KR102171207B1 - Method for quantitative measurement of defects within block-copolymer thin-flim having lamellar pattern - Google Patents

Abstract

The present invention relates to a method for quantitatively analyzing morphological defects in a block-copolymer thin film having a self-assembled lamella pattern, including a series of processing steps using SEM images.

Description

{Method for quantitative measurement of defects within block-copolymer thin-flim having lamellar pattern}

The present invention relates to a method for quantitatively analyzing morphological defects in a block-copolymer thin film having a self-assembled lamella pattern, including a series of processing steps using SEM images.

Memory devices are typically provided as internal semiconductor integrated circuits within a computer or other electronic device. Memory devices usually have a number of different types of memory, including random access memory (RAM), read only memory (ROM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), and flash memory. Flash memory devices have evolved into a popular source of nonvolatile memory for a wide range of electronic applications. Flash memory devices typically have to take into account high memory density (density), high reliability and low power consumption.

Since the development of the bipolor transistor at Bell Labs in 1947, the degree of integration of semiconductor chips has evolved according to Moore's law. These developments have been faithfully maintained thanks to the continuous development of the photolithography process, but according to the recent ITRS (International Technology Roadmap for Semiconductor), it is known that process technology of 30 nm or less has many problems to implement due to the limitation of photolithography technology. . Accordingly, various nanopattern fabrication technologies are being developed based on new principles, and among these research fields, research on molecular assembly nanostructures is receiving worldwide attention.

As described above, attempts to increase the degree of integration of semiconductor devices in order to increase the memory density in semiconductor devices such as flash memories are continuing. Planarly, the area occupied by each unit cell has been reduced, and in response to such a decrease in unit cell area, a design rule of a smaller nanoscale CD (Critical Dimension) of several to several tens of ㎚ is applied. Accordingly, a new technique for forming a fine pattern such as a fine contact hole pattern having a nano-scale opening size or a fine line pattern having a nano-scale width is required.

When relying only on top-down photolithography technology to form fine patterns for semiconductor device manufacturing, there is a limitation in improving resolution due to the wavelength of the light source and the resolution limit of the optical system. . As one of efforts to overcome the limitation of resolution in the photolithography technology and develop a next-generation microfabrication technology, methods of forming a microstructure in a bottom-up method using a self-assembly phenomenon of molecules have been attempted.

Molecular assembly The most representative polymer material that forms nanostructures is a block copolymer (BCP), which has a molecular structure in which chemically different unit blocks are connected through covalent bonds. Therefore, the block copolymer can form various nanostructures such as spheres, cylinders, and lamellas having a regularity of several to tens of nanometers, and is thermodynamically stable and has nanostructures. It has the advantage of being able to design size and physical properties at the synthesis stage. In addition, it is possible to quickly implement nanostructures in a large area through a parallel process, and it is easy to remove the template after forming an inorganic and organic nanostructure thin film using a block copolymer template, nanowires, quantum dots, magnetic storage media, nonvolatile memory In the fields of IT, BT, ET, etc., intensive research is underway with nanopattern manufacturing technology for manufacturing various next-generation devices.

The lamellar pattern that appears from the double self-assembled block copolymer thin film is a promising sub-10 nm nanoscale pattern for next-generation devices such as memory, storage and arithmetic logic units (ALU). It is the material. Quantitative measurement of the quality of the lamella pattern is an important factor determining whether or not the developed pattern can be applied to an actual device. However, the conventional measurement method is dependent on an optical microscope that detects local defects by image processing, and it is difficult to provide detailed information on the quality of the pattern due to the technical limitations of the measurement method. Accordingly, there is a need for a quantitative measurement method capable of analyzing microscopic order, quality and uniformity of patterns and delivering specific information.

The present inventors have worked hard to find a factor capable of quantitatively analyzing the defects contained therein and the processing process through several steps of processing using the original SEM image of the surface of the thin film to be inspected. Converted to an image, i) skeletal imaged and measured the length of the linear part to derive deviation from the ideal pattern, or ii) regarded as a two-dimensional matrix and calculated entropy by matching identifiable blocks to create self-assembled lamella patterns. It was confirmed that it was possible to provide a factor capable of quantitatively analyzing the quality of the block copolymer thin film having, and the present invention was completed.

