Surface roughness evaluation method and device therefor, surface roughness evaluation program, and storage medium storing surface roughness evaluation program

Abstract

The present invention evaluates non-uniformity of roughness of a surface.　The present disclosure involves: acquiring local histogram information indicating a relationship between an amount and frequency of surface irregularities for each local measurement region; generating multiple sets of cumulative histogram information by accumulating n pieces of local histogram information from the first local histogram information progressively; defining, as reference cumulative histogram information, cumulative histogram information generated by accumulating the maximum number of N pieces of local histogram information; detecting a degree of similarity of each cumulative histogram information to the reference cumulative histogram information; and using the smallest n, for which the detected degree of similarity is equal to or greater than a predetermined threshold value, as a part of an index indicating non-uniformity of surface roughness.

Description

Surface roughness evaluation method and device, surface roughness evaluation program, and storage medium storing the surface roughness evaluation program

[0001] The present disclosure relates to a surface roughness evaluation method for evaluating the non-uniformity of the surface roughness of an object, a surface roughness evaluation device, a surface roughness evaluation program, and a storage medium storing a surface roughness evaluation program.

[0002] Surface roughness can be an important factor in various materials, articles, parts, devices, etc. For example, Patent Document 1 describes a method for transporting electronic components in a stable manner by making the surface roughness of the transport path smaller than the surface roughness of the electronic components themselves, thereby preventing defects in the electronic components from occurring during transport.

[0003] However, while conventional methods can measure the surface roughness at multiple locations and calculate the average surface roughness, they do not provide an index for objectively determining the non-uniformity (or uniformity) of surface roughness.

[0004] Patent Document 1: Japanese Patent Application Laid-Open No. 2017-210342

[0005] An object of the present disclosure is to provide a surface roughness evaluation method, a surface roughness evaluation device, a surface roughness evaluation program, and a storage medium storing a surface roughness evaluation program, which are capable of objectively quantifying and evaluating the non-uniformity of the surface roughness of an article.

[0006] The surface roughness evaluation method according to the present disclosure includes the steps of: obtaining, for each of a plurality of local measurement regions set on the surface to be evaluated, local histogram information indicating the relationship between the amount of surface unevenness and frequency within the measurement region; accumulating the first to Nth local histogram information for each of the measurement regions sequentially from the first to the Nth local histogram information to generate a plurality of n (n = 1 to N) pieces of cumulative histogram information; using the cumulative histogram information generated by accumulating n = N pieces as reference cumulative histogram information, and detecting a similarity to the reference cumulative histogram information for each of the plurality of cumulative histogram information generated by accumulating the local histogram information sequentially from n = 1 to n; and sequentially detecting, from n = 1 to n = N, whether the detected similarity satisfies a predetermined criterion, and using a number n representing the nth cumulative histogram information that first satisfies the criterion as part of an index indicating the non-uniformity of the surface roughness.

[0007] By using such a surface roughness evaluation method, the non-uniformity of the surface roughness of an article can be objectively quantified and evaluated. Even if the surface of an article has a fine, highly flat surface roughness locally, the surface may have a non-uniform surface roughness when viewed as a whole. However, by quantitatively evaluating the non-uniformity of the surface roughness of an article as disclosed herein, it is possible to appropriately supply products with a uniform surface roughness. Furthermore, if the non-uniformity of the surface roughness or its display indicates deterioration of the equipment's functionality, it is possible to appropriately determine the performance and deterioration state (durability) of the equipment, or whether the equipment requires maintenance.

[0008] The surface roughness evaluation device according to the present disclosure comprises: means for obtaining, for each of a plurality of local measurement regions set on the surface to be evaluated, local histogram information indicating the relationship between the amount of surface unevenness and frequency within the measurement region; means for accumulating the first to Nth local histogram information for each of the measurement regions sequentially from the first to the Nth local histogram information to generate a plurality of n (n = 1 to N) pieces of accumulated cumulative histogram information; means for using the cumulative histogram information generated by accumulating n = N pieces as reference cumulative histogram information, and for detecting a similarity to the reference cumulative histogram information for each of the plurality of pieces of cumulative histogram information generated by accumulating the local histogram information sequentially from n = 1 to N; and means for sequentially detecting, from n = 1 to n = N, whether the detected similarity satisfies a predetermined criterion, and using a number n representing the nth piece of cumulative histogram information that first satisfies the criterion, as part of an index indicating the non-uniformity of the surface roughness.

[0009] a step of calculating, for each of a plurality of local measurement regions set on a surface to be evaluated, local histogram information indicating the relationship between the amount of unevenness and frequency of the surface within the measurement region; a step of accumulating the first to Nth local histogram information for each of the measurement regions sequentially from the first to the Nth local histogram information to generate a plurality of n (n = 1 to N) pieces of cumulative histogram information; a step of using the cumulative histogram information generated by accumulating n = N pieces of local histogram information as reference cumulative histogram information, and detecting the similarity of each of the plurality of cumulative histogram information generated by accumulating the local histogram information sequentially from n = 1 to N, to the reference cumulative histogram information; and a step of sequentially detecting, from n = 1 to n = N, whether the detected similarity satisfies a predetermined criterion, and using a number n representing the nth cumulative histogram information that first satisfies the criterion, as part of an index indicating the non-uniformity of the surface roughness.

[0010] Furthermore, a storage medium storing a surface roughness evaluation program according to the present disclosure is a storage medium storing the above-described surface roughness evaluation program.

[0011] These surface roughness evaluation devices, surface roughness evaluation programs, and storage media storing the surface roughness evaluation programs can be used to objectively quantify and evaluate the non-uniformity of the surface roughness of an article by operating the evaluation device using the devices or executing the programs on a computer, thereby enabling the appropriate supply of products with uniform surface roughness and enabling appropriate knowledge of the device's performance, deterioration state (durability), whether the device requires maintenance, etc.

[0012] The plurality of measurement regions may be set at a predetermined distance or more from each other.

[0013] By setting multiple measurement areas at least a predetermined distance apart, it is possible to detect surface roughness from measurement areas that are less correlated with each other in terms of position and verify the degree of similarity. As a result, it is possible to more appropriately detect the distribution of feature quantities related to the surface roughness of the evaluation target, and to detect index values ​​that more appropriately reflect the non-uniformity (uniformity) of the surface roughness.

[0014] In generating the local histogram information, the local histogram information may be normalized based on the most frequent value.

