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

Abstract

To evaluate unevenness of surface roughness.SOLUTION: In the present disclosure, a surface roughness evaluation method obtains local histogram information showing the relation between an amount of surface unevenness and a frequency for each local measurement region; accumulates n pieces of local histogram information starting from the first to generate multiple pieces of cumulative histogram information; with the cumulative histogram information generated by accumulating a maximum number N as reference cumulative histogram information, detects the similarity of each piece of cumulative histogram information to the reference cumulative histogram information; and uses the smallest n at which the detected similarity is equal to or larger than a predetermined threshold as a part of an index showing the unevenness of surface roughness.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a surface roughness evaluation method for evaluating non-uniformity in 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. [Background technology]

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

[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. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2017-210342 A Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present disclosure is to provide a surface roughness evaluation method, a surface roughness evaluation apparatus, 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. [Means for solving the problem]

[0006] The surface roughness evaluation method according to the present disclosure includes: determining, for each of a plurality of local measurement regions set on the surface to be evaluated, local histogram information indicating a 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 measurement region from the first to the Nth local histogram information to generate a plurality of accumulated histogram information each accumulated in n (n=1 to N) pieces; a step of determining the cumulative histogram information generated by accumulating n=N pieces of local histogram information as reference cumulative histogram information, and detecting a similarity of each of the plurality of pieces of cumulative histogram information generated by sequentially accumulating the local histogram information from n=1 to n=N to the reference cumulative histogram information; detecting whether the detected similarity satisfies a predetermined criterion from n=1 to n=N, and using a number n representing the n-th piece of cumulative histogram information that first satisfies the criterion as part of an index showing the unevenness of the surface roughness; has.

[0007] By using such a surface roughness evaluation method, the unevenness of the surface roughness of an article can be objectively quantified and evaluated. Even if the surface of an article has a fine and highly flat surface roughness locally, the surface roughness may be uneven when viewed as a whole. However, by quantitatively evaluating the unevenness of the surface roughness of an article as disclosed herein, a product with a uniform surface roughness can be appropriately supplied. In addition, when the unevenness of the surface roughness or its display shows deterioration of the function of the device, it is possible to appropriately know the performance and deterioration state (durability) of the device, or whether the device needs maintenance.

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

[0009] In addition, the surface roughness evaluation program according to the present disclosure includes determining, for each of a plurality of local measurement regions set on the surface to be evaluated, local histogram information indicating a 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 measurement region from the first to the Nth local histogram information to generate a plurality of n (n=1 to N) pieces of accumulated histogram information; a step of determining the cumulative histogram information generated by accumulating n=N pieces of local histogram information as reference cumulative histogram information, and detecting a similarity of each of the plurality of pieces of cumulative histogram information generated by sequentially accumulating the local histogram information from n=1 to n=N to the reference cumulative histogram information; detecting whether the detected similarity satisfies a predetermined criterion from n=1 to n=N, and using a number n representing the n-th piece of cumulative histogram information that first satisfies the criterion as part of an index showing the unevenness of the surface roughness; It is a program for causing a computer to execute the above.

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

[0011] With these surface roughness evaluation devices, surface roughness evaluation programs, and storage media storing the surface roughness evaluation programs, when the evaluation device is operated using these devices or the programs are executed by a computer to perform their functions, the unevenness of the surface roughness of an article can be objectively quantified and evaluated. As a result, products with uniform surface roughness can be appropriately supplied, and the performance and deterioration state (durability) of the device, or whether the device needs maintenance, can be appropriately known.

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

[0013] By setting multiple measurement areas at a specified distance or more, it is possible to detect surface roughness from measurement areas that are less related to each other in terms of position and verify the similarity. As a result, it is possible to more appropriately detect the distribution state of the feature quantities related to the surface roughness of the evaluation target, and to detect an index value that more appropriately reflects 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 frequency.

[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 the 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 distributions of the unevenness amounts (local histogram information) of multiple measurement regions are equal, and the unevenness of the surface of the measurement object can be evaluated more appropriately.

[0016] In the step of using the number n representing the nth position as part of an index showing the unevenness of the surface roughness, if the detected similarity satisfies equation (2), it may be determined that the similarity complies with a predetermined criterion. Hm n ≧ HmTHL (2) However, Hm n is the similarity between the n-th cumulative histogram information and the reference cumulative histogram information, and HmTHL is a predetermined threshold value of the similarity.