A first aspect of the present invention is a method of quantitatively analyzing morphological defects in a self-assembled block copolymer thin film, comprising: a first step of preparing an original SEM image of the surface of the self-assembled block copolymer thin film; And a second step of converting the original image to a binary image.

A second aspect of the present invention is step A of preparing a self-assembled block copolymer thin film; B step of calculating stripe pattern uniformity (SPU), alignment entropy (SC), or both for the prepared self-assembled block copolymer thin film by performing the analysis method according to the first aspect; And it provides a quality control method of a self-assembled block copolymer thin film comprising the step C of selecting a product having the calculated stripe pattern uniformity of 1 to 1.005, or an array entropy of 0.01 or less.

The third aspect of the present invention is a step I of preparing a self-assembled block copolymer thin film having a regular pattern having a stripe pattern uniformity of 1 to 1.005 selected by the method of the second aspect, or an array entropy of 0.01 or less; And an II step of transferring the self-assembled block copolymer thin film having a regular pattern onto a desired material as a template.

Hereinafter, the present invention will be described in more detail.

The present invention is a method for quantitatively analyzing morphological defects in a self-assembled block copolymer thin film,

A first step of preparing an original SEM image of the surface of the self-assembled block copolymer thin film; And

It provides a method comprising a second step of converting the original image to a binary image.

For example, the second step can be achieved by converting the original SEM image to a gray-scale image and then converting the obtained gray-scale image to a binary method image.

Specifically, conversion from the original SEM image to a gray-scale image can be performed by applying a luminosity algorithm known in the art. In this case, the strength of the signal may be defined as 0.21R+0.72G+0.07B. However, the present invention is not limited thereto, and non-limiting methods known in the art may be performed alone or in combination to achieve the above object.

Further, the conversion from the gray-scale image to the binary method image may be performed by applying a threshold value. The threshold value may be defined as ±2σ from the average value of gray pixel values, but is not limited thereto.

For example, quantitative analysis using the method of the present invention can be accomplished by finally calculating stripe pattern uniformity (SPU), configuration entropy, or both.

Specifically, in the quantitative analysis method of the present invention, in order to provide stripe pattern uniformity (SPU), the binary method image obtained from the second step is processed into a skeleton image representing nonlinear line segments (process ) A third step; A fourth step of calculating a local orientation angle for each pixel in segments of the skeleton image obtained from the third step; A fifth step of calculating a length of local arc ( I _i ) of each division; And it may further include a sixth step of calculating the stripe pattern uniformity (SPU) from the local arc length calculated from the fifth step.

Specifically, in step 5, the local arc length can be calculated using Equation 2 below:

[Equation 2]

In Equation 2 above,

l _i is the arc length of the ith division,

f _i is a function of the ith division, and

x _1i and x _2i are the starting and ending points of the i-th division, respectively.

In calculating the local arc length, the application of the method of calculating the arc length of the nonlinear division given in Equation 2 above can block overestimation of the local arc length, especially near the sharp inflection point.

Specifically, in step 6, the stripe pattern uniformity (SPU) from the local arc length can be calculated using Equation 3 below:

[Equation 3]

In Equation 3 above,

l _i is the arc length of the ith division,

L is the image size of the square containing the striped pattern,

D is the intrinsic periodicity of the pattern,

N _seg is the number of segments in the lamella pattern,

n _i is the total number of points (pixels) along the i-th division,

x _{j +1} and x _{j -1} are each two neighboring points of the j-th point on the i-th partition, and

θ _j is the local orientation angle measured at the j-th point on the i-th segment, x _j .

In addition, in the quantitative analysis method of the present invention, in order to provide configuration entropy ( S _C ), a 1× n- dimensional rectangular pixel block is prepared from the binary image obtained from the second step. 3'step; A fourth' step of sweeping the block from the upper left to the lower right of the image matrix; A 5'step of calculating the coefficients of identifiable binary strings and their frequency; A 6′ step of calculating information entropy, H _n for each 1× n block; A 7'step of creating an H _n vs. n plot and searching for a linear region of the plot; An 8'step of searching for the saturating behavior of H _n ; And it may further include a 9'step of calculating the alignment entropy ( S _C ) from the saturated H _n .