[0015] By normalizing the unevenness amounts of multiple measurement regions of the measurement object, the weights of the local histogram information for each measurement region can be made approximately equal when the local histogram information is accumulated (added) in a subsequent process. In other words, when generating the cumulative histogram information, it is possible to avoid a situation in which the weights of each local histogram information differ depending on the absolute value (actual frequency) of the unevenness amount. As a result, it is possible to obtain cumulative histogram information in which the contributions of the unevenness amount distributions (local histogram information) of multiple measurement regions are equalized, thereby enabling a more appropriate evaluation of the nonuniformity of the surface of the measurement object.

[0016] In generating the cumulative histogram information, the cumulative histogram information may be normalized based on the most frequent value.

[0017] When generating cumulative histogram information, the frequencies obtained by accumulating local histogram information are normalized using the highest frequency (cumulative value) as the reference. This prevents differences in the absolute values ​​of the frequencies resulting from differences in the number of accumulated local histogram information from directly appearing as differences in similarity when the similarity of each cumulative histogram information to the reference cumulative histogram information is calculated in subsequent processing. This makes it possible to obtain cumulative histogram information that reflects the distribution of unevenness on the surface of the object to be evaluated (non-uniformity of surface roughness), allowing for a more appropriate evaluation of the non-uniformity of the surface of the object to be measured.

[0018] In the step of using the number n representing the nth position as part of an index indicating the non-uniformity of the surface roughness, if the detected similarity satisfies the formula (2), it may be determined that the similarity complies with a predetermined criterion. n ≧ Hm THL ... (2) However, Hm n is the similarity between the n-th cumulative histogram information and the reference cumulative histogram information, and Hm THL is a predetermined similarity threshold.

[0019] By making this judgment, an index is generated using a value n that indicates that the nth cumulative histogram information is similar to the distribution state of the unevenness of the surface of the object to be measured with a sufficiently high degree of similarity, and an index that appropriately represents the unevenness / uniformity of the surface of the object to be measured can be obtained.

[0020] FIG. 1 is a flowchart showing the processing steps of a surface roughness non-uniformity detection method according to the present disclosure. FIG. 2 is a block diagram showing the configuration of a surface roughness non-uniformity detection device according to the present disclosure. FIG. 3A is a diagram illustrating a local measurement region set on a target surface for detecting surface roughness non-uniformity. FIG. 3B is a diagram illustrating surface roughness measurement raw data obtained from the local measurement region. FIG. 3C is a diagram illustrating surface roughness measurement data resulting from preprocessing (flattening) the data shown in FIG. 3B. FIG. 3D is a diagram illustrating an example of a histogram of irregularities for each local measurement region shown in FIG. 3A. FIG. 3E is a diagram illustrating example local histogram information obtained by normalizing the irregularity amount histogram shown in FIG. 3D. FIG. 3F is a diagram illustrating a cumulative histogram information calculation process. FIG. 3G is a diagram illustrating a process of calculating the similarity between cumulative histogram information and reference cumulative histogram information and comparing it with a predetermined threshold. FIG. 4 relates to a first embodiment of the present disclosure and illustrates an example of surface roughness measurement data after preprocessing for each local measurement region of a metal surface immediately after polishing. FIG. 5 is a diagram illustrating surface roughness measurement data after preprocessing for each local measurement region of a surface-treated metal surface, according to a first embodiment of the present disclosure. FIG. 6 is a diagram illustrating a roughness amount histogram for the surface roughness measurement data shown in FIG. 4. FIG. 7 is a diagram illustrating the convergence state of the similarity between the cumulative histogram information and the reference cumulative histogram information for the metal surface shown in FIG. 4 and the metal surface shown in FIG. 5. FIG. 8 is a diagram illustrating the similarity of histograms. FIG. 9 is a diagram illustrating surface roughness measurement data after preprocessing for each local measurement region of a thin film surface, according to a second embodiment of the present disclosure. FIG. 10 is a diagram illustrating the convergence state of the similarity between the cumulative histogram information and the reference cumulative histogram information for the thin film surface shown in FIG. 9.

[0021] A method, apparatus, program, etc. for detecting (evaluating) non-uniformity in the surface roughness of an object according to the present disclosure will be described with reference to FIGS.

[0022] The embodiments of the present disclosure described below are examples for explaining the present disclosure. Various components according to the embodiments of the present disclosure, such as numerical values, shapes, materials, manufacturing processes, etc., can be modified or changed within the scope that does not cause technical problems. Furthermore, the shapes, etc. shown in the drawings of the present disclosure do not necessarily correspond to the actual shapes, etc. This is because the shapes, etc. may have been modified for the purpose of explanation.

[0023] Here, the "unevenness of surface roughness" according to the present disclosure is an index showing the state in which the surface roughness state of an object varies depending on the location (position) on the entire surface of the evaluation target, and the "unevenness of surface roughness" indicates a state in which the surface roughness state "varies" depending on the location. Therefore, even if the surface is rough, if the "roughness" is approximately the same across the entire surface of the evaluation target, it can be evaluated as having low unevenness (high uniformity), and even if the surface is extremely fine and smooth, if the degree of unevenness varies depending on the location on the surface of the evaluation target, it can be evaluated as having high unevenness (low uniformity).

[0024] In this application, the concept of "unevenness (degree of unevenness)" and "uniformity (degree of uniformity)" of surface roughness is simply referred to as "unevenness (degree of unevenness)," and in particular, in contexts focusing on one of them, it may also be simply referred to as "unevenness."

[0025] In the method, apparatus, etc. of the present disclosure, the object for which the non-uniformity of the surface roughness is to be detected may be any object. For example, it may be the surface of a concrete building / structure, an asphalt-paved road, a metal object, etc. It may also be the surface of a crystalline substrate (such as a single crystal substrate) in the field of electronic components, a metallic material, or various materials other than metal. These objects for which the non-uniformity of the method, etc. of the present disclosure is to be detected will be described below, including the significance of detecting non-uniformity.

[0026] The size of the target surface for which the non-uniformity of surface roughness is to be detected may be a relatively large area, such as several meters square, or a small area, such as several millimeters square or several centimeters square, or a very small area, such as several microns square. The shape of the target surface for which the non-uniformity of surface roughness is to be detected is not limited to a rectangle, and may be any shape.