[0017] 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. [Brief description of the drawings]

[0018] [Figure 1] FIG. 1 is a flowchart showing the processing steps of a surface roughness non-uniformity detection method according to the present disclosure. [Diagram 2] FIG. 2 is a block diagram showing the configuration of a surface roughness non-uniformity detection device according to the present disclosure. [Figure 3A] FIG. 3A is a diagram for explaining a local measurement area set on a target surface for detecting the degree of surface roughness nonuniformity. [Figure 3B] FIG. 3B is a diagram for explaining the surface roughness measurement raw data obtained from a local measurement region. [Figure 3C] FIG. 3C is a diagram for explaining surface roughness measurement data resulting from pre-processing (flattening) the data shown in FIG. 3B. [Figure 3D] FIG. 3D is a diagram showing an example of a histogram of the unevenness amount for each local measurement region shown in FIG. 3A. [Figure 3E] FIG. 3E is a diagram showing an example of local histogram information obtained by normalizing the unevenness amount histogram shown in FIG. 3D. [Figure 3F] FIG. 3F is a diagram for explaining the cumulative histogram information calculation process. [Figure 3G] FIG. 3G is a diagram for explaining a process of calculating the similarity between the cumulative histogram information and the reference cumulative histogram information and comparing it with a predetermined threshold value. [Figure 4] FIG. 4 is a diagram illustrating surface roughness measurement data after pretreatment for each local measurement region of a metal surface immediately after polishing. [Diagram 5] FIG. 5 is a diagram illustrating surface roughness measurement data after pretreatment for each local measurement region of a metal surface that has been subjected to a surface treatment. [Figure 6] FIG. 6 is a diagram showing an unevenness histogram for the surface roughness measurement data shown in FIG. [Figure 7] FIG. 7 is a diagram for explaining the convergence state of the similarity between the cumulative histogram information and the reference cumulative histogram information for each of the metal surface shown in FIG. 4 and the metal surface shown in FIG. [Figure 8] FIG. 8 is a diagram for explaining the similarity of histograms. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] A method, device, program, and the like relating to detecting (evaluating) non-uniformity in the surface roughness of an object according to the present disclosure will be described with reference to FIGS. 1 to 8. FIG.

[0020] 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. In addition, the shapes, etc. shown in the drawings of the present disclosure do not necessarily match the actual shapes, etc. This is because the shapes, etc. may be modified for the purpose of explanation.

[0021] Here, the "unevenness of surface roughness" according to the present disclosure is an index showing a state in which the state of surface roughness on the surface of an object differs depending on the location (position) on the entire surface of the evaluation target, and the unevenness of the surface roughness indicates a state in which the state of surface roughness "varies" depending on the location. Therefore, even if the surface is rough, if the "roughness" is almost the same on 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 is evaluated as having high unevenness (low uniformity).

[0022] 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 side, it may also be simply referred to as "unevenness."

[0023] In the method, device, etc. of the present disclosure, the object to be detected for unevenness in surface roughness 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 crystal substrate (such as a single crystal substrate) in the field of electronic components, a metal material, or various materials not limited to metal. The objects to be detected for unevenness in the method, etc. of the present disclosure will be described later, including the significance of detecting unevenness.

[0024] The size of the target surface for detecting the non-uniformity of the surface roughness may be a relatively wide range such as several meters square, a small range such as several millimeters square or several centimeters square, or a very small range such as several μ square. The shape of the target surface for detecting the non-uniformity of the surface roughness is not limited to a rectangle and may be any shape.

[0025] In addition, the degree of roughness of the surface to be measured is not limited in any way. For example, it may be a so-called "rough" surface with visible irregularities such as the above-mentioned concrete building / structure, or it may be the surface of a polished article, or it may be the surface of a precision material that has been subjected to surface treatment.

[0026] Fig. 1 is a flowchart showing a processing procedure of an example 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 the present embodiment includes the steps of collecting data of a surface roughness measurement target (step S1), pre-processing 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).

[0027] The processing of each step will be described below.

[0028] 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 called measurement areas or local areas) on the surface of the object for which the non-uniformity is to be detected (sometimes called the surface of the object for evaluation), and detecting the amount of unevenness at a plurality of positions within each local area.