Specifically, step 6'can be performed using Equation 6:

[Equation 6]

In Equation 6 above,

N _T is the sum of N _u , the total number of arrays of blocks with the next cell filled with 1s, and N _z , the total number of arrays of blocks with the next cell filled with 0s,

는 i _n 의 지수를 갖는 식별가능한 블록의 수, 및

Is the number of identifiable blocks with an exponent of i _n , and

는, k가 다음 셀의 상태를 나타낼 때, (i _n ,k)의 지수를 갖는 식별가능한 블록의 수임.

It is, when k indicates the status of the next cell, ordination of identifiable blocks with the index _(n i, k).

Furthermore, step 9'can be performed using Equation 7 below:

[Equation 7]

.

.

S _C calculated through Equation 7 may be a normalized global configuration entropy in consideration of system symmetry.

The arrangement entropy is to provide quantitative information on the order of a block copolymer thin film having a self-assembled lamella pattern, and an S _C value of 0 is obtained for a perfectly aligned 2D binary method stripe pattern with a noise level of 0. It provides an S _C value of 1 for a 2D pattern that is very disorderly.

In order to provide a device with high performance, it is necessary to establish a fine pattern process as the demands for miniaturization and density continue to continue. In particular, fabricating high-density nanopatterns over a large area through a low-cost process is an important factor in the development of various next-generation nanodevices. In the conventional semiconductor process, optical lithography techniques such as I-line and ArF were mainly used, but in order to manufacture a pattern at a level of 40 nm or less, a high-energy light source is required, and there are numerous technical difficulties in developing optical equipment according to it. , There is also a phenomenon in which the photoresist forming the pattern collapses. On the other hand, processes using high-energy light sources such as electron beam or extreme ultraviolet lithography can solve patterns of 20 nm or less, but are slow due to the formation of tandem patterns, and these disadvantages appear larger as the scale is reduced in a large area. . In addition, in the case of nanoimprint, which is another method, since the pattern is transferred through direct contact between the mask and the photoresist, contamination or damage of the mask occurs during this process, which is pointed out as a fatal problem.

Accordingly, a method of fabricating a nano pattern using a self-assembled nano structure formed by itself is attracting attention. Block copolymers, which are representative molecular assembly materials, have a size of about 5 to 50 nm because the ends of polymer chains with different chemical properties are connected by covalent bonds, and thus, spheres, cylinders, lamellars, etc. Spontaneously form periodic nanostructures of various types. This process is a parallel process that can be mass-produced. Since the nanostructure is thermodynamically stable, it is advantageous to fabricate a pattern having a high aspect ratio, and the size of the nanostructure can be variously controlled. Such a block copolymer nanostructure can be used as an etching and deposition template in a pattern transfer process. Therefore, block copolymer lithography is considered to have sufficient potential as nanolithography that can be applied to the fabrication of nanodots and nanowires, and to fabricate various next-generation nanodevices such as photonic crystals, high-performance magnetic storage media, large-capacity memory devices, semiconductor devices, and solar cells. .

Furthermore, it is important in the fabrication of functional nanostructures to form self-assembled block copolymer nanostructures on various substrates over a large area. However, in the case of a nanostructure formed by a naturally formed self-assembled block copolymer pattern, the structure is irregular and may contain many defective structures, so it is necessary to check the quality before practical use.

In this context, the present invention is a step of preparing a self-assembled block copolymer thin film;

B step of calculating stripe pattern uniformity (SPU), alignment entropy (SC), or both for the prepared self-assembled block copolymer thin film by performing the analysis method of the present invention described above; And

It provides a quality control method for a self-assembled block copolymer thin film comprising step C of selecting a product having the calculated stripe pattern uniformity of 1 to 1.005, or an array entropy of 0.01 or less.