[0027] Furthermore, there is no limitation on the degree of roughness of the surface to be measured. For example, it may be a so-called "rough" surface with visible irregularities, such as the concrete building / structure mentioned above, or it may be the surface of a polished article, or the surface of a precision material that has been subjected to a surface treatment.

[0028] 1 is a flowchart showing an example of a processing procedure of a surface roughness non-uniformity detection method according to an embodiment of the present disclosure. As shown in FIG. 1, the non-uniformity detection method according to this embodiment includes the steps of collecting data on a surface roughness measurement target (step S1), preprocessing such as flattening (step S2), calculating local histogram information (step S3), calculating cumulative histogram information (step S4), calculating the similarity between the cumulative histogram information and reference cumulative histogram information (step S5), and detecting the degree of non-uniformity (step S6).

[0029] The processing of each step will be explained below.

[0030] First, data on the amount of unevenness (height, depth) is collected from the surface of the object for which the non-uniformity of the surface roughness is to be detected (step S1). The data on the amount of unevenness is obtained by setting a plurality of local measurement areas (sometimes simply referred to as measurement areas or local areas) on the surface of the object for which the non-uniformity is to be detected (sometimes referred to as the surface of the object for evaluation), and detecting the amount of unevenness at a plurality of positions within each local area.

[0031] An example of setting (arrangement) of local measurement regions is shown in Fig. 3A. In the example shown in Fig. 3A, N local regions S are arranged substantially uniformly over the entire rectangular surface A to be subjected to non-uniformity detection and at substantially equal intervals in both the vertical and horizontal directions (x and y directions). n (n=1 to N) is set for each local region S n (n=1 to N) are all rectangular regions having the same size and a length lx in the x direction and a length ly in the y direction. n A distance L is ensured between the local regions S (n=1 to N). n The minimum distance L of the interval L between (n = 1 to N) min is, for example, a local region Sn The length may be 20 times the length of one side (length lx in the x direction or length ly in the Y direction).

[0032] Local region S n (n=1 to N) are spaced apart by a predetermined distance L min By setting the measurement points at such a distance, it is possible to detect the surface roughness and verify the similarity from measurement areas that have little positional correlation with each other on the evaluation target surface A. As a result, it is possible to more appropriately detect the distribution of feature quantities related to the evaluation target surface roughness, and to appropriately detect the non-uniformity (uniformity) of the surface roughness.

[0033] However, the local region S n The arrangement of the local regions S (n=1 to N) is not limited to the example shown in FIG. n (n=1 to N) are spaced a predetermined distance L min If the distance is greater than or equal to the local region S n (n=1 to N) may be placed at any position on the evaluation target surface A. For example, each local region S n (n=1 to N) may be randomly arranged within the evaluation target surface A. Also, for example, a local region S n (n=1 to N) may be arranged not uniformly within the evaluation target surface A, but in a range where the non-uniformity is predicted to be large. However, when detecting non-uniformity on the evaluation target surface A where the tendency of non-uniformity is not predicted, a plurality of measurement regions S n (n=1 to N) are preferably arranged uniformly over substantially the entire surface A to be evaluated.

[0034] Local region S n The number N (n=1 to N) is, for example, 100, but is not limited to 100 and may be any number. For example, when it is predicted that the surface roughness is less uneven, the local region S n The set number N (n=1 to N) may be relatively small (for example, 10), and when it is predicted that the surface roughness is largely non-uniform, the local region S n The set number N (n=1 to N) may be set to a relatively large value (for example, 1000).

[0035] Each of the local regions S nFor each local region S (n=1 to N), data on the amount of unevenness is obtained for multiple positions within the local region. n (n=1 to N) is scanned in the X and Y directions with a resolution of Px×Py, and data on the amount of unevenness is obtained for each position (sometimes called a pixel). In this embodiment, for example, the resolution in the X and Y directions is Px=1024 (pixels / lx), Py=1024 (pixels / l y). Therefore, one local region S n For each of the local regions S (n=1 to N), data on the amount of unevenness at 1024×1024 locations (pixels) is obtained. n Surface roughness measurement original data Do corresponding to (n = 1 to N) n (n=1 to N) and then subjected to the next step.

[0036] The data on the amount of unevenness may be actually measured using a surface profile measuring device, such as an atomic force microscope (AFM), a scanning probe microscope (SPM), a laser microscope, or a stylus measuring device, or the amount of unevenness may be measured from the results of cross-sectional observation using an SEM or the like.

[0037] The data on the amount of unevenness may be obtained based on already measured three-dimensional shape measurement data. n It is also possible to obtain the position information corresponding to (n=1 to N) by extracting it or by applying appropriate processing to the three-dimensional shape measurement data.

[0038] Next, the surface roughness measurement raw data Do obtained in step S1 n (n=1 to N) are subjected to pre-processing such as flattening, and the pre-processed surface shape measurement data D n (n=1 to N) is generated (step S2).

[0039] The flattening is performed by flattening each local region S of the surface A to be evaluated. n The surface roughness measurement original data Do obtained for (n = 1 to N) n (n=1 to N), the influence of the overall tilt of the evaluation target surface A is removed (offset), and each measurement area S nSurface shape measurement data D clearly showing information on the amount of unevenness (n=1 to N) n (n=1 to N).

[0040] The local region S shown in FIG. n The surface roughness measurement original data Do obtained in step S1 for (n=1 to N) n When one of the (n=1 to N) is represented by a method of displaying the amount of unevenness in an image, an image like that shown in FIG. 3B is obtained. In FIG. 3B, the amount of unevenness is represented by the color or shading of the image (in this application, the drawing is a black and white image, so shading is used), and the measurement area S n This allows intuitive understanding of the state of unevenness (n=1 to N).

[0041] In Figure 3B, the difference in shading, i.e., the difference in the amount of unevenness, appears in the left-right direction of the drawing as a whole, but the state of the amount of unevenness for each pixel does not appear in the image. For example, if the entire surface A to be evaluated is inclined, the change in the amount of unevenness due to the inclination of the entire surface A to be evaluated is superimposed on the data of the amount of unevenness for each pixel, and the weight of the amount of unevenness for each pixel becomes relatively small. Therefore, the measured data as it is (the surface roughness measurement original data Do obtained in step S1) n (n=1 to N)), the unevenness amount for each pixel may not be detected properly.