[0029] 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 surface A of a rectangular nonuniformity detection target surface A and at substantially equal intervals in both the vertical and horizontal directions (x and y directions). n (n=1 to N) is set. Each local region S n The size of each local region S (n=1 to N) is the same, and each local region S is a rectangular region having a length lx in the x direction and a length ly in the y direction. n A distance L is provided between the local regions S (n=1 to N). n Minimum distance L for interval L of (n=1~N) minFor example, in a local region S n It may be 20 times the length of one side (the length lx in the x direction or the length ly in the Y direction).

[0030] Local area S n (n=1 to N) are placed at a predetermined distance L min By setting the distances equal to or greater than this, it is possible to detect the surface roughness from measurement areas with low positional correlation on the evaluation target surface A and verify the similarity. As a result, it is possible to more appropriately detect the distribution state of the feature quantities related to the evaluation target surface roughness, and to appropriately detect the non-uniformity (uniformity) of the surface roughness.

[0031] However, in the local area 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 apart by a given distance L min If the distance is greater than or equal to the local area 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 on the evaluation target surface A. Also, for example, a local region S n (n=1 to N) may be arranged not uniformly on the evaluation target surface A, but in a large number in an area where the non-uniformity is expected to be large. However, when detecting non-uniformity on the evaluation target surface A where the tendency of non-uniformity is not expected, multiple measurement areas S n (n=1 to N) are preferably arranged uniformly over substantially the entire surface A to be evaluated.

[0032] Local area 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 expected 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 the surface roughness is expected to be 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).

[0033] Each local region S n For 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), and Py=1024 (pixels / ly). Therefore, one local region S n For (n=1 to N), data on the amount of unevenness at 1024 x 1024 locations (pixels) is obtained. The obtained data is n Surface roughness measurement source data Do corresponding to (n=1~N) n (n=1 to N) and then subjected to the next step.

[0034] The data on the amount of unevenness may be actually measured using a surface profile measuring device, such as an AFM (atomic force microscope), a SPM (scanning probe microscope), a laser microscope, or a stylus measuring device, and the amount of unevenness may be measured from the results of cross-sectional observation using a SEM or the like.

[0035] 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 performing appropriate processing on the three-dimensional shape measurement data.

[0036] Next, the surface roughness measurement original data Do obtained in step S1 n (n=1 to N) are subjected to pre-processing such as flattening, and the surface shape measurement data after pre-processing D n (n=1 to N) is generated (step S2).

[0037] The flattening is performed on each local area S of the evaluation target surface A.n The surface roughness measurement data Do obtained for (n=1 to N) n (n=1 to N), the effect of the overall inclination of the evaluation target surface A is removed (offset), and each measurement area S n Surface shape measurement data D that clearly shows the amount of unevenness (n=1~N) n This is the process of generating (n=1 to N).

[0038] The local region S shown in FIG. n For (n=1 to N), the surface roughness measurement original data Do obtained in step S1 n When one of the measurement areas S (n=1 to N) is represented by a method of displaying the amount of unevenness as an image, an image as shown in FIG. 3B is obtained. FIG. 3B shows the amount of unevenness represented by the color or shading of the image (in this application, the drawing is a black and white image, so it is shading). n This allows the user to intuitively grasp the state of the unevenness (n=1 to N).

[0039] In FIG. 3B, the difference in shading, that is, 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 is not shown in the image. For example, when the entire surface A to be evaluated is inclined, the unevenness amount data for each pixel is superimposed with the change in the amount of unevenness caused by the overall inclination of the surface A to be evaluated, and the weight of the amount of unevenness for each pixel is relatively small, so the measured data as it is (the surface roughness measurement original data Do obtained in step S1) is not displayed. n (n=1 to N)), the unevenness amount for each pixel may not be detected properly.

[0040] Flattening removes the effect of the overall inclination 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 unevenness information of each pixel is clearly expressed in (n=1 to N). Note that this flattening process is, for example, a process for generating data of the measured surface roughness measurement original data Do n This can be achieved by, for example, removing long-period components of the amount of change in unevenness for (n=1 to N).

[0041] The surface roughness measurement original data Do shown as an image in FIG. 3B n Surface shape measurement data D obtained by flattening (n=1 to N) n If the surface shape measurement data D (n=1 to N) is expressed as an image in the same manner as in FIG. 3B, it becomes the image in FIG. 3C. As is clear from FIG. 3C, the surface shape measurement data D n (n=1 to N) is each measurement area S n The unevenness information for each pixel (n=1 to N) is clearly shown.

