Information processing apparatus, information processing method, and program

Abstract

To calculate an evaluation value of an object in image data on the basis of pixels inside and around a predetermined area regardless of the size of pixels of the image data.SOLUTION: The data reading unit 1 reads target image data IMG. A reference frame setting part 2 sets a reference frame defined in reference image data having a pixel size different from that of target image data IMG to the target image data IMG. An inclusion area evaluation value-calculating part 3 calculates inclusion area evaluation values E1 on the basis of pixels included in the reference frame. A reference frame conversion evaluation value-calculating part 4 calculates reference frame conversion evaluation values E1 on the basis of the inclusion region evaluation values E2 and a predetermined coefficient. An extended area evaluation value-calculating part 5 calculates an extended area evaluation value E3 on the basis of the pixels included in the reference frame and the pixels partially included in the reference frame. The evaluation determination unit 6 determines one of the inclusive area evaluation E1, the converted reference frame evaluation E2, and the extended area evaluation as the evaluation E calculated for the reference frame based on the magnitude relationship among the inclusive area evaluation LA, the converted reference frame evaluation LA, and the extended area evaluation.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] With the miniaturization of semiconductor process nodes, there is an urgent need for even higher sensitivity in the inspection of semiconductor wafers, photomasks, etc. For example, a technique for inspecting samples such as masks by imaging the sample is widely known (Patent Documents 1 and 2).

[0003] Furthermore, in order to evaluate the size of an object captured in a captured image, a method is known in which an evaluation value is calculated based on the pixel values ​​of pixels in an area in which the object is captured, and the object is evaluated based on the magnitude of the evaluation value (Non-Patent Document 1). This method proposes that the evaluation value is calculated by removing background components from the sum of pixel values ​​in a predetermined area containing an integer number of pixels at the position in the image where the object is captured. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-161475 [Patent Document 2] Japanese Patent Publication No. 2023-117036 [Non-patent literature]

[0005] [Non-Patent Document 1] Tsuneo Terasawa et. al, “Actinic Mask Blank inspection and Signal Analysis for Detecting Phase Defects Down to 1.5 nm in Height”, 2009, Japanese Journal of Applied Physics, 48, 06FA04. Summary of the Invention [Problem to be solved by the invention]

[0006] Inspection of samples such as photomasks may be performed using multiple inspection devices. However, when imaging a sample using multiple inspection devices, pixel sizes may differ due to differences in the equipment and model of the inspection devices. When pixel sizes differ, the number of pixels that appear with pixel values ​​reflecting the image of the same object will differ even when imaging areas of the same size on the sample.

[0007] On the other hand, even if the pixel sizes of images captured by an inspection device are different, it is desirable to be able to evaluate the same object in the same way, so it is necessary to calculate evaluation values ​​that are equal or close enough to be considered equal. However, when calculating the evaluation value, even if one image contains an integer number of pixels within an area of ​​a predetermined size, it is possible that in the other image, due to differences in pixel size, there may be pixels that are only partially contained within an area of ​​the same size. In this case, it is not possible to simply calculate the evaluation value as proposed in Non-Patent Document 1.

[0008] Therefore, even when the pixel sizes of images captured by inspection devices are different, a method is required to calculate evaluation values ​​that are equal or close enough to be considered equal. [Means for solving the problem]

[0009] The information processing device according to the present disclosure comprises a data reading unit that reads target image data obtained by imaging a sample; a reference frame setting unit that sets a reference frame for the target image data, the reference frame being defined to include an integer number of pixels of reference image data having a pixel size different from that of the target image data; a first evaluation value calculation unit that calculates a first evaluation value based on the pixels contained within the reference frame in the target image data; a second evaluation value calculation unit that calculates a second evaluation value based on the first evaluation value and a predetermined coefficient based on the relationship between the area of ​​a region consisting of pixels contained within the reference frame in the target image data and the area of ​​the reference frame; a third evaluation value calculation unit that calculates a third evaluation value based on the pixels contained within the reference frame in the target image data and pixels partially contained within the reference frame; and an evaluation value determination unit that determines one of the first to third evaluation values ​​as the evaluation value calculated for the reference frame based on the magnitude relationship between the first to third evaluation values.