[0042] The flattening process removes the influence of the overall tilt of the evaluation target surface A from the measured data, and flattens each measurement area S n This is a process for generating data in which the unevenness information of each pixel is clearly expressed in (n=1 to N). Note that this flattening process is performed, for example, by n This can be achieved by removing long-period components of the amount of change in the amount of unevenness for (n=1 to N).

[0043] The surface roughness measurement original data Do shown as an image in FIG. n Surface shape measurement data D obtained by flattening (n=1 to N) nIf the surface shape measurement data D after flattening (pre-processing) is expressed as an image in the same manner as in FIG. 3B, it becomes the image shown in FIG. 3C. As is clear from FIG. 3C, n (n=1 to N) is each measurement area S n The unevenness information of each pixel (n=1 to N) is clearly shown.

[0044] The pre-processing step (step S2) may include other processes besides flattening. For example, the surface roughness measurement raw data Do may be affected by the measurement environment, such as the mechanical characteristics or optical characteristics of the surface shape measuring device used. n If such an influence appears (is superimposed) on the image signal (n=1 to N), it is desirable to perform a preprocessing step to eliminate such an influence.

[0045] Next, the surface shape measurement data D obtained in step S2 n (n=1 to N), the local region S n For each pixel (n=1 to N), local histogram information Rhg represents the relationship between the amount of unevenness and the frequency (number of pixels) of the amount of unevenness. n (n=1 to N) is calculated (generated) (step S3).

[0046] Specifically, the local region S n For each (n=1 to N), the local region S n Surface shape measurement data D n Based on this, the unevenness of each pixel is used as a feature value (class value), and the number of pixels having that unevenness is used as a frequency (frequency). n (n=1 to N) is generated. Next, the histogram hg n The frequency (number of pixels) (n = 1 to N) is converted to a logarithm, and then normalized based on the most frequent value of the frequency after the logarithm conversion (the maximum number of pixels for each unevenness amount), to obtain the local histogram information Rhg n (n=1 to N) is calculated.

[0047] By converting the feature values ​​(frequency, number of pixels) into logarithms, it becomes possible to perform statistical processing in which the relationship (correlation) between histograms is clearly reflected in the similarity between the histograms. In other words, the histogram formed by the feature values ​​converted into logarithms (local histogram information Rhg n By using the above formula (n=1 to N), it is possible to detect differences in the distribution of unevenness amounts as differences in the similarity between histograms.

[0048] Furthermore, by performing normalization, different local regions S n Local histogram information Rhg (n=1 to N) n When accumulating the local histogram information for each measurement region (n = 1 to N), the weights of the local histogram information for each measurement region can be made approximately equal. If normalization is not performed, the weights of the local histogram information in the accumulated histogram may differ depending on the magnitude of the absolute value of the frequency of the unevenness amount (actual frequency). However, by performing normalization, the contributions of the unevenness amount distributions (local histogram information) of multiple measurement regions can be made equal and accumulated, allowing the unevenness of the surface of the measurement object to be appropriately evaluated.

[0049] The unevenness of each pixel is used as a feature value, and a histogram is generated in which the number of pixels having that unevenness is used as a frequency. The frequency (number of pixels) is then converted to a logarithm to create a histogram hg. n An example of (n=1 to N) is shown in FIG. 3D. The histogram hg shown in FIG. n (n=1 to N) is normalized as a histogram, that is, local histogram information Rhg n (n=1 to N) results in a histogram as shown in FIG. 3E.

[0050] Next, the local histogram information Rhg obtained in step S3 n (n=1 to N), a plurality of local histogram information Rhg1, . . . , Rhg n Accumulated histogram information Rrp (n=1 to N) n (n=1 to N) is calculated (step S4).

[0051] Specifically, as shown in FIG. 3F, N pieces of local histogram information Rhg from the first to the Nth (N=100 in this embodiment) n For each number (n) from 1 to N, the first to nth local histogram information Rhg1, ..., Rhg n The accumulated results are normalized by the mode, and the accumulated histogram information Rrp corresponding to the accumulated number n is calculated. n (n=1 to N) are calculated. As a result, N pieces of cumulative histogram information Rrp1, ..., Rrp N is obtained.

[0052] The cumulative histogram information Rrp calculated in this way n (n=1 to N), all (1st to Nth) local histogram information Rhg1, ..., Rhg N Accumulated histogram information Rrp N is the reference cumulative histogram information that indicates the characteristics of the overall unevenness of the surface to be evaluated, and as will be described later, each cumulative histogram information Rrp n (n=1 to N-1) is used as a basis for calculating the similarity.

[0053] The cumulative histogram information Rrp1 with a cumulative number of 1 is the first selected local histogram information Rhg n (n=1 to N).

[0054] In addition, in FIG. 3F, the local regions S1, ..., S2 described above with reference to FIG. 3A are n , ..., S N Local histogram information Rhg1, ..., Rhg n , ..., Rhg N the cumulative histogram information Rrp n Although the order of the local histogram information accumulated to calculate (n=1 to N) is described, these do not need to be the same. That is, N pieces of accumulated histogram information Rrp n The order of the local histogram information accumulated to calculate (n=1 to N) is N pieces of local histogram information Rhg n(n=1 to N), and a predetermined number (n) of local histogram information Rhg1, ..., Rhg n may be sequentially accumulated.

[0055] Alternatively, the accumulated local histogram information Rhg n The order of (n=1 to N) is not determined, and when accumulating, the local histogram information Rhg n (n=1 to N) is selected appropriately (for example, randomly), and the cumulative histogram information Rrp n (n=1 to N) may be calculated.

[0056] Accumulative histogram information Rrp n (n=1 to N), the accumulated local histogram information Rhg n The number of (n=1 to N) is important, and different local histogram information Rhg n If selecting from (n=1 to N), the local histogram information Rhg is appropriately determined according to the number of accumulated data. n (n=1 to N) is selected. That is, the local region S n (n=1 to N) regardless of the arrangement or order. n Local histogram information Rhg corresponding to (n=1 to N) n (n=1 to N) may be accumulated sequentially.