Further, the present invention is a step I of preparing a self-assembled block copolymer thin film having a regular pattern having a stripe pattern uniformity of 1 to 1.005 selected by the above method, or an array entropy of 0.01 or less; And

It provides a method of manufacturing a semiconductor device comprising the step II of transferring the self-assembled block copolymer thin film having a regular pattern onto a desired material as a template.

For example, the self-assembled block copolymer thin film having a regular pattern may be manufactured by a graphoepitaxy technique, but is not limited thereto.

The analysis method of the present invention provides information that can quantitatively analyze the degree of defects, stripe pattern uniformity, and alignment entropy for a self-assembled block copolymer thin film. It can be discriminated, and can be used for quality control in the device preparation stage in the semiconductor production process based on this.

Figure 1 shows the original SEM image in the first row, the perfect 1D pattern with equivalent periodicity and orientation angle in the second row, and the color of the skeletonized stripes in the SEM image in the third row. It is a diagram showing a map. (a) is for shear only, and (b) to (d) are results for soft shear (SS) annealed with toluene, THF and mixtures thereof for 10 minutes, respectively. (e) is the ratio between the measured total length of a linear stripe with a periodicity of D in a 2D box with a finite scale of L ² and the ideal total length of the stripe with an offset and orientation angle of different combinations ( L ² / D ) Shows the color map for In the above calculations, L was assumed to be sufficiently greater than D (ie, L / D obtained from experimental image data was about 35 to 37).
FIG. 2 is a diagram showing a (left) 2D stripe pattern image in perfect order without noise and (right) a little noise. The noise degree can be defined as the normalized absolute value of the pixel value of a binary method image.
3 is a diagram showing a 2D stripe pattern image as a function of noise level.
4 is a diagram showing a relationship between configuration entropy and noise level of a 2D binary method stripe pattern.
5 is a diagram showing the arrangement entropy of a 2D stripe pattern having defects of different number densities.
6 is an experimentally observed SEM image of a self-assembly pattern of a block copolymer thin film having morphological defects annealed under different conditions at the top, and the bottom is generated from the original corresponding to the SEM image of the top, respectively. It is a diagram showing the acquired 2D binary method stripe pattern image. From the processed binary image, the array entropy of the pattern was calculated.
7 is a diagram showing the effect of the type of solvent (eg, toluene, tetrahydrofuran (THF), and mixtures thereof) on lamellar pattern formation. (a) is the orientation uniformity factor (Hermans parameter S) and orientation correlation distance change (insertion degree) of the lamella pattern formed on the block copolymer thin film according to the time exposed to each solvent, and (b) is the block copolymer thin film. The degree of change in the distance correlation function (g(r)) of the formed lamella pattern and the change in the distance correlation function (insertion degree), (c) is the lamella pattern formed on the block copolymer thin film according to the exposure time to the solvent. The defect area fraction change and the number density change (insertion degree) for each defect type are shown, (d) shows the stripe pattern uniformity (SPU, left) and array entropy (S _C , right) of the lamella pattern formed on the block copolymer thin film.
8 is a diagram showing the relationship between stripe pattern uniformity (SPU) and array entropy (S _C ) of a lamella pattern formed on a block copolymer thin film.

Hereinafter, the present invention will be described in more detail through examples. These examples are for explaining the present invention more specifically, and the scope of the present invention is not limited to these examples.

Example 1: Algorithm for uniformity analysis of stripe pattern

Stripe pattern uniformity (SPU), first developed and introduced in the present invention, is that a given stripe pattern is an ideal stripe pattern (that is, without defects, deflection, fluctuations in width and periodicity, noise, etc.) and Can be used to determine how similar they are. Compared to other quantitative indices such as Hermans orientation order parameter, defect count/area density, spatial correlation length, and orientational persistence length, SPU (arc) Measure the local deviations of the pattern from the ideal pattern by measuring the increase in length. This increase is due to the occurrence of non-zero local curvature, resulting from bending or morphological defects of the stripe pattern. Thus, SPU does not differentiate between morphological defects and localized bending, but rather quantitatively measures the possible non-ideal factors present in the stripe pattern that contribute to the local arc length increase. This indicates that the SPU can serve as a fast and effective measurement method for detecting the relative deviation of a 1D line-space (eg, lamella) pattern from an ideal given 1D stripe pattern. SPU can be calculated using the following equation:

[Equation 1]

[Equation 2]

In the above equation, L is the size of the square image including the stripe pattern, D is the intrinsic periodicity of the pattern, N _seg is the number of segments in the lamella pattern, and l _i is the arc of the i-th segment. The length, f _i is a function of the i-th division, and x _1i and x _2i are the starting and ending points of the i-th division, respectively. For a binary stripe pattern, Equation 1 above can be written as follows for numerical calculation:

[Equation 3]

In the above equation, n _i is the total number of points (pixels) following the i-th division, x _{j +1} and x _{j -1} are the two neighboring points of the j-th point on the i-th division, respectively, and θ _j is i It is the local orientation angle measured at the j-th point on the th segment, x _j . The method of calculating the arc length of the nonlinear division given in Equation 2 above can block overestimation of the local arc length, particularly near the sharp inflection point. The total length of the linear segmentation of the ideal stripe pattern within a 2D box with a finite scale was not exactly equal to L ² / D . However, since the image size is given sufficiently larger than the stripe pattern periodicity, the deviation is negligible. As shown in Fig. 1(e), when L / D is about 35 to 37, the deviation from L ² / D of the measured total length of the stripe pattern, given by the change in the offset and orientation angle of the stripe pattern, is It is negligible (e.g. the L ² / D ratio is about 1).

The algorithm for calculating the SPU is summarized in Table 1 below.

Step 1 Get original image data Step 2 Conversion from original image to binary image Step 3 Binary image processing with skeletal images representing non-linear line segments Step 4 Calculation of local orientation angle for each pixel in segmentation Step 5 Calculation of local arc length for each segment using Equation 2 Step 6 SPU calculation using Equation 3

<Comparison with existing stripe pattern uniformity detection algorithm>

1. Conventional Algorithm

-Analysis of orientational order

-Analyzing spatial correlation length

-Direct measurement of spatial order for ideal lamellar pattern is not possible

2. The algorithm of the present invention

-Detect the quality of the lamella pattern by calculating the increase in the line segment due to a combination of several other factors such as topological defects, bending and width variations.

3. Advantages of the algorithm of the present invention

-Provides more specific quantitative information on the quality of the pattern

-Measure'ideality' by calculating SPU (e.g. SPU = 1 for a perfect lamella pattern)

Example 2: Analysis of configuration entropy

The arrangement entropy of 2D patterns, first developed and introduced in the present invention, can be used to determine how structurally and morphologically ordered a given stripe pattern is in a quantitative manner. The alignment entropy may be information that is complementary to other quantitative information measuring defects and disorders within a given pattern. Compared to other quantitative indices, alignment entropy measures the overall orderness of a given 2D pattern. The array entropy for an ideally ordered stripe pattern approaches 0, whereas a perfectly randomly disordered pattern without any spatial correlation exhibits an increasing behavior of the array entropy that converges to unity.

For a given image of the block copolymer self-assembly pattern, the original image was converted to gray-scale, and finally converted to a binary image using an appropriate relevant threshold. The converted image can be regarded as a 2D matrix with dimensions of L ₁ × L ₂ , with each cell filled with 0 or 1. The constellation entropy can be calculated from information entropy, which is determined by the frequency distribution of distinguishable blocks in the matrix. Mathematically, it is convenient to set the block size to 1× n , where n ≥2. From the first cell with an exponent of [1, 1] to the last cell with an exponent of [L ₁ , L ₂ ], all cells in the matrix were swept and matched with a designated block of size 1× n . During the sweeping process, the number of identifiable blocks can be counted. For example, given n = 5, C ₂₁ = [1 0 1 0 1] can be indexed as i ₅ = 21, while C ₁₅ = [0 1 1 1 1] is indexed as i ₅ = 15 Can be. After that, the binary chord can be further analyzed with the value of the cell immediately following the given block. N _u is the total array number of blocks with the next cell filled with 1s, and N _z is the total array number of blocks with the next cell filled with zeros. Thereafter, the entropy of the arrangement of the unit block (eg, 1×1 size), H ₀ , can be calculated according to the following equations 4 and 5:

[Equation 4]

[Equation 5]

.