[0010] The information processing device according to the present disclosure includes a data reading unit that reads target image data obtained by imaging a sample; a reference frame setting unit that sets a reference frame for the target image data that is defined to include an integer number of pixels of reference image data that has a pixel size different from that of the target image data; a first evaluation value calculation unit that calculates a first evaluation value based on the pixels contained within the reference frame in the target image data; a second evaluation value calculation unit that calculates a second evaluation value based on the first evaluation value and a predetermined coefficient based on the relationship between the area of ​​a region consisting of pixels contained within the reference frame in the target image data and the area of ​​the reference frame; and an evaluation value determination unit that determines the second evaluation value as an evaluation value calculated for the reference frame.

[0011] The information processing method according to the present disclosure reads target image data obtained by imaging a sample, sets a reference frame for the target image data that is defined to include an integer number of pixels of reference image data that have a pixel size different from that of the target image data, calculates a first evaluation value based on the pixels contained within the reference frame in the target image data, calculates a second evaluation value based on the first evaluation value and a predetermined coefficient based on the relationship between the area of ​​the region consisting of pixels contained within the reference frame in the target image data and the area of ​​the reference frame, calculates a third evaluation value based on the pixels contained within the reference frame in the target image data and pixels that are partially contained within the reference frame, and determines one of the first to third evaluation values ​​as the evaluation value calculated for the reference frame based on the magnitude relationship between the first to third evaluation values.

[0012] The program according to the present disclosure causes a computer to execute the following steps: reading target image data obtained by imaging a sample; setting a reference frame for the target image data, the reference frame being defined to include an integer number of pixels of reference image data having a pixel size different from that of the target image data; calculating a first evaluation value based on the pixels contained within the reference frame in the target image data; calculating a second evaluation value based on the first evaluation value and a predetermined coefficient based on the relationship between the area of ​​the reference frame and the area of ​​the region consisting of pixels contained within the reference frame in the target image data; calculating a third evaluation value based on the pixels contained within the reference frame in the target image data and pixels partially contained within the reference frame; and determining one of the first to third evaluation values ​​as the evaluation value calculated for the reference frame based on the magnitude relationship between the first to third evaluation values. [Effects of the Invention]

[0013] According to the present disclosure, it is possible to calculate an evaluation value of an object in image data based on pixels inside and around a predetermined region, regardless of the size of the pixels in the image data. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a diagram illustrating an example of an optical device having an information processing device according to a first embodiment. [Figure 2] 3A and 3B are diagrams schematically showing reference image data and target image data; [Figure 3] FIG. 10 is a diagram showing a case where a reference frame in the reference image data is applied to the target image data. [Figure 4] 1 is a diagram illustrating a configuration example of an information processing device according to a first embodiment; [Figure 5] FIG. 4 is an enlarged view of the target image data of FIG. 3. [Figure 6] FIG. 1 is a diagram schematically illustrating Case 1, which is an example of the relationship between a reference frame and an object. [Figure 7] FIG. 10 is a diagram schematically illustrating Case 2, which is an example of the relationship between the reference frame and the object. [Figure 8] FIG. 10 is a diagram schematically illustrating Case 3, which is an example of the relationship between the reference frame and the object. [Figure 9] 10 is a flowchart showing an evaluation value calculation operation in the information processing device according to the first embodiment. [Figure 10] FIG. 1 is a diagram illustrating an example of the configuration of a computer for realizing an information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0015] Specific configurations of the embodiments will be described below with reference to the drawings. The following description shows preferred embodiments of the present disclosure, and the scope of the present disclosure is not limited to the following embodiments. In the following description, components with the same reference numerals indicate substantially the same content.