[0057] On the other hand, a local region S that is to be preferentially accumulated n (n=1 to N), the local region S n Local histogram information Rhg (n=1 to N) n The order of the local histogram information Rhgn (n=1 to N) to be accumulated may be intentionally set so that the local region S (n=1 to N) is the target of accumulation at an early stage. n (n=1 to N) means that the characteristics related to the non-uniformity of the evaluation target surface are predicted (for example, when a range where non-uniformity is likely to occur is predicted), or when the cumulative number is small, the local area S is selected as evenly as possible from the entire evaluation target surface. n For example, if you want to select

[0058] Next, as shown in FIG. 3G, the cumulative histogram information Rrp obtained in step S4 n (n=1 to N) (among these, the cumulative histogram information Rrp generated by accumulating n=N items N is the reference cumulative histogram information) n For each of (n=1 to N-1), the reference cumulative histogram information Rrp N Similarity Hm n (n=1 to N-1) is found (step S5).

[0059] Accumulative histogram information Rrp n (n=1 to N-1) reference cumulative histogram information Rrp N Similarity Hm n (n=1 to N-1) is calculated using formula (1).

[0060]

[0061] The similarity between histograms will now be described with reference to Fig. 8. When the histogram shown in Fig. 8(A) is used as a reference, it is preferable to determine whether the histogram shown in Fig. 8(B) or the histogram shown in Fig. 8(C) is similar to the reference histogram by the area of ​​the overlap when they are superimposed on the reference histogram.

[0062] That is, when the histogram shown in Fig. 8(B) is superimposed on the reference histogram shown in Fig. 8(A), the overlapping portion is shown by the hatched area in Fig. 8(D).On the other hand, when the histogram shown in Fig. 8(C) is superimposed on the reference histogram shown in Fig. 8(A), the overlapping portion is shown by the hatched area in Fig. 8(E).

[0063] 8(D) and 8(E), it is clear that the area of ​​the shaded portion is clearly larger in Fig. 8(D), and the histogram shown in Fig. 8(B) is closer to, i.e., more similar to, the reference histogram shown in Fig. 8(A) than the histogram shown in Fig. 8(C). From this perspective, the above formula (1) is a formula for calculating the proportion of overlap with the reference histogram as the similarity between the histograms.

[0064] Next, the cumulative histogram information Rrp obtained in step S5 n (n=1 to N-1) reference cumulative histogram information Rrp N Similarity Hm n (n=1 to N-1) are sequentially judged from n=1 to n=N to see whether they conform to a predetermined standard, and a number n representing the nth piece of cumulative histogram information that is first detected as conforming to the standard is detected. Then, based on the detected number n and the number N of local histogram information accumulated to generate the reference cumulative histogram information RrpN, an index f(n, N) indicating the non-uniformity of the surface roughness of the evaluation target surface is generated and output (Step S6).

[0065] Accumulative histogram information Rrp n (n=1 to N-1) reference cumulative histogram information Rrp N Similarity Hm n (n=1 to N-1) conforms to a predetermined standard. n If satisfies the formula (2), it is determined that the similarity satisfies a predetermined standard.

[0066] Hm n ≧ Hm THL ... (2) However, Hm THL is a predetermined threshold value.

[0067] Therefore, in step S6, as shown in the flowchart of FIG. 3G, starting from the n=1th cumulative histogram information Rrp1, the nth cumulative histogram information Rrp n Reference cumulative histogram information Rrp N Similarity Hm n is the reference similarity Hm THL It is determined whether the reference similarity Hm THL If it is not equal to or greater than n, the similarity Hm n is the reference similarity Hm THL It is detected whether or not it is equal to or greater than this (step S61).

[0068] This process is repeated to obtain the similarity Hm n is the reference similarity Hm THL If a case where the similarity is equal to or greater than the first detected similarity Hm n In other words, the number "n" representing the nth number of the reference cumulative histogram information Rrp N Similarity Hm n is the reference similarity Hm THL The cumulative histogram information Rrp is n The local histogram information Rhg1, ..., Rhg n Then, the number "n" and the reference cumulative histogram information Rrp N and the number "N" of local histogram information accumulated to generate the index f(n, N), an index f(n, N) indicating the non-uniformity of the surface roughness of the surface to be evaluated is generated and output (step S63).

[0069] This number "n" is the number of N pieces of local histogram information Rhg1, ..., Rhg N The cumulative histogram information (reference cumulative histogram information) Rrp is calculated by accumulating the above values ​​to show the characteristics of the overall unevenness of the surface to be evaluated. N For n pieces of local histogram information Rhg1, ..., Rhg n The cumulative histogram information Rrp calculated by accumulating n is a number that indicates sufficient similarity.

[0070] Therefore, if n is small, a small number of local histogram information Rhg n The characteristics of the overall amount of unevenness of the surface to be evaluated can be expressed by (n = 1 to N), and it can be determined that the non-uniformity of the surface roughness is small. n Unless (n=1 to N) is used, it is not possible to express the characteristics of the overall amount of unevenness of the surface to be evaluated, and therefore it can be determined that the surface roughness is highly non-uniform.

[0071] Since this number "n" is a value that indicates how much "n" relative to N can characterize the amount of unevenness of the surface to be evaluated, the index that indicates the non-uniformity of the surface roughness of the surface to be evaluated, which is generated based on n, is a function f(n, N) based on n and N. The index that indicates the non-uniformity of the surface roughness of the surface to be evaluated expressed by f(n, N) can be, for example, n / N.

[0072] The method for detecting (evaluating) non-uniformity in surface roughness according to the present disclosure can also be implemented by a program configured to cause a computer to execute each process (step).

[0073] The surface roughness non-uniformity detection (evaluation) method according to the present disclosure can also be achieved by, for example, the device shown in Fig. 2. Fig. 2 is a block diagram showing an example of the configuration of a surface roughness non-uniformity detection device according to an embodiment of the present disclosure. As shown in Fig. 2, the surface roughness non-uniformity detection device 1 includes a calculation processing unit 10, a storage unit 20, an input I / F unit 31, and an output I / F unit 32. The calculation processing unit 10 also includes a preprocessing unit 11, a local histogram information calculation unit 12, a cumulative histogram information calculation unit 13, a similarity calculation unit 14, and a non-uniformity detection unit 15.

[0074] The preprocessing unit 11, local histogram information calculation unit 12, cumulative histogram information calculation unit 13, similarity calculation unit 14, and non-uniformity detection unit 15 of the arithmetic processing unit 10 perform processing corresponding to the preprocessing step (step S2), local histogram information calculation step (step S3), cumulative histogram information calculation step (step S4), similarity calculation step (step S5), and non-uniformity detection step (step S6) of the method according to the present disclosure described above, respectively.

