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. Hmn ≧ HmTHL (2) Here, Hmn 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 Sn (n=1 to N) are set substantially uniformly over the entire surface A of a rectangular nonuniformity detection target surface A and at substantially equal intervals in each of the vertical and horizontal directions (x and y directions). The size of each local region Sn (n=1 to N) is the same, and each local region Sn (n=1 to N) is a rectangular region having an x-direction length lx and a Y-direction length ly. A distance L is secured between each local region Sn (n=1 to N). The minimum distance Lmin of the distance L between the local regions Sn (n=1 to N) may be, for example, 20 times the length of one side of the local region Sn (x-direction length lx or Y-direction length ly).

[0030] By setting each of the local regions Sn (n=1 to N) at a distance equal to or greater than a predetermined distance Lmin from each other, it is possible to detect the surface roughness from measurement regions that have little positional correlation with each other on the evaluation target surface A, and to 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, the arrangement of the local regions Sn (n=1 to N) is not limited to the example of FIG. 3A. As long as the local regions Sn (n=1 to N) are separated from each other by a predetermined distance Lmin or more, the local regions Sn (n=1 to N) may be arranged at any position on the evaluation target surface A. For example, the local regions Sn (n=1 to N) may be arranged randomly on the evaluation target surface A. Also, for example, the local regions Sn (n=1 to N) may not be arranged uniformly on the evaluation target surface A, but may be arranged in large numbers in an area where non-uniformity is predicted to be large. However, when detecting non-uniformity on the evaluation target surface A where no tendency of non-uniformity is predicted, it is preferable to arrange multiple measurement regions Sn (n=1 to N) uniformly over approximately the entire surface of the evaluation target surface A.

[0032] The number N of local regions Sn (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 there is little non-uniformity in the surface roughness, the set number N of local regions Sn (n=1 to N) may be set relatively small (for example, 10, etc.), and when it is predicted that there is great non-uniformity in the surface roughness, the set number N of local regions Sn (n=1 to N) may be set relatively large (for example, 1000, etc.).

[0033] For each of the local regions Sn (n=1 to N) thus set, data on the amount of unevenness at multiple positions within the local region is obtained. As shown in FIG. 3A, one local region Sn (n=1 to N) is scanned in the X direction and the Y direction with a resolution of Px×Py, respectively, and data on the amount of unevenness at each position (sometimes called pixel) is obtained. In this embodiment, for example, the resolution in the X direction and the Y direction is Px=1024 (pixels / lx), Py=1024 (pixels / ly). Therefore, data on the amount of unevenness at 1024×1024 positions (pixels) is obtained for one local region Sn (n=1 to N). The obtained data is provided to the next process as surface roughness measurement original data Don (n=1 to N) corresponding to each local region Sn (n=1 to N).

[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 unevenness amount data may be obtained based on already measured three-dimensional shape measurement data, or may be obtained by extracting information on a position corresponding to a local region Sn (n=1 to N) from the already measured three-dimensional shape measurement data, or by performing appropriate processing on the three-dimensional shape measurement data.

[0036] Next, the surface roughness measurement raw data Don (n=1 to N) obtained in step S1 is subjected to preprocessing such as flattening to generate preprocessed surface shape measurement data Dn (n=1 to N) (step S2).

[0037] Flattening is a process in which the effects of the overall inclination of the surface A to be evaluated are removed (offset) from the original surface roughness measurement data Don (n = 1 to N) obtained for each local region Sn (n = 1 to N) of the surface A to be evaluated, and surface shape measurement data Dn (n = 1 to N) is generated in which information on the amount of unevenness in each measurement region Sn (n = 1 to N) is clearly represented.

[0038] When one of the surface roughness measurement original data Don (n = 1 to N) obtained in step S1 for the local area Sn (n = 1 to N) shown in Fig. 3A is represented by a method for displaying the amount of unevenness as an image, an image like that shown in Fig. 3B is obtained. Fig. 3B shows the amount of unevenness in the measurement area Sn (n = 1 to N) intuitively by representing the amount of unevenness as the color or shading of the image (in this application, the drawing is a black and white image, so shading is used).

[0039] In Fig. 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, 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 becomes relatively small, so the amount of unevenness for each pixel may not be properly detected from the measured data as it is (the surface roughness measurement raw data Don (n = 1 to N) obtained in step S1).