[0016] Embodiment 1 An information processing device according to the first embodiment will be described. The information processing device according to the present embodiment is configured to calculate an evaluation value of an object in object image data having a pixel size different from that of reference image data, by a method according to the size of the object.

[0017] In the following description, image data may be data that has not yet been converted into an image, such as so-called RAW data, obtained by capturing an image using an imaging device such as a camera. Also, image data may be an image generated by performing predetermined processing on numerical data such as RAW data.

[0018] 1 is a diagram illustrating an example of an optical device having an information processing device according to the first embodiment. The optical device 1000 includes an imaging device 1100 and an information processing device 100 according to the first embodiment. The imaging device 1100 images a sample 1200 and outputs target image data IMG acquired therefrom to the information processing device 100.

[0019] The information processing device 100 calculates an evaluation value for an area of ​​the target image data IMG that contains the target object. Here, the reference frame, which is an area that contains pixels used to calculate the evaluation value, is determined in advance based on the pixel size in reference image data that is captured by a reference imaging device and has pixel sizes different from those of the target image data. In this example, the reference frame is defined as a rectangular area that contains an integer number of pixels in the reference image data. However, the reference frame is not limited to this, and may be an area of ​​a shape other than a rectangle, such as a cross, that contains an integer number of pixels in the reference image data.

[0020] As described below, the information processing device 100 applies a reference frame to target image data having pixel sizes different from those of the reference image data, and calculates an evaluation value based on the pixels inside the reference frame or the pixels inside and around the reference frame.

[0021] Next, we will explain the problems caused by differences in pixel size between the reference image data and the target image data. Figure 2 is a diagram showing the reference image data and the target image data. The reference image data REF and the target image data IMG contain the same target object present on the sample. In Figure 2, the pixel value of each pixel is displayed in grayscale. The white pixels near the center of the reference image data REF and the target image data IMG indicate the pixels containing the target object.

[0022] To evaluate an object in image data, an evaluation value is calculated based on the pixel values ​​of pixels in which the object appears. Here, the evaluation value is calculated as the sum of the pixel values ​​of pixels within a reference frame of a predetermined size. When calculating the sum of the pixel values ​​of pixels within the reference frame, background components may be removed from the sum of pixel values, as in Non-Patent Document 1, for example.

[0023] In the reference image data REF of Fig. 2, a reference frame F is predefined, with a size of 2 rows and 2 columns, i.e., 2 x 2 = 4 pixels. In the reference image data REF, the reference frame F is positioned so that the upper left corner of the reference frame F coincides with the upper left corner of a single pixel. Then, by sequentially changing the pixel that serves as the reference for the positioning of the reference frame F in the reference image data REF, the reference frame F can sweep the entire reference image data REF. This makes it possible to calculate an evaluation value for an object reflected in the reference image data REF.

[0024] However, if the model of the equipment used to image the sample is different, or if there are differences between the equipment even if it is the same type, the pixel size of the image data obtained by imaging the same object may differ between the reference image data REF and the target image data IMG, as shown in Figure 2. For example, even if a sample is imaged using different imaging equipment with the same optical system, the pixel size of the imaging equipment may not match due to manufacturing errors, etc. Furthermore, it is also possible that the pixel size may differ between an older model imaging equipment and a newer model imaging equipment due to differences in resolution, for example.

[0025] Even in this case, it is desirable to be able to perform similar evaluations on the same object. Therefore, it is required that evaluation values ​​calculated from image data acquired by different devices be calculated as equivalent or as close as possible. However, when calculating evaluation values ​​for an object using multiple images with different pixel sizes, the following problems arise.

[0026] 3 is a diagram showing the case where the reference frame F in the reference image data REF is applied to the target image data IMG. In FIG. 3, the reference frame F in the reference image data REF includes four pixels. In contrast, in the target image data IMG, each side of the reference frame F is 4.1 times the length of a pixel. Therefore, when the upper left corner of the reference frame F is aligned with the upper left corner of a pixel in the target image data IMG, the reference frame F includes 4 x 4 = 16 pixels, and the right and bottom sides of the reference frame F cross 9 pixels.