[0075] The functions of each processing unit are executed by driving the arithmetic processing unit 10 based on a program stored in the storage unit 20. Data calculated / generated by the execution of processing in each processing unit is stored in the storage unit 20 as appropriate, and is referenced by other processing units, and is also output via the output I / F unit 32.

[0076] On the other hand, the processing of the surface roughness measurement raw data collection step (step S1) of the method according to the present disclosure is performed by collecting three-dimensional shape measurement data (surface roughness measurement raw data Do n (n=1 to N)) via the input I / F unit 31 and storing it in the memory unit 20. Note that when the processing in the surface roughness non-uniformity detection apparatus 1 is to detect non-uniformity in the amount of concavo-convex in a predetermined direction of an object whose shape is defined two-dimensionally as described below, the surface profile measuring apparatus 50 can measure two-dimensional shape measurement data and input it to the surface roughness non-uniformity detection apparatus 1. The functions and configuration of the surface profile measuring apparatus 50 may be determined depending on the object to be processed in the surface roughness non-uniformity detection apparatus 1.

[0077] The configuration of the surface roughness non-uniformity detection apparatus 1 is not limited to the configuration shown in Fig. 2. For example, a configuration may be adopted in which part of the processing executed by the surface roughness non-uniformity detection apparatus 1 is performed by the surface profile measuring apparatus 50. Specifically, for example, processing in the pre-processing unit 11, such as flattening of the measured data, or further processing in the local histogram calculation unit 12, may be performed by the surface profile measuring apparatus 50.

[0078] As described above, the surface roughness non-uniformity detection method and surface roughness non-uniformity detection device of this embodiment can quantify the surface roughness non-uniformity of a desired object numerically and objectively detect and evaluate it. As a result, it is possible to appropriately supply products with a uniform surface roughness. Furthermore, it is possible to compare the surface roughness non-uniformity between objects, components, and materials.

[0079] As a specific example, when grinding PZT (lead zirconate titanate), which is used as a piezoelectric material, variations in surface roughness may occur between chips within the surface, resulting in unstable characteristics. In such cases, the method and apparatus of this embodiment can detect and suppress variations in surface roughness, thereby obtaining a material with stable characteristics.

[0080] Furthermore, this embodiment is useful in the manufacture of electronic components, and makes it possible to detect the unevenness of the surface roughness of a thin film on a single crystal substrate, the unevenness of the surface roughness of a PET film sheet, the unevenness of the surface roughness of an MLCC or LTCC green sheet, the unevenness of the surface roughness of an aluminum foil, etc. This reduces the unevenness of the surface roughness of each of these members / parts, and makes it possible to reduce the variation in electrical characteristics when mass-producing electronic components, etc.

[0081] Furthermore, when measuring the contact angle of a polymeric material surface, it is necessary to determine whether or not the surface roughness is uneven. In such cases, the need for correction can be easily determined by inspecting the unevenness of the surface roughness of the material surface using the method and apparatus according to this embodiment.

[0082] Furthermore, when the non-uniformity of the surface roughness or the output of non-uniform measurement results indicates deterioration of the device's functionality, it is possible to appropriately know the device's performance and state of deterioration (durability), or whether the device is due for maintenance. For example, the replacement time for a cantilever equipped with a probe in an SPM (scanning probe microscope) has previously only been determined empirically by the operator, but now it is possible to accurately determine the replacement time for a sample with known roughness by using cumulative histogram information Rrp n By generating and comparing these data, it is possible to grasp the operating state of the cantilever, and to predict when it is time to replace the cantilever or the number of roughness measurements required until replacement.

[0083] Furthermore, this embodiment makes it possible to detect the unevenness of the surface roughness of, for example, an asphalt road surface. This makes it possible to select appropriate tires to match the unevenness of the road surface roughness, for example, during automobile races. This disclosure also makes it possible to detect the unevenness of the surface roughness of a concrete wall. This makes it possible to reduce the unevenness of the surface roughness of the concrete wall and improve the quality of the concrete wall. This embodiment also makes it possible to detect the unevenness of the surface roughness of various painted surfaces. This makes it possible to reduce the unevenness of the painted surface and improve the painting quality.

[0084] This embodiment also makes it possible to detect unevenness in the surface roughness of an oil painting. This allows, for example, the brush strokes of each artist to be evaluated. Alternatively, this embodiment also makes it possible to detect unevenness in the surface roughness of fabrics such as towels. This makes it possible to reduce unevenness in the surface roughness, thereby providing towels and other items that are comfortable for users.

[0085] The present disclosure is not limited to the above-described embodiments, and various arbitrary and suitable modifications are possible.

[0086] For example, in the above-described embodiment, a method for detecting unevenness in the roughness (amount of unevenness in the height direction) of a specific surface (two-dimensional surface) of a three-dimensional object has been disclosed. Specifically, as shown in FIG. 3A, a plurality of local measurement areas S are arranged two-dimensionally on the evaluation target surface A. n (n=1 to N), and each local region S n (n=1 to N) is further scanned in the X and Y directions at a predetermined resolution, and the local histogram information Rhg n (n=1 to N) was generated. That is, unevenness of the surface roughness was detected using unevenness amount measurement points arranged two-dimensionally.

[0087] However, for example, the method disclosed herein may be applied to detecting unevenness in the amount of unevenness in a specific direction (one axial direction) at each position in the other direction (the other axial direction) for an object defined by two significant dimensions (two axes). In this case, unevenness in the surface roughness in one dimension (the amount of unevenness in the other axial direction) may be detected based on two-dimensional shape measurement data using unevenness measurement points arranged on a straight line, i.e., arranged one-dimensionally in the specific direction (one axial direction).

[0088] Alternatively, for example, a certain physical quantity may be set as a feature quantity for a plurality of measurement points set in three dimensions, and non-uniformity of that physical quantity may be detected at the plurality of measurement points in three dimensions.

[0089] First Example: An example in which the non-uniformity of surface roughness was detected using the method according to the present disclosure will be described. First, the results of detecting the non-uniformity of two types of metal surfaces, α and β, will be described as the first example.

[0090] The members on which metal surfaces α and β are formed are both rectangular metal members with a planar shape of 100 mm square and a thickness of 10 mm. Surfaces α and β are one of the opposing 100 mm square surfaces of this rectangular metal member. Metal surface α is a surface that has been conventionally polished, while metal surface β is a surface that has been conventionally polished and further surface-treated.