[0040] Flattening is a process for removing the influence of the overall inclination of the evaluation target surface A from the measured data, and generating data in which the unevenness amount information of each pixel in each measurement region Sn (n=1 to N) is clearly expressed. Note that this flattening process can be performed, for example, by a method such as removing the long-period components of the amount of change in unevenness from the measured original surface roughness measurement data Don (n=1 to N).

[0041] When the surface shape measurement data Dn (n = 1 to N) obtained by flattening the original surface roughness measurement data Don (n = 1 to N) shown as an image in Fig. 3B is represented 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 Dn (n = 1 to N) after flattening (pre-processing) clearly shows the unevenness amount information for each pixel in each measurement area Sn (n = 1 to N).

[0042] The pre-processing step (step S2) may include processing other than flattening. For example, if some measurement environment such as the mechanical characteristics or optical characteristics of the surface profile measuring device used is present (superimposed) in the surface roughness measurement raw data Don (n=1 to N), it is desirable to perform a pre-processing step to eliminate such influences.

[0043] Next, based on the surface shape measurement data Dn (n = 1 to N) obtained in step S2, local histogram information Rhgn (n = 1 to N) that represents the relationship between the amount of unevenness and the frequency (number of pixels) of the amount of unevenness is calculated (generated) for each local region Sn (n = 1 to N) (step S3).

[0044] Specifically, for each local region Sn (n=1 to N), a histogram hgn (n=1 to N) is generated based on the surface shape measurement data Dn of that local region Sn, with the unevenness of each pixel being the feature value (class value) and the number of pixels having that unevenness being the degree (frequency). Next, the frequency (number of pixels) of the histogram hgn (n=1 to N) is converted to a logarithm, and then normalized based on the mode of the frequency after the logarithm conversion (the maximum number of pixels for each unevenness amount) to calculate local histogram information Rhgn (n=1 to N).

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

[0046] Furthermore, by performing normalization, when the local histogram information Rhgn (n=1 to N) of different local regions Sn (n=1 to N) is accumulated in the next process, the weights of the local histogram information for each measurement region can be made substantially equal. If normalization is not performed, the weights of the local histogram information in the accumulated histogram may differ depending on the absolute value (actual frequency) of the 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 equalized and accumulated, allowing the unevenness of the surface of the measurement object to be appropriately evaluated.

[0047] An example of a histogram hgn (n=1 to N) is shown in Fig. 3D, which is generated by taking the unevenness of each pixel as a feature value and the number of pixels having that unevenness as a frequency, and then converting the frequency (number of pixels) into a logarithm. Also, a histogram obtained by normalizing the histogram hgn (n=1 to N) shown in Fig. 3D, i.e., local histogram information Rhgn (n=1 to N), becomes a histogram as shown in Fig. 3E. Note that in Fig. 3E, the characteristic value is shown as 0 for the most frequent unevenness.

[0048] Next, based on the local histogram information Rhgn (n=1 to N) obtained in step S3, accumulated histogram information Rrpn (n=1 to N) is calculated by accumulating the multiple pieces of local histogram information Rhg1, . . . , Rhgn (n=1 to N) (step S4).

[0049] 3F, for N pieces of local histogram information Rhgn (n=1 to N) from 1 to N (N=100 in this embodiment), the 1st to nth pieces of local histogram information Rhg1, ..., Rhgn are accumulated for each number (n) from 1 to N, and the accumulated result is normalized by the mode to calculate accumulated histogram information Rrpn (n=1 to N) corresponding to the accumulated number n. In this way, N pieces of accumulated histogram information Rrp1, ..., RrpN are obtained.

[0050] Of the cumulative histogram information Rrpn (n = 1 to N) calculated in this manner, the cumulative histogram information RrpN obtained by accumulating all (1st to Nth) local histogram information Rhg1, ..., RhgN is used as reference cumulative histogram information that indicates the characteristics of the overall unevenness of the surface to be evaluated, and is used as the basis for calculating the similarity with each cumulative histogram information Rrpn (n = 1 to N-1), as described below.

[0051] Incidentally, the cumulative histogram information Rrp1 with a cumulative number of 1 is the same as the first selected piece of local histogram information Rhgn (n=1 to N).