[0027] Therefore, in the target image data IMG, the number of pixels contained within the reference frame F is not an integer, as in the reference image data REF, and some pixels are only partially contained. As a result, it is no longer possible to simply calculate the evaluation value as in the case of the reference image data REF.

[0028] Therefore, hereinafter, an information processing apparatus will be described that calculates an appropriate evaluation value using pixel values ​​even when there are pixels that are only partially included in the reference frame F, as in the target image data IMG of FIG.

[0029] 4 is a diagram illustrating an example of the configuration of an information processing device according to the first embodiment. Information processing device 100 includes a data reading unit 1, a reference frame setting unit 2, an inclusion area evaluation value calculation unit 3, a reference frame converted evaluation value calculation unit 4, an extended area evaluation value calculation unit 5, and an evaluation value determination unit 6.

[0030] The data reading unit 1 reads target image data IMG that is the target for calculating an evaluation value. The data reading unit 1 may read the target image data IMG from an imaging device that images a sample. Alternatively, for example, the target image data IMG may be stored in advance in a storage device (not shown), and the data reading unit 1 may read the target image data IMG from the storage device as needed.

[0031] The reference frame setting unit 2 holds information indicating a reference frame F of a predetermined size that is determined in advance based on the size of the pixels of the reference image data REF. FIG. 5 is an enlarged view of the target image data IMG in FIG. 3. The reference frame setting unit 2 sets the reference frame F within a two-dimensional plane in which the pixels of the target image data IMG are arranged. In this example, as shown in FIG. 5, the reference frame F is set so that the upper left corner of the reference frame F coincides with the upper left corner of the pixel.

[0032] The reference frame setting unit 2 may previously store information specifying the size and arrangement of the reference frame F. Furthermore, the reference frame F may be provided to the reference frame setting unit 2 via various input means or communication means. Furthermore, the reference frame setting unit 2 may acquire information specifying the size and arrangement of the reference frame F stored in a storage device (not shown) directly or via a reading means (not shown) as necessary.

[0033] In this embodiment, the area consisting of pixels included in reference frame F, i.e., pixels that exist inside reference frame F without being crossed by reference frame F, is called the inclusion area A1. The area including inclusion area A1 and pixels crossed by reference frame F, i.e., the area including the area inside reference frame F and the part of the pixels crossed by reference frame F outside reference frame F, is called the extended area A2.

[0034] The inclusion area evaluation value calculation unit 3 calculates the sum of the pixel values ​​of the pixels included in the inclusion area A1 as the inclusion area evaluation value E1. In the example of Figure 5, the inclusion area evaluation value E1 is calculated for the pixel values ​​of the 16 pixels included in the inclusion area A1. Note that the inclusion area evaluation value calculation unit 3 may also calculate other statistical values, such as the average pixel values ​​of the pixels included in the inclusion area A1, as the inclusion area evaluation value E1.

[0035] The reference-frame-converted evaluation value calculation unit 4 calculates the reference-frame-converted evaluation value E2 based on the inclusion area evaluation value E1 and a predetermined coefficient α based on the relationship between the area of ​​the inclusion area A1, which is made up of pixels included in the reference frame F, and the area of ​​the reference frame F. For example, the reference-frame-converted evaluation value calculation unit 4 calculates the reference-frame-converted evaluation value E2 by dividing the total value of the pixels included in the reference frame F by the predetermined coefficient α. In this case, the coefficient α may be, for example, the area of ​​the inclusion area A1 divided by the area of ​​the reference frame F. In the example of FIG. 5, it is assumed that the length of each side of the reference frame F is 4.1 times the pixel length of the target image data IMG. Therefore, since 16 / (4.1 * 4.1) = 0.9518, for example, the coefficient α may be 0.95.