[0091] The metal surfaces α and β are scanned by a scanning probe microscope (SPM) to measure their three-dimensional shape. n The arrangement of the local regions S (n=1 to N) is the same as that shown in FIG. n (n=1 to N) has a planar shape of 10 μm square, and each local region S n The interval between the local regions (n=1 to N) is 200 μm, and n The number N (n=1 to N) is 100. n (n=1 to N) is scanned in the X and Y directions at a resolution of 1024 pixels. n From (n=1 to N), data on the amount of unevenness for 1024×1024 pixels is measured.

[0092] 4A to 4C show the surface roughness measurement raw data Dα obtained by scanning the metal surface α under these conditions. 0n Surface shape measurement data Dα obtained by flattening (n=1 to N) n (n=1 to N) are shown as images. i , αS j , αS k Surface shape measurement data Dα of (i, j, i = 1 to N) i , Dα j , Dα k (i, j, i = 1 to N).

[0093] 5A to 5C show the surface roughness measurement raw data Dβ obtained by scanning the metal surface β under these conditions. 0n (n=1 to N) is subjected to flattening processing to obtain surface shape measurement data Dβ n (n=1 to N) are shown as images. i , βS j , βS k (i, j, i are each one of 1 to N) surface shape measurement data Dβ i , Dβ j , Dβ k is.

[0094] As is clear from FIG. 4, the metal surface α subjected to normal polishing alone has a local region αS shown in FIG. 4(B) and FIG. 4(C). j , αS k Surface shape measurement data Dα j , Dα k In each local region αS, a scratch-like irregularity is generated. i , αS j , αS k Surface shape measurement data Dα i , Dα j , Dα k The surface roughness of the sample is uneven.

[0095] On the other hand, as is clear from a comparison of Figures 4 and 5, compared to metal surface α, which has been subjected to normal polishing alone, metal surface β, which has been subjected to normal polishing and further surface processing, has a more uniform surface roughness and is less non-uniform.

[0096] The non-uniformity detection process according to this embodiment was performed on such metal surfaces α and β. As an example of data during the process, FIG. 6 shows a local region αS of the metal surface α. i , αS j , αS k Surface shape measurement data Dα i , Dα j , Dα k The histogram hgα obtained from i , hgα j , hgα k The histogram hg is shown in Figure 6.i , hg j , hg k is histogram information before normalization to obtain local histogram information. As is clear from FIG. 6, the surface shape measurement data Dα k The local region αS k Histogram hgα k In the local area αS, portions with large amounts of unevenness that are thought to be due to the above-mentioned scratch-like unevenness are clearly formed at a certain frequency. i , αS j Histogram hgα i , hgα j It has a different shape.

[0097] As a result of such a process of detecting non-uniformity on the metal surfaces α and β, the cumulative histogram information Rrp n (n=1 to N) n The number of (n=1 to N), i.e., the numerical value "n", and the cumulative histogram information Rrp n (n=1 to N) and reference cumulative histogram information Rrp N Similarity Hm n The relationship between the surface roughness and the surface roughness (n=1 to N) is shown in FIG. 7 for the metal surfaces α and β.

[0098] As shown in FIG. 7 , for metal surface α, which was observed to have non-uniformity in FIG. 4 , a cumulative number n approaching the maximum value of 100 was required for the similarity to converge to a value close to 1, whereas for metal surface β, which was observed to have low non-uniformity in FIG. 5 , the similarity converged to approximately 1 before the cumulative number n reached 50.

[0099] Suppose the reference similarity Hm THL = 0.95, that is, the similarity Hm n When the cumulative histogram information Rrp is 0.95 or more, n (n=1 to N) and reference cumulative histogram information Rrp N If we define the non-uniformity of the metal surface α as similar, then the non-uniformity of the metal surface β is expressed as n=37 for N=100.

[0100] In this way, the surface roughness non-uniformity detection method, detection device, etc. disclosed herein can quantify the surface roughness non-uniformity of a desired article numerically and objectively detect and evaluate it.

[0101] Second Example Next, as a second example of the present disclosure, the results of detecting the non-uniformity of the surface roughness of a thin film formed on a crystalline substrate (single crystalline substrate) will be described.

[0102] The surface roughness of the sample in Example 2 was evaluated on a thin film surface of a Pt thin film formed by sputtering on a single crystal Si substrate. The Pt thin film to be evaluated had a rectangular shape with a planar shape of 10 mm square and a thickness of 200 nm.

[0103] In the evaluation of the surface roughness, first, the three-dimensional shape of the surface portion of such a Pt thin film is measured by measuring a local measurement area S arranged on the Pt thin film in the same manner as in FIG. n (n=1 to N) are scanned with an SPM to perform measurement in the same manner as in the first embodiment. n (n=1 to N) has a planar shape of 10 μm square, and each local region S n The interval between the local regions (n=1 to N) is 200 μm, and n The number N of (n=1 to N) is 100, and each local region S n (n=1 to N) is scanned in the X and Y directions at a resolution of 1024 pixels to obtain 1024×1024 data on the amount of unevenness.

[0104] FIG. 9 shows the surface roughness measurement data D obtained by scanning the surface of a Pt thin film under these conditions and flattening the obtained raw surface roughness measurement data. n (n=1 to N) D i (i is any one of 1 to N) is expressed as an image. From the image shown in FIG. 9, the surface shape measurement data D i The local region S i It can be seen that the surface shape of the Pt thin film is substantially uniform within the film.

[0105] In the second embodiment, the local region S i Other local regions S m (m=1 to N, excluding m=i) surface shape measurement data Dm The images shown in FIG. 9 are the surface shape measurement data D i From this, it can be seen that the surface shape of the Pt thin film in the second example, i.e., the state of the surface roughness, is similar to that of the local region S n (n = 1 to N). In other words, the surface roughness of the Pt thin film to be evaluated can be observed to be approximately uniform. In other words, the non-uniformity of the surface roughness of the Pt thin film to be evaluated can be observed to be low (no non-uniformity can be observed).

[0106] The surface of such a Pt thin film was subjected to the non-uniformity detection process according to this embodiment. n (n=1 to N) to the local region S n Local histogram information Rhg for each (n = 1 to N) n (n=1 to N), and obtain a plurality of local histogram information Rhg1, ..., Rhg n Accumulated histogram information Rrp (n=1 to N) n (n=1 to N) and calculate the cumulative histogram information Rrp n For each of (n=1 to N-1), the reference cumulative histogram information Rrp N Similarity Hm n (n=1 to N-1).