[0052] 3F, the local histogram information Rhg1, ..., Rhgn, ..., RhgN corresponding to the local regions S1, ..., Sn, ..., SN described above with reference to FIG. 3A is described as the order of the local histogram information accumulated to calculate the cumulative histogram information Rrpn (n = 1 to N), but these do not need to be the same. In other words, the order of the local histogram information accumulated to calculate the N pieces of cumulative histogram information Rrpn (n = 1 to N) may be such that a new order is separately set for the N pieces of local histogram information Rhgn (n = 1 to N), and a predetermined number (n pieces) of local histogram information Rhg1, ..., Rhgn are sequentially accumulated in the newly set order.

[0053] Alternatively, the order of the accumulated local histogram information Rhgn (n = 1 to N) may not be decided, and when accumulating, the number of pieces of local histogram information Rhgn (n = 1 to N) to be accumulated may be appropriately selected (for example, randomly) to calculate the accumulated histogram information Rrpn (n = 1 to N).

[0054] In calculating the cumulative histogram information Rrpn(n=1 to N), the number of accumulated local histogram information Rhgn(n=1 to N) is important, and if selecting from different local histogram information Rhgn(n=1 to N), appropriate local histogram information Rhgn(n=1 to N) may be selected according to the number to be accumulated. In other words, the local histogram information Rhgn(n=1 to N) corresponding to any local region Sn(n=1 to N) may be accumulated sequentially, regardless of the arrangement or order of the local regions S1(n=1 to N) described above with reference to Fig. 3A.

[0055] On the other hand, when there is a local region Sn (n=1 to N) that is to be preferentially accumulated, the order of the accumulated local histogram information Rhgn (n=1 to N) may be intentionally set so that the local histogram information Rhgn (n=1 to N) of that local region Sn (n=1 to N) is accumulated early. Examples of cases where there is a local region Sn (n=1 to N) that is to be preferentially accumulated include cases where characteristics related to the non-uniformity of the evaluation target surface are predicted (for example, cases where a range where non-uniformity is likely to occur is predicted), and cases where it is desired to select local regions Sn as evenly as possible from the entire evaluation target surface even when the accumulated number is small.

[0056] Next, as shown in FIG. 3G, based on the cumulative histogram information Rrpn (n=1 to N) obtained in step S4 (among which, the cumulative histogram information RrpN generated by accumulating n=N items is the reference cumulative histogram information), a similarity Hmn (n=1 to N-1) to the reference cumulative histogram information RrpN is calculated for each piece of cumulative histogram information Rrpn (n=1 to N-1) (step S5).

[0057] The similarity Hmn (n=1 to N-1) of the cumulative histogram information Rrpn (n=1 to N-1) to the reference cumulative histogram information RrpN is calculated by the 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 similarity Hmn (n=1 to N-1) of the cumulative histogram information Rrpn (n=1 to N-1) obtained in step S5 to the reference cumulative histogram information RrpN is successively determined from n=1 to n=N to see if it meets a predetermined criterion, and a number n representing the nth cumulative histogram information first detected as meeting 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] Regarding whether the similarity Hmn (n = 1 to N-1) of the cumulative histogram information Rrpn (n = 1 to N-1) to the reference cumulative histogram information RrpN meets a predetermined criterion, if the similarity Hmn satisfies equation (2), it is determined that the similarity meets the predetermined criterion.

[0064] Hmn ≧ HmTHL (2) Here, HmTHL is a predetermined threshold value.

[0065] Therefore, in step S6, starting from the n=1th cumulative histogram information Rrp1, for example as shown in the flowchart in FIG. 3G, it is detected whether the similarity Hmn of the nth cumulative histogram information Rrpn to the reference cumulative histogram information RrpN is equal to or greater than the reference similarity HmTHL (step S61), and if it is not equal to or greater than the reference similarity HmTHL, it is detected whether the similarity Hmn is equal to or greater than the reference similarity HmTHL for the next n (step S62) (step S61).

[0066] This process is repeated, and when a case is detected in which the similarity Hmn is equal to or greater than the reference similarity HmTHL, a number "n" representing the nth number related to the initially detected similarity Hmn is detected, in other words, the number "n" of local histogram information Rhg1, ..., Rhgn accumulated to calculate the cumulative histogram information Rrpn in which the similarity Hmn with the reference cumulative histogram information RrpN is equal to or greater than the reference similarity HmTHL. Then, based on the 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 S63).

