of particles.

When the number of pixels corresponding to each gray scale value in the captured image is determined for each gray scale value, the cumulative number of particles at a specific (i-th) gray scale value may be obtained. That is, the cumulative number of particles in the i-th gray scale value means the number of particles accumulated from the minimum gray scale value 0 to the i-th gray scale value.

If the number of pixels corresponding to the 0 to 2nd gray scale value in the arbitrary captured image is 2 gray scale values 0, 3 gray scale values 1, and 4 gray scale values 2, the gray scale value 1 The cumulative particle number in E is five, and the cumulative particle number in gray scale value 2 is nine.

FIG. 5 corresponds to the cumulative particle number distribution in pixels for each captured image obtained by changing the source power to 200, 400, 600, and 800W. It can be seen that the cumulative particle number distribution differs for each source power.

Next, the scale value selector 130 corresponds to a point immediately before the gray scale value at which the accumulation of the cumulative particle number overlaps at least two of the cumulative particle number distributions acquired for each captured image as shown in FIG. 5. The gray scale value (the '49' value in FIG. 5) is searched for (S330).

That is, in FIG. 5, the gray scale value 49 is a gray scale value immediately before overlapping of at least two distributions, and represents an initial gray scale value such that cumulative particle number distributions do not overlap.

Thereafter, the surface roughness calculating unit 140 obtains the cumulative number of particles in the searched gray scale value for each of the captured images and calculates the surface roughness of the deposited thin film for each of the photographed images (S340).

That is, the number of all pixels whose gray scale value is in the range of 0 to 49 is the cumulative particle number at the gray scale value 49. Using only the searched cumulative particle number, the surface roughness of the deposited thin film can be obtained.

The surface roughness is measured based on the black particle number distribution of the image, and the black particles are located below the reference surface position of the thin film. That is, the distribution of the black particle number corresponds to an element for determining the surface roughness, and the range of 0 to 49 corresponds to the gray scale range in which the black particle number is distributed.

6 is a graph showing the correlation between the surface roughness actually measured using the conventional ATM and the cumulative particle number according to the first embodiment of the present invention for each source power. Here, the gray scale range used for the calculation of the cumulative particle number is 0 to 49 range as before.

The horizontal axis represents the RF source power value applied to the thin film deposition. The vertical axis is the surface roughness value (-◆-) actually measured using the conventional AFM, and the right vertical axis is the cumulative particle number (-▲) in the range of 0 to 49 gray scale values obtained for each source power through the first embodiment. -). In the case of the right vertical axis, the scale value is omitted.

Here, the distance from the actual surface roughness is slightly different depending on the source power used, but the trend is maintained. Therefore, in the present invention, the surface roughness calculating unit 140 calculates the surface roughness of the deposited thin film by multiplying the calculated cumulative particle number by a separate scale factor (positive constant).

For example, the surface roughness for 800 W in FIG. 6 is 1.65. In addition, the cumulative particle count calculated in the grayscale range of 0-49 for the same 800W source power is 2882372 with reference to FIG. 5. In other words, dividing 1.65 by 2882372 yields a scale factor of 5.72 × 10 ⁻⁷ .

If the scale factor obtained for 800W is also applied to the cumulative particle number results for the other three source powers (multiply), the surface roughness value in the captured image for each source power can be calculated (inferred).

In the present embodiment, the 800W cumulative particle number is used to determine the scale factor, but a unique scale factor value calculated based on another source power 200W, 400W, or 600W may be used.

As described above, the present invention has an advantage that the surface roughness of the deposited thin film can be easily measured only by calculating the cumulative number of particles at a specific gray scale value from the captured image of the deposited thin film photographed using the optical microscope.

In the above case, the surface roughness is calculated using the number of particles in the pixel unit, but the surface roughness may be calculated in units of electrons as described below. The reason is explained in detail below.