[0036] The reference-frame-converted evaluation value calculation unit 4 may store information indicating the coefficient α in advance. Furthermore, the information indicating the coefficient α may be provided to the reference-frame-converted evaluation value calculation unit 4 via various input means or communication means. Furthermore, the reference-frame-converted evaluation value calculation unit 4 may obtain information indicating the coefficient α stored in a storage device (not shown) directly or via a reading means (not shown) as necessary.

[0037] The extended region evaluation value calculation unit 5 calculates the total value of the pixels included in the extended region A2 as the extended region evaluation value E3. In the example of Fig. 5, the extended region evaluation value E3 is calculated for the pixel values ​​of 25 pixels included in the extended region A2. Note that the extended region evaluation value calculation unit 5 may also calculate other statistical values, such as the average value of the pixel values ​​of the pixels included in the extended region A2, as the extended region evaluation value E3.

[0038] The evaluation value determination unit 6 determines the magnitude relationship among the inclusion area evaluation value E1, the reference frame converted evaluation value E2, and the extended area evaluation value E3. Then, based on the determination result, the evaluation value determination unit 6 selects an evaluation value E for the pixel within the reference frame F.

[0039] Next, we will explain the relationship between the reference frame F, the object OBJ, and the evaluation value calculated by the evaluation value determiner 6. In this embodiment, the size of the object OBJ is estimated based on the magnitude relationship between the inclusion area evaluation value E1, the reference frame converted evaluation value E2, and the extended area evaluation value E3, and the evaluation value E is determined based on the estimation result. In this embodiment, the size of the object OBJ is divided into the following cases 1 to 3 to calculate the evaluation value E.

[0040] Case 1 Assume that the object OBJ is large enough to be contained within the inclusion area A1. Figure 6 is a diagram schematically illustrating Case 1, an example of the relationship between the reference frame F and the object OBJ. In this case, the object OBJ is contained within both the inclusion area A1 and the extended area A2. Therefore, the inclusion area evaluation value E1 and the extended area evaluation value E3 can be expected to be the same value or similar enough to be considered the same value. On the other hand, the reference frame converted evaluation value E2 will be larger than the inclusion area evaluation value E1 and the extended area evaluation value E3.

[0041] Therefore, if the reference-frame-converted evaluation value E2 is greater than the inclusion area evaluation value E1 and the extended area evaluation value E3, it can be determined that the object OBJ is large enough to be included in the inclusion area A1. In this case, the reference-frame-converted evaluation value E2 is considered to overestimate the area occupied by the object OBJ. Therefore, in Case 1, by using the inclusion area evaluation value E1 or the extended area evaluation value E3 as the evaluation value E of the object OBJ, the proportion of the area occupied by the object OBJ within the reference frame F can be suitably reflected.

[0042] Case 2 Assume a case where the object OBJ does not fit within the inclusion region A1 but is sized to be included within the reference frame F. Fig. 7 is a diagram schematically showing Case 2, which is an example of the relationship between the reference frame F and the object OBJ. In this case, since the object OBJ extends outside the inclusion region A1, it is assumed that the extended region evaluation value E3 is greater than the inclusion region evaluation value E1. Also, since the area of the portion of the object OBJ that does not fit within the inclusion region A1 but protrudes within the reference frame F is small, it is assumed that the reference frame conversion evaluation value E2 calculated by dividing the inclusion region evaluation value E1 by the coefficient α = 0.95 is greater than both the inclusion region evaluation value E1 and the extended region evaluation value E3.

[0043] Therefore, when the reference frame conversion evaluation value E2 is the largest and the inclusion region evaluation value E1 is the smallest, that is, when E1 < E3 < E2, it can be determined that the object OBJ does not fit within the inclusion region A1 but is sized to be included within the reference frame F. At this time, it is considered that the inclusion region evaluation value E1 underestimates the region occupied by the object OBJ, and the reference frame conversion evaluation value E2 overestimates the region occupied by the object OBJ. Therefore, in Case 2, by using the extended region evaluation value E3 as the evaluation value E of the object OBJ, the ratio of the region occupied by the object OBJ within the reference frame F can be suitably reflected.