[0067] This number "n" indicates that the cumulative histogram information Rrpn calculated by accumulating n pieces of local histogram information Rhg1, ..., Rhgn is sufficiently similar to the cumulative histogram information (reference cumulative histogram information) RrpN, which is assumed to indicate the characteristics of the overall unevenness of the surface being evaluated by accumulating N pieces of local histogram information Rhg1, ..., RhgN.

[0068] Therefore, if n is small, the characteristics of the overall amount of unevenness of the evaluation target surface can be expressed with a small number of pieces of local histogram information Rhgn (n = 1 to N), and it can be determined that the non-uniformity of the surface roughness is small. On the other hand, if n is large, it is necessary to use a large number of pieces of local histogram information Rhgn (n = 1 to N) to express the characteristics of the overall amount of unevenness of the evaluation target surface, and therefore it can be determined that the non-uniformity of the surface roughness is large.

[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 processing of the surface roughness measurement original data collection step (step S1) of the method according to the present disclosure described above is performed by importing three-dimensional shape measurement data (surface roughness measurement original data Don (n=1 to N)) measured by a surface shape measuring device 50, which is a device separate from the surface roughness non-uniformity detecting device 1, via the input I / F unit 31 and storing it in the storage unit 20. Note that, when the processing in the surface roughness non-uniformity detecting device 1 is to detect non-uniformity in the amount of unevenness in a predetermined direction of an object whose shape is defined two-dimensionally as described later, the surface shape measuring device 50 may measure the two-dimensional shape measurement data and input it to the surface roughness non-uniformity detecting device 1. The function and configuration of the surface shape measuring device 50 may be determined depending on the processing target in the surface roughness non-uniformity detecting device 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] Furthermore, in cases where degradation of the equipment's functionality 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 state of deterioration (durability), or whether the equipment is due for maintenance, etc. For example, until now, the only way to determine when to replace a cantilever on which a scanning probe microscope (SPM) probe is installed was for the operator to empirically determine, but by generating and comparing cumulative histogram information Rrpn for samples with known unevenness, it is possible to grasp the operating state and predict when to replace the cantilever or the number of roughness measurements required until replacement.

[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 arbitrary and suitable modifications are possible.

[0084] For example, the above-mentioned embodiment discloses 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. Specifically, as shown in FIG. 3A, a plurality of local measurement areas Sn (n=1 to N) arranged two-dimensionally on the evaluation target surface A are set, and each local area Sn (n=1 to N) is further scanned in the XY direction with a predetermined resolution, respectively, to generate local histogram information Rhgn (n=1 to N) of the amount of unevenness. That is, the unevenness of the surface roughness is detected using unevenness amount 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 with a scanning probe microscope (SPM) to measure their three-dimensional shape. The arrangement of the local measurement areas Sn (n=1 to N) to be scanned is the same as that shown in FIG. 3A. The planar shape of each local area Sn (n=1 to N) is 10 μm square, the interval between each local area Sn (n=1 to N) is 200 μm, and the number N of local areas Sn (n=1 to N) is 100. Each local area Sn (n=1 to N) is scanned with a resolution of 1024 pixels in both the X and Y directions. Therefore, data on the amount of unevenness for 1024 x 1024 pixels is measured from each local area Sn (n=1 to N).

[0090] Figures 4(A) to 4(C) show images of surface shape measurement data Dαn (n=1 to N) obtained by flattening original surface roughness measurement data Dαon (n=1 to N) obtained by scanning the metal surface α under these conditions. Figures 4(A) to 4(C) 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, respectively.

[0091] 5(A) to 5(C) show images of surface shape measurement data Dβn (n=1 to N) obtained by flattening the original surface roughness measurement data Dβon (n=1 to N) obtained by scanning the metal surface β under these conditions. Figures 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 the non-uniformity detection process for such metal surfaces α and β, the relationship between the number of local histogram information Rhgn (n = 1 to N) accumulated to generate the cumulative histogram information Rrpn (n = 1 to N), i.e., the numerical value "n", and the similarity Hmn (n = 1 to N) between the cumulative histogram information Rrpn (n = 1 to N) and the reference cumulative histogram information RrpN is shown in Figure 7 for each of the metal surfaces α and β.

[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] If the similarity HmTHL=0.95, that is, if it is defined that the cumulative histogram information Rrpn (n=1 to N) and the reference cumulative histogram information RrpN are similar when the similarity Hmn is 0.95 or more, the non-uniformity of the metal surface α is expressed as n=87 for N=100. On the other hand, 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

[Claim 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.

Images