As described above, the black particle portion in the photographed image is actually a portion having a large number of electrons on the deposited thin film. Therefore, the black particle number in the image can be used to convert the electron number. For the calculation of the number of electrons included in any gray scale range (ex, 0 to 49), which is the range used for the calculation of the cumulative particle number, refer to Equation 4.

Here, N _i is the number of pixels corresponding to the i-th gray scale value within the arbitrary gray scale range (0 to 49) among the pixels constituting the captured image, G _i is the i-th gray scale value, G _max Is the maximum gray scale value that can be expressed in the captured image, n is the number of electrons generated per LSB of the bit string for the pixel, and the LSB (Least significant bit) is the least significant bit of the bit string expressed in binary or the bit string. It means the least significant bit.

The portion of the gray scale value, that is, the black portion, in the actual photographed image is the portion having many electrons. Therefore, since the number of electrons generated in the actual captured image increases as the value of Gi decreases, the number of electrons is proportional to the value of (Gmax-Gi). The number of electrons of Equation 4 represents the number of electrons generated for the corresponding pixels on the captured image.

로 정의된다. 이는 촬상 영상에서 그레이 스케일 값이 큰 부분 즉, 밝은 부분은 빛이 많이 통과해서 카메라의 CCD 센서 상에서는 전자를 많이 발생시키는 공간이기 때문이다. 따라서, CCD 센서 상에서 발생하는 전자수는 Gi 값에 비례한다.In contrast, the number of electrons generated on an actual CCD sensor

. This is because a large gray scale value in the captured image, that is, a bright portion, is a space where a lot of light passes and generates a lot of electrons on the CCD sensor of the camera. Therefore, the number of electrons generated on the CCD sensor is proportional to the Gi value.

As a simple example, assume that the arbitrary gray scale range is 40-41, and the number of pixels having gray scale values 40 and 41 is 50 and 100, respectively. In this case, this is _{M = 2, G 1 = 40} , G 2 = 41, N 1 = 50, N 2 = 100. At this time, the number of particles (electrons) in units of pixels is 40 × 50 + 41 × 100 = 6,100. When n = 180 is applied, the number of electrons generated in the CCD is calculated to be 6,100 × 180 = 1,098,000. The number of electrons generated in the captured image is {50 × (255-40) + 100 × (255-41)} × 180 = 5,787,000 according to equation (4).

As described above, the present invention can not only induce the surface roughness only by the number of particles per pixel, but also calculate the surface roughness based on the number of electrons. That is, it is also possible to infer the surface roughness by multiplying the scale factor in the same manner as shown in the y-axis of FIG.

7 is a block diagram of an apparatus for measuring surface roughness of a deposited thin film according to a second exemplary embodiment of the present invention. This second embodiment is a method of calculating the surface roughness by using each of the captured images obtained for each source power applied to the optical microscope.

The apparatus 200 according to the second embodiment is for acquiring profiling of surface roughness with respect to the horizontal axis (x-axis) and vertical axis (y-axis) directions of the captured image, and the image input unit 210 and the profile acquisition The unit 220 is included.

The image input unit 210 receives a captured image of the surface of the deposited thin film imaged using an optical microscope. The profile obtaining unit 220 obtains a profile of surface roughness in the x-axis or y-axis direction of the captured image by using gray scale values of the pixels constituting the captured image.

8 is a flowchart of a measuring method using FIG. 7. Hereinafter, a method of obtaining the profile of the surface roughness will be described with reference to FIGS. 7 and 8.

First, the image input unit 210 receives a captured image of the surface of the deposited thin film imaged using an optical microscope (S910).

FIG. 9 is an example of a captured image acquired at step S910 of FIG. 8. 9 corresponds to an enlarged image by extracting a partial region of FIG. 4. That is, in step S910, the captured image may be used as it is or an enlarged image thereof may be used.

Next, the profile obtaining unit 220 obtains a profile of surface roughness in the x-axis or y-axis direction of the captured image by using gray scale values of the pixels constituting the captured image (S920).

This step S920 will be described in detail as follows.