[0042] As the processing in the pre-processing step (step S2), processing other than flattening may be performed. For example, some measurement environment such as mechanical characteristics and optical characteristics of the surface shape measuring device used may affect the surface roughness measurement raw data Do. n If such an effect appears (is superimposed) in (n=1 to N), it is desirable to perform a process to eliminate such an effect as a preprocessing.

[0043] Next, the surface shape measurement data D obtained in step S2 n Based on (n=1 to N), local region S n For each (n = 1 to N), the 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).

[0044] 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 treated as a feature value (class value), and the number of pixels having that unevenness is used as a frequency (frequency). n (n=1~N). Next, generate 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 frequency (the maximum number of pixels for each unevenness amount) after the logarithm conversion. The local histogram information Rhg n Calculate (n=1 to N).

[0045] By converting the feature values ​​(frequency, number of pixels) to logarithms, it becomes possible to perform statistical processing in which the relationship (correlation) between histograms is prominently expressed in the similarity between the histograms. In other words, the histogram formed by the feature values ​​converted to logarithms (local histogram information Rhg n (n=1 to N)), it is possible to detect differences in distribution of unevenness amounts as differences in the degree of similarity between histograms.

[0046] In addition, by performing normalization, different local regions S n Local histogram information Rhg for (n=1~N) n When performing a process of accumulating (n=1 to N), the weight of the local histogram information for each measurement region can be made approximately equal. If normalization is not performed, the weight of each piece of local histogram information in the accumulated histogram may differ depending on the magnitude of the absolute value (actual frequency) of the unevenness amount. However, by performing normalization, the contributions of the unevenness amount distribution (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.

[0047] The unevenness of each pixel is used as a feature value, and a histogram is generated by counting the number of pixels with that unevenness as a frequency. The frequency (number of pixels) is then converted to a logarithm to give the histogram hg. n An example of (n=1 to N) is shown in FIG. 3D. Also, the histogram hg n (n=1 to N) normalized histogram, i.e., local histogram information Rhg n (n=1 to N) will result in a histogram as shown in Fig. 3E. In Fig. 3E, the characteristic value is shown with the most frequent amount of unevenness set to 0.

[0048] Next, the local histogram information Rhg obtained in step S3 n Based on (n=1 to N), multiple local histogram information Rhg1, ..., Rhg nAccumulated histogram information Rrp of (n=1~N) n (n=1 to N) is calculated (step S4).

[0049] 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=1 to N), the 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). As a result, N pieces of cumulative histogram information Rrp1, ..., Rrp N is obtained.

[0050] The cumulative histogram information Rrp calculated in this way n Of (n=1 to N), all (1st to Nth) local histogram information Rhg1, ..., Rhg N The cumulative histogram information Rrp N is the reference cumulative histogram information indicating the characteristics of the overall unevenness of the surface to be evaluated, and as will be described later, each cumulative histogram information Rrp n It is used as the basis for calculating the similarity with (n=1 to N-1).

[0051] The cumulative histogram information Rrp1 with a cumulative number of 1 is the first selected local histogram information Rhg n (n=1~N).

[0052] In addition, in FIG. 3F, the local regions S1, . . . , S n ,…,S N The local histogram information Rhg1,…,Rhg n ,…,Rhg N The cumulative histogram information Rrp nAlthough the order of the local histogram information accumulated to calculate (n=1 to N) is described above, these do not have 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 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.

[0053] Alternatively, the accumulated local histogram information Rhg n The order of (n=1 to N) is not decided, and when accumulating, the local histogram information Rhg n (n=1 to N) is appropriately selected (for example, randomly), and the cumulative histogram information Rrp n (n=1 to N) may be calculated.

[0054] Accumulative histogram information Rrp n (n=1 to N), the accumulated local histogram information Rhg n The number of (n=1~N) is important, and the different local histogram information Rhg n If you select from (n=1 to N), the local histogram information Rhg n In other words, the arrangement and order of the local regions S1 (n=1 to N) described above with reference to FIG. 3A do not matter, and any local region S n Local histogram information Rhg corresponding to (n=1 to N) n (n=1 to N) may be accumulated sequentially.