[0044] Case 3 Assume a case where the object OBJ extends outside the reference frame F. Fig. 8 is a diagram schematically showing Case 3, which is an example of the relationship between the reference frame F and the object OBJ. In this case, the object OBJ occupies the region within the reference frame F and also occupies all or part of the extended region A2. Therefore, the extended region evaluation value E3 is greater than both the inclusion region evaluation value E1 and the reference frame conversion evaluation value E2. Note that, similar to Case 2, the reference frame conversion evaluation value E2 is greater than the inclusion region evaluation value E1.

[0045] Therefore, when the extended region evaluation value E3 is the largest and the inclusion region evaluation value E1 is the smallest, that is, when E1 < E2 < E3, it can be determined that the object OBJ extends outside the reference frame F. At this time, the inclusion region evaluation value E1 is considered to undervalue the region occupied by the object OBJ. Also, since the extended region evaluation value E3 captures many pixel values outside the reference frame F, it is considered to be an excessive value as the evaluation value of the object OBJ within the reference frame F. Thus, in Case 3, by using the reference frame conversion evaluation value E2 as the evaluation value E of the object OBJ, the ratio of the region occupied by the object OBJ within the reference frame F can be suitably reflected.

[0046] Next, the evaluation value calculation operation in the information processing apparatus 100 will be described. FIG. 9 is a flowchart showing the evaluation value calculation operation in the information processing apparatus according to Embodiment 1.

[0047] Step S1 The data reading unit 1 reads the target image data IMG that is the target of evaluation value calculation.

[0048] Step S2 The reference frame setting unit 2 sets the reference frame F based on the pixel array in the target image data IMG. In this example, the reference frame F is arranged so that the upper left end of the reference frame F coincides with the upper left end of one pixel. In this example, 16 pixels are included within the reference frame F, and these 16 pixels constitute the inclusion region A1. And on the left side and the lower side of the inclusion region A1, the reference frame F crosses 9 pixels. Therefore, the extended region A2 includes 16 + 9 = 25 pixels.

[0049] Step S3 The inclusion region evaluation value calculation unit 3 calculates the total value of the pixel values of the pixels included in the inclusion region A1 as the inclusion region evaluation value E1.

[0050] Step S4 The reference frame conversion evaluation value calculation unit 4 calculates, as the reference frame conversion evaluation value E2, a value obtained by dividing the sum value of the pixel values included in the reference frame F by a predetermined coefficient α. Here, α is set to 0.95.

[0051] Step S5 The extended region evaluation value calculation unit 5 calculates, as the extended region evaluation value E3, the sum value of the pixels included in the extended region A2.

[0052] Step S6 The evaluation value determination unit 6 determines whether the extended region evaluation value E3 is greater than the reference frame conversion evaluation value E2. Thereby, the evaluation value determination unit 6 can determine whether the size of the object OBJ corresponds to Case 3.

[0053] Step S7 When the extended region evaluation value E3 is greater than the reference frame conversion evaluation value E2, that is, when the size of the object OBJ corresponds to Case 3, the evaluation value determination unit 6 selects the reference frame conversion evaluation value E2 as the evaluation value E.

[0054] Step S8 When the extended region evaluation value E3 is less than or equal to the reference frame conversion evaluation value E2, that is, when the size of the object OBJ does not correspond to Case 3, the evaluation value determination unit 6 determines whether the difference between the extended region evaluation value E3 and the inclusion region evaluation value E1 is less than a predetermined value β. That is, the evaluation value determination unit 6 determines whether -β < E3 - E1 < β, or equivalently, whether |E3 - E1| < β. Thereby, the evaluation value determination unit 6 can determine whether the size of the object OBJ corresponds to Case 1.