10 is a configuration example of a captured image for explaining operation S920 of FIG. 8. The picked-up image is composed of m × n pixels, is composed of m pixel layers in the x-axis direction, and n pixel layers in the y-axis direction. 10 is an example in which m = 9 and n = 6 for convenience of explanation.

Based on this content, the profile acquisition unit 220 obtains the profile R _j of the surface roughness using Equation 5 below.

는 상기 j번째 픽셀층 상에 x축 방향으로 존재하는 m개의 픽셀들 중 i번째 픽셀에 대한 그레이 스케일 값,

는

의 최대 그레이 스케일 값(255)을 통해 수정된 그레이 스케일 값,

는 상기 j번째 픽셀층 상에 x축 방향으로 존재하는 m개의 픽셀에 대한 그레이 스케일 값의 평균을 의미한다.Here, R _j is the surface roughness value for the j-th pixel layer in the y-axis direction in the captured image,

Is a gray scale value for the i th pixel among m pixels present on the j th pixel layer in the x-axis direction,

The

The gray scale value modified by the maximum gray scale value (255) of,

Denotes an average of gray scale values for m pixels existing in the x-axis direction on the j-th pixel layer.

Of course, a similar operation to the profiling of the surface roughness value (R _i) for the i-th layer of the pixel in the x-axis direction by the same principle as Equation (5) that as a matter of course. In Equation 5, j is i, i is j, and m is n.

FIG. 11 illustrates an image (size 50 × 50 pixels) having the same shape as that of FIG. 9 for each source power applied to the source power applied to the optical microscope in this embodiment, and applies Equation 5 to the prepared captured image. The average grayscale value (R ₁ ) obtained from the first pixel layer along the y axis is shown. When Equation 5 is applied, the number of pixel layers constituting the y-axis, that is, 50 R values in one image, is obtained. FIG. 11 illustrates an average grayscale value of the first pixel layer.

Compared with FIG. 6, the trend is very similar, demonstrating that the method of Equation 5 is effective for calculating the surface roughness. Compared with the case of Figure 6, the biggest advantage is that it can calculate the surface roughness trend without the scale constant.

FIG. 12 illustrates a change in the gray scale value in the j = 1 th pixel layer in the captured image of FIG. 9. The captured image of FIG. 9 is an image of m = 50 and n = 50 and has 50 pixel layers in the x-axis direction and 50 pixel layers in the y-axis direction. 12, the change in the gray scale value of the pixels i = 1 to 50 in the j = 1 th pixel layer may be known.

FIG. 13 illustrates a result of profiling of surface roughness with respect to the y-axis direction in the captured image of FIG. 9. 13 illustrates the surface roughness distribution in grayscale according to the position on a total of 50 y-axes in the captured image. The lower figure of FIG. 13 shows the surface roughness distribution scaled by the actual surface roughness according to the position on a total of 50 y-axes in the captured image. The scaling value was used as the scale factor (0.00186) obtained by dividing the average of the actual surface roughness (0.4 nm) by the total average value 215 of the gray scale shown in the upper figure of FIG. 13.

As described above, in the present embodiment, the distribution of the surface roughness with respect to the y axis can be confirmed by using Equation 5.

According to the present invention as described above, there is an advantage that the surface roughness characteristics can be easily measured by using the particle number characteristics included in any gray scale range from the captured image of the deposited thin film imaged using the optical microscope. Further, according to the present invention, there is an advantage in that the distribution of surface roughness in the x-axis or y-axis direction of the captured image is obtained by using gray scale values of the pixels constituting the captured image of the deposited thin film.

In addition, the present invention does not require any probe like the conventional AFM method, it is possible to obtain the surface roughness and the profile of the deposited thin film by using only the particle number analysis of the captured image, which is necessary for measuring the surface roughness of the deposited thin film. Save time and money.

While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. Accordingly, the true scope of the present invention should be determined by the technical idea of the appended claims.