[0055] On the other hand, the local area S that is to be preferentially accumulated n If there are (n=1~N), the local region S n Local histogram information Rhg for (n=1~N) nThe 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) to be accumulated first. n Examples of cases where (n = 1 to N) are when characteristics related to the unevenness of the surface to be evaluated are predicted (for example, when an area where unevenness is likely to occur is predicted), or when it is desirable to select local regions Sn as evenly as possible from the entire surface to be evaluated even when the cumulative number is small.

[0056] Next, as shown in FIG. 3G, the cumulative histogram information Rrp n (n=1 to N) (Of these, the cumulative histogram information Rrp generated by accumulating n=N items N is the reference cumulative histogram information) based on 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) is found (step S5).

[0057] Accumulative histogram information Rrp n (n=1~N-1) Reference cumulative histogram information Rrp N Similarity Hm n (n=1 to N-1) is calculated using formula (1).

[0058]

number

[0059] The similarity between the histograms will now be explained with reference to FIG. When the histogram shown in FIG. 8(A) is used as a reference, it is preferable to determine which of the histograms shown in FIG. 8(B) and FIG. 8(C) is more similar to the reference histogram by the area of ​​overlap when they are superimposed on the reference histogram.

[0060] 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 diagonal lines 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 diagonal lines in Fig. 8(E).

[0061] 8(D) and 8(E), the area of ​​the shaded portion is obviously larger in Fig. 8(D), and the histogram shown in Fig. 8(B) is closer to the reference histogram shown in Fig. 8(A) than the histogram shown in Fig. 8(C), i.e., it is more similar. From this perspective, the above formula (1) is a formula for calculating the proportion of overlap with the reference histogram as the similarity between histograms.

[0062] Next, the cumulative histogram information Rrp obtained in step S5 n (n=1~N-1) Reference cumulative histogram information Rrp N Similarity Hm n (n=1 to N-1) are successively judged from n=1 to n=N to see whether they conform to a predetermined criterion, and a number n representing the nth piece of cumulative histogram information first detected as conforming to the criterion 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 unevenness of the surface roughness of the evaluation target surface is generated and output (Step S6).

[0063] Accumulative histogram information Rrp n (n=1~N-1) Reference cumulative histogram information Rrp N Similarity Hm n (n=1 to N-1) conforms to the specified criteria using the similarity Hm n If satisfies the formula (2), it is determined that the similarity meets a predetermined criterion.

[0064] Hm n ≧ Hm THL (2) However, Hm THL is a predetermined threshold value.

[0065] Therefore, in step S6, for example, 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 standard similarity Hm THL It is detected whether or not the reference similarity Hm THL If it is not equal to or greater than n, the similarity Hm n is the standard similarity Hm THL It is detected whether or not it is equal to or greater than this (step S61).

[0066] This process is repeated to obtain the similarity Hm n is the standard similarity Hm THL If a case where the similarity is equal to or larger than the first detected similarity Hm n In other words, the number "n" representing the nth item of the reference cumulative histogram information Rrp N Similarity Hm n is the standard similarity Hm THL The cumulative histogram information Rrp is equal to or greater than this n The local histogram information Rhg1,…,Rhg n The number "n" of the reference cumulative histogram information Rrp N Based on the number "N" of pieces 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 evaluation target surface is generated and output (step S63).

[0067] 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 used to indicate the overall unevenness characteristics of the surface to be evaluated by accumulating the above. N For n local histograms Rhg1,…,Rhgn The cumulative histogram information Rrp is calculated by accumulating n is a number that indicates sufficient similarity.

[0068] Therefore, if n is small, the number of local histograms Rhg n The overall unevenness of the surface to be evaluated can be expressed by (n = 1 to N), and it can be determined that the surface roughness is not uniform. In addition, if n is large, a large number of local histogram information Rhg n If n=1 to N is not used, it is not possible to express the characteristics of the overall amount of unevenness on the surface being evaluated, and therefore it can be determined that the surface roughness is largely non-uniform.

[0069] Since this number "n" is a value that indicates how much "n" can express the characteristics of the unevenness of the evaluation target surface with respect to N, an index that indicates the unevenness of the surface roughness of the evaluation target surface generated based on n will be a function f(n,N) based on n and N. The index that indicates the unevenness of the surface roughness of the evaluation target surface expressed by f(n,N) can be, for example, n / N.

[0070] Such a method for detecting (evaluating) unevenness 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).