[0055] The evaluation value determination unit 6 may hold in advance information indicating the predetermined value β. Also, the information indicating the predetermined value β may be given to the evaluation value determination unit 6 via various input means and communication means. Further, the evaluation value determination unit 6 may directly or via a reading means (not shown) acquire, as necessary, the information indicating the predetermined value β stored in a storage device (not shown).

[0056] Step S9 If the difference between the extended area evaluation value E3 and the inclusion area evaluation value E1 is smaller than a predetermined value β, that is, if the size of the object OBJ corresponds to case 1, the evaluation value determination unit 6 selects the inclusion area evaluation value E1 as the evaluation value E.

[0057] Step S10 If the difference between the extended area evaluation value E3 and the inclusion area evaluation value E1 is greater than or equal to a predetermined value β, i.e., if the size of the object OBJ falls under case 2, the evaluation value determination unit 6 determines the extended area evaluation value E3 as the evaluation value E in the reference frame F.

[0058] According to the above procedure, depending on the size of the object OBJ within the reference frame F, an appropriate value can be determined as the evaluation value E from the inclusion area evaluation value E1, the reference frame converted evaluation value E2, and the extended area evaluation value E3.

[0059] As a result, even for target image data IMG that has a different pixel size from the reference image data REF, it is possible to calculate an evaluation value that is the same as, or close enough to be considered equal to, the evaluation value for the reference image data REF.

[0060] As a result, it is possible to evaluate the object captured in the image data using an evaluation value that is equivalent or has a degree of accuracy that can be considered equivalent, regardless of the imaging device that acquires the image data.

[0061] Other embodiments Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0062] In the above-described embodiment, the evaluation value E is determined to be one of the inclusion area evaluation value E1, the reference-frame-converted evaluation value E2, and the extended area evaluation value E3. However, evaluation value determiner 6 may convert the inclusion area evaluation value E1 calculated for inclusion area A1 to correspond to the size of reference frame F and determine the evaluation value E as the evaluation value. Evaluation value determiner 6 may also calculate the reference-frame-converted evaluation value E2 based on the inclusion area evaluation value E1 and coefficient α and determine the evaluation value E as the evaluation value. This allows information processing device 100 to have a simpler configuration, including data reader 1, reference-frame setting unit 2, inclusion area evaluation value calculator 3, reference-frame-converted evaluation value calculator, and extended area evaluation value calculator 6.

[0063] In the above embodiment, it has been described that reference-frame-converted evaluation value calculation unit 4 calculates reference-frame-converted evaluation value E2 by dividing the total number of pixel values ​​included in reference frame F by coefficient α, which is the value obtained by dividing the area of ​​inclusion area A1 by the area of ​​reference frame F. Alternatively, although this is essentially the same calculation process, reference-frame-converted evaluation value calculation unit 4 may calculate reference-frame-converted evaluation value E2 by multiplying the total number of pixel values ​​included in reference frame F by a coefficient, which is the value obtained by dividing the area of ​​reference frame F by the area of ​​inclusion area A1.

[0064] Although the evaluation value determination unit 6 has been described as determining which of two values ​​is larger in steps S6 and S8 of FIG. 9 , the method for determining the magnitude relationship between the values ​​is not limited to this. The evaluation value determination unit 6 may determine whether the first value is larger than the second value, as in step S6 of FIG. 9 , or whether the first value is equal to or larger than the second value. Alternatively, the evaluation value determination unit 6 may determine whether the first value is smaller than the second value, as in step S8 of FIG. 9 , or whether the first value is equal to or smaller than the second value. That is, when determining which of two values ​​is larger, the evaluation value determination unit 6 only needs to be able to distinguish between cases where the first value is larger and smaller than the second value. When the first value and the second value are equal, the evaluation value determination unit 6 may perform the same processing as when the first value is larger than the second value, or may perform the same processing as when the first value is smaller than the second value.