.

The array entropy, H _n can be calculated according to Equation 6 in a similar way for larger blocks of size 1× n :

[Equation 6]

는 i _n 의 지수를 갖는 식별가능한 블록의 수를 나타내며,

는, k가 다음 셀의 상태(예컨대, u=1 및 z=0)를 나타낼 때, (i _n ,k)의 지수를 갖는 식별가능한 블록의 수이다. 상기 시스템의 대칭성을 고려하여, 정상화된 글로벌 배열 엔트로피(normalized global configuration entropy), S _C 는 하기 반응식 7에 따라 계산될 수 있다:In the above equation,

Represents the number of identifiable blocks with an exponent of i _n ,

Is a k, the following number of identifiable blocks with the index to indicate the status (e. G., U = 1 and z = 0) of the cell, (i _n, k). Considering the symmetry of the system, the normalized global configuration entropy, S _C , can be calculated according to the following Scheme 7:

[Equation 7]

.

.

The algorithm for the calculation is summarized in Table 2 below.

Step 1 Preparation of the original SEM image Step 2 Conversion from original image to gray-scale image Step 3 Conversion from gray-scale image to binary image Step 4 Preparation of 1× n- dimensional rectangular pixel block Step 5 Sweep the block from the top left to the bottom right of the image matrix Step 6 Calculation of the coefficients of identifiable binary strings and their frequency Step 7 Information entropy for each 1× n block, Calculation of H _n Step 8 H _n versus n plot and search for the linear region of the plot Step 9 Exploring the saturating behavior of H _n Step 10 Calculation of configuration entropy from saturated H _n

Experimental example One: On the noise level Confirm the change of entropy of the array according to

In order to confirm whether the array entropy can deliver quantitative information on the order of the 2D binary method pattern, as shown in FIG. 2, the above algorithm was applied to a stripe pattern having an adjusted noise level. As shown in FIG. 3, it was possible to construct a perfectly aligned stripe pattern and a completely disordered pattern while increasing the noise level. As shown in FIG. 4, the algorithm for calculating the array entropy was able to deliver specific, physically meaningful, and quantitative information on the order of the 2D binary method pattern. For example, for a perfectly aligned 2D binary striped image with a noise level of 0, the algorithm gave a value of S _C = 0, while for a perfectly disordered 2D pattern it provided a value of S _C = 1. .

In addition, an algorithm was applied to calculate S _C , and as shown in FIG. 5, an increase in S _C value as the number density of morphological defects in the stripe pattern increased was successfully detected. Finally, as shown in FIG. 6, the algorithm was applied to the calculation of the experimentally acquired image of the block copolymer pattern having defects. As shown in FIG. 6, the array entropy could quantitatively measure the quality of the pattern, in inverse proportion to the entropy.

Specifically, after dissolving PS-b-P4VP in toluene or tetrahydrofuran (THF) solvent, a thin film was formed by spin coating on a silicon substrate and evaporation of the solvent. Then, annealing was performed in an environment heated to 160° C. or higher, and again exposed to a specific type of solvent, such as toluene, tetrahydrofuran, or a mixed solvent thereof, to form a self-assembled lamella pattern. For the obtained pattern, a high-resolution image was obtained with an electron microscope, and the image processing and numerical analysis algorithms of Examples 1 and 2 were applied to calculate the orientation uniformity (Hermann's variable, S) of the lamella pattern, and the result was shown in FIG. It is shown in (a). The closer the S is to 1, the more perfectly it is a lamella pattern oriented in one direction. The degree of S being away from 1 was mathematically analyzed to obtain the orientation correlation distance. The longer the orientation correlation distance, the more uniformly oriented lamellar pattern in one direction over a large area. In addition, as shown in Fig. 7(b), the distance correlation function (g(r)) of these vectors is calculated using the local orientation vectors of the lamella pattern, and the distance correlation function correlation distance (L _C ) is calculated from this. I did. The larger L _C , the more uniformly oriented lamella pattern in a wide area. In addition, as shown in Fig. 7(c), the number of topological defects in the lamella pattern and the fraction of the area affected by the defect can be calculated by processing the image with a specific algorithm, as shown in Fig. 7(d). Likewise, it was derived through the calculation of the SPU index how much the lamella pattern was not bent and stretched straight. In this case, the closer the SPU is to 1, the closer the lamella pattern is to a wide area. When the SPU is greater than 1, it indicates that there is a bend in the lamella pattern, and it means that the degree or tendency of the bend is increased. From the SPU, it was possible to quantitatively display the uniformity of the lamella pattern in which the orientation uniformity (S) cannot be distinguished on the right side of FIG. 7(a) or 7(d).