[0071] The surface roughness non-uniformity detection (evaluation) method according to the present disclosure can also be achieved by, for example, an apparatus shown in Fig. 2. Fig. 2 is a block diagram showing an example of a configuration of a surface roughness non-uniformity detection apparatus according to an embodiment of the present disclosure. As shown in Fig. 2, the surface roughness non-uniformity detection apparatus 1 has 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 has a pre-processing 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.

[0072] The preprocessing unit 11, the local histogram information calculation unit 12, the cumulative histogram information calculation unit 13, the similarity calculation unit 14 and the non-uniformity detection unit 15 of the arithmetic processing unit 10 respectively perform processes corresponding to the preprocessing step (step S2), the local histogram information calculation step (step S3), the cumulative histogram information calculation step (step S4), the similarity calculation step (step S5) and the non-uniformity detection step (step S6) of the method according to the present disclosure described above.

[0073] 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. Each piece of data calculated / generated by the execution of processing in each processing unit is appropriately stored in the storage unit 20, referred to by other processing units, and output via the output I / F unit 32.

[0074] On the other hand, the process of the surface roughness measurement raw data collection step (step S1) of the method according to the present disclosure uses three-dimensional shape measurement data (surface roughness measurement raw data Do) measured by a surface shape measuring device 50 which is a device separate from the surface roughness nonuniformity detection device 1. n (n=1 to N)) via the input I / F unit 31 and stored in the memory unit 20. 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 function 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.

[0075] The configuration of the surface roughness non-uniformity detection apparatus 1 is not limited to the configuration in Fig. 2. For example, a part of the processing executed by the surface roughness non-uniformity detection apparatus 1 may be performed by the surface profile measuring apparatus 50. Specifically, for example, the processing in the pre-processing unit 11 such as flattening the measured data, or further the processing in the local histogram calculation unit 12, may be performed by the surface profile measuring apparatus 50.

[0076] As described above, according to the surface roughness non-uniformity method and the surface roughness non-uniformity detection device of the present embodiment, the non-uniformity of the surface roughness of a desired object can be quantitatively detected and evaluated objectively by using numerical values. As a result, a product having a uniform surface roughness can be appropriately supplied. In addition, it becomes possible to compare the non-uniformity of the surface roughness between objects, members, and materials.

[0077] As a specific example, when grinding PZT (lead zirconate titanate), which is used as a piezoelectric material, surface roughness variations may occur between chips within the surface, resulting in unstable characteristics. In such cases, the method and device of this embodiment can detect and suppress the surface roughness variations, thereby obtaining a material with stable characteristics.

[0078] Furthermore, this embodiment is related to 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 a green sheet of MLCC or LTCC, the unevenness of the surface roughness of an aluminum foil, etc. This makes it possible to reduce the unevenness of the surface roughness of each of these members / parts, and to reduce the variation in electrical characteristics when mass-producing electronic components, etc.

[0079] In addition, when measuring the contact angle of a polymeric material surface, it is necessary to determine whether or not to correct the contact angle depending on whether the surface roughness is uneven. In such a case, the need for correction can be easily determined by inspecting the unevenness of the surface roughness of the material surface using the method and device according to the present embodiment.

[0080] In addition, when the deterioration of the equipment's functions appears in the output of non-uniform surface roughness or non-uniform measurement results, it is possible to appropriately know the equipment's performance and deterioration state (durability), or whether the equipment is due for maintenance. For example, until now, the only way to determine when to replace a cantilever equipped with a SPM (scanning probe microscope) probe was for the operator to do so empirically, but now it is possible to determine when to replace a cantilever equipped with a SPM (scanning probe microscope) probe by using cumulative histogram information Rrp nBy 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 how many sheets of roughness should be measured before it needs to be replaced.

[0081] In addition, 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 an appropriate tire that matches the unevenness of the road surface roughness, for example, during an automobile race. In addition, this disclosure 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, for example, a concrete wall, and improve the quality of the concrete wall. In addition, this embodiment 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.

[0082] This embodiment also makes it possible to detect unevenness in the surface roughness of an oil painting. This makes it possible to evaluate, for example, the brush strokes of each painter. Alternatively, this embodiment also makes it possible to detect unevenness in the surface roughness of a fabric such as a towel. This makes it possible to reduce unevenness in the unevenness (surface roughness) and provide towels and the like that are comfortable for users.

[0083] The present disclosure is not limited to the above-described embodiment, and various suitable modifications may be made. Change is possible.