100,200: device for measuring the surface roughness of the deposited thin film
110,210: Image input unit 120: Particle number distribution acquisition unit
130: scale value selection unit 140: surface roughness calculator
220: profile acquisition unit

Claims (12)

Receiving an image of an image of the surface of the deposited thin film photographed using an optical microscope, and receiving the image of each of the plurality of source powers applied to the optical microscope;
Acquiring a cumulative particle number distribution indicating a cumulative particle number at each gray scale value by using the number of pixels corresponding to each gray scale value in the captured image;
Searching for a gray scale value immediately before the gray scale value at which overlapping of the cumulative particle number occurs first on at least two distribution charts among the cumulative particle number distributions acquired for each captured image; And
And calculating the surface roughness of the deposited thin film for each of the captured images by acquiring the cumulative number of particles in the searched gray scale value for each of the captured images. The method according to claim 1,
The cumulative particle number,
The method of measuring the surface roughness of the deposited thin film is the number of particles accumulated from the minimum gray scale value 0 to each gray scale value in the captured image. The method according to claim 1,
Computing the surface roughness of the deposited thin film,
And calculating a surface roughness of the deposited thin film by multiplying the cumulative particle number by a scale factor. The method according to claim 1,
The optical microscope is a surface roughness measuring method of the deposited thin film to obtain the captured image using only the object beam of the reference beam and the object beam. Receiving a captured image of the surface of the deposited thin film photographed using an optical microscope; And
Obtaining a profile of surface roughness with respect to an x-axis or y-axis direction of the captured image by using gray scale values of the pixels constituting the captured image,
Acquiring the profile of the surface roughness,
Method of measuring the surface roughness of the deposited thin film obtained by using the following equation:

Here, R _j is the surface roughness value for the j-th pixel layer in the y-axis direction in the captured image,

Is a gray scale value for the i th pixel among m pixels present on the j th pixel layer in the x-axis direction,

The

The gray scale value modified by the maximum gray scale value (255) of,

Denotes an average of gray scale values for m pixels existing in the x-axis direction on the j-th pixel layer. delete An image input unit configured to receive an image captured on the surface of the deposited thin film photographed by using an optical microscope, and to receive the image captured by each source power applied to the optical microscope;
A particle number distribution obtaining unit obtaining a cumulative particle number distribution indicating a cumulative particle number at each gray scale value by using the number of pixels corresponding to each gray scale value in the captured image;
A scale value selection unit searching for a gray scale value immediately before a gray scale value at which overlapping of the cumulative particle numbers first occurs on at least two distribution charts among the cumulative particle number distributions acquired for each of the captured images; And
And a surface roughness calculating unit configured to acquire the cumulative particle counts of the searched gray scale values for each of the captured images, and calculate a surface roughness of the deposited thin films for each of the photographed images. The method of claim 7,
The cumulative particle number,
The surface roughness measuring apparatus of the deposited thin film which is the number of particles accumulated from the minimum gray scale value 0 to each gray scale value in the captured image. The method of claim 7,
The surface roughness calculation unit,
And calculating a surface roughness of the deposited thin film by multiplying the accumulated particle number by a scale factor. The method of claim 7,
The optical microscope is a surface roughness measuring apparatus of the deposited thin film to obtain the captured image using only the object beam of the reference beam and the object beam. An image input unit configured to receive an image captured on the surface of the deposited thin film photographed using an optical microscope; And
And a profile obtaining unit obtaining a profile of surface roughness in the x-axis or y-axis direction of the captured image by using gray scale values of the pixels constituting the captured image.
The profile acquisition unit,
Apparatus for measuring the surface roughness of the deposited thin film obtained by using the following equation:

Here, R _j is the surface roughness value for the j-th pixel layer in the y-axis direction in the captured image,

Is a gray scale value for the i th pixel among m pixels present on the j th pixel layer in the x-axis direction,

The

The gray scale value modified by the maximum gray scale value (255) of,

Denotes an average of gray scale values for m pixels existing in the x-axis direction on the j-th pixel layer. delete

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