Array entropy can be calculated as a quantitative index for interpreting the uniformity of the lamella pattern in different directions. As shown in Figure 8, the sequence entropy (S _C ) showed a statistically significant correlation with the SPU. In particular, when the SPU exceeded 1.1, S _C increased rapidly, indicating that the non-uniformity of the lamella pattern rapidly increased near SPU = 1.1.

<Comparison with existing array entropy>

1. Conventional Algorithm

(1) Existing information entropy

-Analyzing binary complexity

-Cannot detect local spatial order

(2) Conventional image entropy

-Analyzing the local complexity of pixels by calculating the local frequency of the binary signal of neighboring cells

-Local spatial order cannot be detected

2. The algorithm of the present invention

-Detecting global complexity of not only lamella patterns but also binary patterns using moving window method

3. Advantages of the algorithm of the present invention

-Provides more specific quantitative information on the quality of the pattern

-'Ideality' can be measured by calculating S (e.g. S = 0 for a perfect ideal lamella pattern, S = 1 for a fully randomized binary pattern)

Claims (7)

As a method of quantitatively analyzing morphological defects in a self-assembled block copolymer thin film,
A first step of preparing an original SEM image of the surface of the self-assembled block copolymer thin film; And
A second step of converting the original image into a binary image;
A third step of preparing a 1× n- dimensional rectangular pixel block from the binary image obtained from the second step;
A fourth step of sweeping the block from the upper left to the lower right of the image matrix;
A fifth step of calculating the coefficients of identifiable binary strings and their frequency;
A sixth step of calculating information entropy, H _n for each 1× n block;
A seventh step of creating a H _n vs. n plot and searching for a linear region of the plot;
An eighth step of searching for the saturating behavior of H _n ; And
The method comprising the ninth step of calculating the configuration entropy ( S _C ) from saturated H _n .
The method of claim 1,
The second step is achieved by converting the original SEM image to a gray-scale image and then converting the obtained gray-scale image to a binary method image.
The method of claim 1,
The method, wherein the sixth step is carried out using Equation 6:
[Equation 6]

In Equation 6 above,
N _T is the sum of N _u , the total number of arrays of blocks with the next cell filled with 1s, and N _z , the total number of arrays of blocks with the next cell filled with 0s,

Is the number of identifiable blocks with an exponent of i _n , and

It is, when k indicates the status of the next cell, ordination of identifiable blocks with the index _(n i, k).
The ninth step is to be carried out using Equation 7 below:
[Equation 7]

.
Step A of preparing a self-assembled block copolymer thin film;
Step B of calculating an array entropy (S _C ) for the prepared self-assembled block copolymer thin film by performing the analysis method according to any one of claims 1 to 4; And
Quality control method of a self-assembled block copolymer thin film comprising step C of selecting a product having the calculated array entropy of 0.01 or less.
Step I of preparing a self-assembled block copolymer thin film having a regular pattern having an array entropy selected by the method of step C of claim 5 is 0.01 or less; And
A method of manufacturing a semiconductor device comprising the step II of transferring the self-assembled block copolymer thin film having a regular pattern onto a desired material as a template.
The method of claim 6,
The method of manufacturing a semiconductor device in which the self-assembled block copolymer thin film having a regular pattern is manufactured by a graphoepitaxy technique.

Images