[0084] For example, in the above-described embodiment, a method for detecting unevenness in 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 XY direction at a predetermined resolution, and the local histogram information Rhg n(n = 1 to N) was generated. In other words, unevenness in surface roughness was detected using unevenness measurement points arranged two-dimensionally.

[0085] However, for example, the method etc. 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 in two dimensions (two axes) as significant dimensions. In this case, unevenness in the surface roughness in one dimension (unevenness in the other axial direction) may be detected using unevenness measurement points arranged on a straight line, i.e., arranged one-dimensionally in a specific direction (one axial direction), based on two-dimensional shape measurement data.

[0086] 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 in the physical quantity may be detected at the plurality of measurement points in three dimensions.

[0087] Working Example An example in which the method according to the present disclosure is used to detect unevenness in surface roughness will be described below. Here, the results of detecting unevenness on two types of metal surfaces, α and β, will be described.

[0088] The components on which metal surfaces α and β are formed are both rectangular metal components 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 component. Metal surface α is a surface that has been normally polished, while metal surface β is a surface that has been conventionally polished and further surface-treated.

[0089] Such metal surfaces α and β are scanned by a scanning probe microscope (SPM) to measure their three-dimensional shape. n The arrangement of (n=1 to N) is the same as that shown in FIG. 3A. n The planar shape of each local region S (n=1 to N) is 10 μm square. nThe interval between (n = 1 to N) is 200 μm, and the local region S n The number N (n=1 to N) is 100. n (n=1 to N) is scanned in the X and Y directions with a resolution of 1024 pixels. n From (n=1 to N), data on the amount of unevenness for 1024×1024 pixels is measured.

[0090] Figures 4(A) to 4(C) show the surface roughness measurement data Dαo obtained by scanning the metal surface α under these conditions. n Surface shape measurement data Dα obtained by flattening (n=1 to N) n (n=1 to N) are represented as images. Figures 4(A) to 4(C) respectively show surface shape measurement data Dαi, Dαj, and Dαk (i, j, i=1 to N) of local regions αSi, αSj, and αSk (i, j, i=1 to N) of the metal surface α.

[0091] Also, Fig. 5(A) to Fig. 5(C) show the surface roughness measurement raw data Dβo obtained by scanning the metal surface β under such conditions. n Surface shape measurement data Dβ obtained by flattening (n=1 to N) n 5(A) to 5(C) show surface shape measurement data Dβi, Dβj, and Dβk (i, j, i = 1 to N) of local regions βSi, βSj, and βSk (i, j, i = 1 to N) of the metal surface β, respectively.

[0092] As is clear from Figure 4, on the metal surface α which has been subjected to only normal polishing, scratch-like irregularities have appeared in the surface shape measurement data Dαj, Dαk of the local regions αSj, αSk shown in Figures 4(B) and 4(C), and the surface roughness of the surface shape measurement data Dαi, Dαj, Dαk of each local region αSi, αSj, αSk is uneven.

[0093] 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 only, metal surface β, which has been subjected to surface processing in addition to normal polishing, has a more uniform surface roughness and is less non-uniform.

[0094] The non-uniformity detection process according to the present embodiment was performed on such metal surfaces α and β. As an example of data during processing, histograms hgαi, hgαj, and hgαk obtained from surface shape measurement data Dαi, Dαj, and Dαk of local regions αSi, αSj, and αSk of the metal surface α are shown in FIG. 6. The histograms hgi, hgj, and hgk are histogram information before normalization to obtain local histogram information. As is clear from FIG. 6, in the histogram hgαk of the local region αSk showing the surface shape measurement data Dαk in FIG. 4(C), a portion with a large amount of unevenness that is thought to be due to the above-mentioned scratch-like unevenness is clearly formed at a certain frequency, and has a different shape from the histograms hgαi and hgαj of the other local regions αSi and αSj.

[0095] As a result of such a process of detecting non-uniformity on the metal surfaces α and β, the cumulative histogram information Rrp n Accumulated local histogram information Rhg to generate (n=1~N) n The number of (n=1 to N), i.e., the number "n", and the cumulative histogram information Rrp n (n=1~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.

[0096] 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 is required for the similarity to converge to close to 1, whereas for metal surface β, which was observed to have low non-uniformity in FIG. 5, the similarity converges to approximately 1 before the cumulative number n reaches 50.