[0107] As a result of such a process for detecting non-uniformity on the Pt thin film surface, the cumulative histogram information Rrp n (n=1 to N) n The cumulative degree is the number of (n=1 to N) (i.e., the number "n") and the cumulative histogram information Rrp n (n=1 to N) and reference cumulative histogram information Rrp N Similarity Hm n The relationship between (n=1 to N) is shown in FIG.

[0108] The cumulative degree n (n=1 to N) and the similarity Hm n In the relationship (n=1 to N), if the reference similarity Hm THL= 0.95, that is, the similarity Hm n When the cumulative histogram information Rrp is 0.95 or more, n (n=1 to N) and reference cumulative histogram information Rrp N are defined as similar, the non-uniformity of the Pt thin film surface is expressed as n=1 with N=100.

[0109] 9, for a surface (Pt thin film surface) whose surface shape can be observed to be approximately uniform, the method, device, etc. for detecting non-uniformity in surface roughness according to the present disclosure detects an extremely low evaluation value of n = 1 where N = 100. In this way, a sufficiently low evaluation value is detected for a surface to be evaluated that has extremely low non-uniformity, and from this perspective, the method, device, etc. for detecting non-uniformity in surface roughness according to the present disclosure can appropriately quantify the non-uniformity of the surface roughness of a desired article using numerical values ​​and objectively evaluate it.

[0110] DESCRIPTION OF SYMBOLS 1... Surface roughness non-uniformity detection device 10... Arithmetic processing unit 11... Pre-processing unit 12... Local histogram information calculation unit 13... Cumulative histogram information calculation unit 14... Similarity calculation unit 15... Non-uniformity detection unit 20... Storage unit 31... Input I / F unit 32... Output I / F unit 50... Surface shape measuring device

Claims

1. A surface roughness evaluation method comprising the steps of: obtaining local histogram information for each of a plurality of local measurement regions set on a surface to be evaluated, the local histogram information indicating the relationship between the amount of surface unevenness and frequency within the measurement region; accumulating the 1st to Nth local histogram information for each of the measurement regions, successively from the 1st to the Nth local histogram information, to generate a plurality of cumulative histogram information accumulated by accumulating n (n=1 to N) pieces of information; setting the cumulative histogram information generated by accumulating n=N pieces of local histogram information as reference cumulative histogram information, and detecting a similarity to the reference cumulative histogram information for each of the plurality of cumulative histogram information generated by successively accumulating the local histogram information from n=1 to N; and detecting whether the detected similarity satisfies a predetermined criterion, successively from n=1 to n=N, and using a number n representing the nth cumulative histogram information that first satisfies the criterion, as part of an index indicating the non-uniformity of the roughness of the surface.

2. The surface roughness evaluation method according to claim 1, wherein the plurality of measurement areas are set at least a predetermined distance apart from each other.

3. The surface roughness evaluation method according to claim 1, wherein in the step of generating local histogram information, local histogram information normalized based on the most frequent frequency is generated.

4. The surface roughness evaluation method according to claim 1, wherein in the step of generating cumulative histogram information, cumulative histogram information normalized based on the most frequent frequency is generated.

5. The surface roughness evaluation method according to claim 1, wherein in the step of using the number n representing the nth position as a part of an index showing the unevenness of the surface roughness, if the detected similarity satisfies the formula (2), it is determined that the similarity meets the predetermined criterion. n ≧Hm THL ... (2) where Hm n is the similarity between the n-th cumulative histogram information and the reference cumulative histogram information, Hm THL is a predetermined similarity threshold, 6. A surface roughness evaluation device comprising: a means for obtaining local histogram information indicating the relationship between the amount of surface unevenness and frequency within a plurality of local measurement regions set on a surface to be evaluated; a means for accumulating the 1st to Nth local histogram information for each of the measurement regions, sequentially from the 1st to the Nth, to generate a plurality of accumulated histogram information accumulated over n (n = 1 to N); a means for using the accumulated histogram information generated by accumulating n = N pieces as reference cumulative histogram information, and for detecting a similarity to the reference cumulative histogram information for each of the plurality of accumulated histogram information generated by accumulating the local histogram information sequentially from n = 1 to N; and a means for sequentially detecting whether the detected similarity complies with a predetermined criterion, from n = 1 to n = N, and using a number n representing the nth cumulative histogram information that first complies with the criterion, as a part of an index indicating the unevenness of the roughness of the surface.

7. The surface roughness evaluation device according to claim 6, wherein the plurality of measurement areas are set at least a predetermined distance apart from each other.

8. The surface roughness evaluation device according to claim 6, wherein said means for generating local histogram information generates local histogram information normalized based on the most frequent frequency.

9. The surface roughness evaluation device according to claim 6, wherein said means for generating cumulative histogram information generates cumulative histogram information normalized with respect to the most frequent frequency.

10. The surface roughness evaluation device according to claim 6, wherein the means for using the number n representing the nth position as a part of an index showing the unevenness of the surface roughness judges that the detected similarity satisfies the predetermined standard when the formula (2) is satisfied. Hm n ≧Hm THL ... (2) where Hm n is the similarity between the n-th cumulative histogram information and the reference cumulative histogram information, Hm THL is a predetermined similarity threshold, 11. A surface roughness evaluation program for causing a computer to execute the steps of: obtaining local histogram information indicating the relationship between the amount of surface unevenness and frequency within a plurality of local measurement regions set on a surface to be evaluated; accumulating the 1st to Nth local histogram information for each of the measurement regions, successively from the 1st to the Nth, to generate a plurality of accumulated histogram information accumulated over n (n = 1 to N); setting the accumulated histogram information generated by accumulating over n = N items as reference cumulative histogram information, and detecting the similarity to the reference cumulative histogram information for each of the plurality of accumulated histogram information generated by successively accumulating the local histogram information from n = 1 to N; and successively detecting whether the detected similarity satisfies a predetermined criterion, from n = 1 to n = N, and using the number n representing the nth accumulated histogram information that first satisfies the criterion, as part of an index indicating the unevenness of the roughness of the surface.

12. A storage medium storing the surface roughness evaluation program according to claim 11.

Images