[0097] Suppose the similarity Hm THL = 0.95, that is, the similarity Hmn If Rrp is 0.95 or more, the cumulative histogram information n (n=1~N) and reference cumulative histogram information Rrp N are defined as similar, the non-uniformity of the metal surface α is expressed as n=87 for N=100, while the non-uniformity of the metal surface β is expressed as n=37 for N=100.

[0098] In this way, according to the surface roughness non-uniformity detection method, detection device, etc. disclosed herein, the surface roughness non-uniformity of a desired article can be objectively detected and evaluated by quantifying it numerically. [Explanation of symbols]

[0099] 1. Surface roughness non-uniformity detection device 10...Computation processing unit 11...Pretreatment section 12...Local histogram information calculation unit 13...Cumulative histogram information calculation unit 14…Similarity calculation unit 15…Non-uniformity detector 20...Storage section 31...Input I / F section 32...Output I / F section 50...Surface shape measuring device

Claims

1. determining, for each of a plurality of local measurement regions set on the surface to be evaluated, local histogram information indicating a 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 measurement region from the first to the Nth local histogram information to generate a plurality of n (n=1 to N) pieces of accumulated histogram information; a step of determining the cumulative histogram information generated by accumulating n=N pieces of local histogram information as reference cumulative histogram information, and detecting a similarity of each of a plurality of pieces of cumulative histogram information generated by sequentially accumulating the local histogram information from n=1 to n=N, to the reference cumulative histogram information; detecting whether the detected similarity satisfies a predetermined criterion from n=1 to n=N, and using a number n representing the n-th cumulative histogram information that first satisfies the criterion as a part of an index showing the unevenness of the surface roughness; The surface roughness evaluation method has the following features.

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

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

4. 2. The surface roughness evaluation method of claim 1, wherein in the step of using the number n representing the nth position as part of an index indicating the unevenness of the surface roughness, the detected similarity is judged to comply with the specified criterion if it satisfies equation (2). Hm n ≧ Hm THL ・・・(2) However, 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,

5. a means for determining, for each of a plurality of local measurement regions set on a surface to be evaluated, local histogram information indicating a relationship between the amount of unevenness and the frequency of the surface in the measurement region; a means for accumulating the first to Nth pieces of local histogram information for each of the measurement regions sequentially from the first to the Nth pieces to generate a plurality of n (n=1 to N) pieces of accumulated histogram information; a means for determining, as reference cumulative histogram information, the cumulative histogram information generated by accumulating n=N pieces of local histogram information, and for each of a plurality of pieces of cumulative histogram information generated by sequentially accumulating the local histogram information from n=1 to n=N, detecting a similarity to the reference cumulative histogram information; a means for successively detecting whether the detected similarity satisfies a predetermined criterion from n=1 to n=N, and using a number n representing the n-th cumulative histogram information that first satisfies the criterion as a part of an index showing unevenness of the surface roughness; A surface roughness evaluation device having the following structure.

6. 6. The surface roughness evaluation device according to claim 5, wherein the plurality of measurement regions are set apart from one another by a predetermined distance or more.

7. 6. The surface roughness evaluation device according to claim 5, wherein said means for generating local histogram information generates local histogram information normalized with respect to the most frequent frequency.

8. The surface roughness evaluation device described in claim 5, wherein the means for using the number n representing the nth position as part of an index indicating the unevenness of the surface roughness judges that the detected similarity complies with the specified standard if the detected similarity satisfies equation (2). Hm n ≧ Hm THL ・・・(2) However, 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,

9. determining, for each of a plurality of local measurement regions set on the surface to be evaluated, local histogram information indicating a 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 measurement region from the first to the Nth local histogram information to generate a plurality of n (n=1 to N) pieces of accumulated histogram information; a step of determining the cumulative histogram information generated by accumulating n=N pieces of local histogram information as reference cumulative histogram information, and detecting a similarity of each of a plurality of pieces of cumulative histogram information generated by sequentially accumulating the local histogram information from n=1 to n=N, to the reference cumulative histogram information; detecting whether the detected similarity satisfies a predetermined criterion from n=1 to n=N, and using a number n representing the n-th cumulative histogram information that first satisfies the criterion as a part of an index showing the unevenness of the surface roughness; A surface roughness evaluation program for running the above on a computer.

10. A storage medium storing the surface roughness evaluation program according to claim 9.

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