[0065] In the above embodiment, the data reading unit 1 has been described as reading the target image data IMG from the imaging device 1100, but this is merely an example. For example, the data reading unit 1 may read the target image data IMG stored in advance in a storage device (not shown) as needed. The storage device in which the target image data IMG is stored may be part of the information processing device 100, or may be provided separately from the information processing device 100. Image data and other information used by the information processing device 100 may be read as needed from the imaging device 1100, part of the information processing device 100, or part or all of an external storage device.

[0066] The size and location of the reference frame are merely examples, and the reference frame may be of any size as needed, and may be located at any position relative to the pixels of the target image data IMG.

[0067] In the above-described embodiments, the optical device according to the present disclosure has been described primarily as a hardware configuration, but this is not limiting. The optical device according to the present disclosure can also be realized by having a computer execute a computer program to perform any desired processing. These processes may be realized by having a computer including at least one processor (e.g., a microprocessor, a CPU, a GPU, an MPU, or a DSP (Digital Signal Processor)) execute the program. Specifically, one or more programs including instructions for causing a computer to perform these algorithms related to transmission signal processing or reception signal processing may be created, and the programs may be supplied to the computer.

[0068] A computer program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can supply the program to a computer via a wired communication path such as an electric wire or optical fiber, or via a wireless communication path.

[0069] An example of the configuration of a computer for realizing the information processing device 100 is shown below. FIG. 10 is a diagram showing an example of the configuration of a computer for realizing the information processing device 100. The information processing device 100 can be realized by a computer 9000 such as a dedicated computer or a personal computer (PC). However, the computer does not need to be physically single, and may be multiple when performing distributed processing. As shown in FIG. 10, the computer 9000 has, for example, a processor 9001, a ROM (Read Only Memory) 9002, a RAM (Random Access Memory) 9003, a storage unit 9004, a communication interface 9005, and a user interface 9006.

[0070] The processor 9001, ROM 9002, RAM 9003, storage unit 9004, communication interface 9005, and user interface 9006 are connected to each other so as to be able to communicate with each other via a bus 9007. Note that although explanation of OS software for operating the computer is omitted, it is also installed in the computer 9000 as appropriate.

[0071] The ROM is configured by, for example, a nonvolatile semiconductor memory device, etc. The ROM 9002 stores information such as various programs used by the computer 9000.

[0072] The storage unit 9004 is configured by various storage devices such as a hard disk, a solid state disk, etc. Furthermore, the storage unit 9004 is not limited to a storage device installed in the computer 9000, but may be a storage device external to the computer 9000. The external storage device may be a cloud storage connected to the computer 9000 via various communication means, for example, a network. The storage unit 9004 stores information such as various programs and data used by the computer 9000.

[0073] The RAM 9003 is configured by a volatile semiconductor memory device, etc. Programs, data, and other information used by the processor 9001 are loaded into the RAM 9003 from one or both of the ROM 9002 and the storage unit 9004 as appropriate.

[0074] The processor 9001 may be configured with, for example, a CPU (Central Processing Unit). The processor 9001 may also include not only a CPU but also a GPU (Graphics Processing Unit). A GPU is suitable for performing routine processing in parallel, and when applied to processing in a neural network, for example, it can improve processing speed compared to a CPU. The processor 9001 executes various processes based on various programs stored in the ROM 9002 or various programs and data held in the RAM 9003, as appropriate. The processor 9001 may also store data generated by processing in the RAM 9003 or the storage unit 9004, as appropriate.

[0075] The communication interface 9005 is an interface that connects the computer 9000 to a communication network such as the Internet or an intranet via various wired communication means or wireless communication means, etc. This allows the computer 9000 to communicate with other devices, systems, sensors, etc. that are connected to the communication network.

[0076] The user interface 9006 includes, for example, a display unit that provides information so that the user can recognize it using a display device or the like, and an audio output unit that outputs audio. The user interface 9006 also includes an input unit that allows the user to input information to the computer 9000 by operating a keyboard, mouse, touch panel, or the like. The user interface 9006 may also include devices such as sensors that obtain information useful to the user.

[0077] Although the computer 9000 has been described as a single device here, this is merely an example. The computer 9000 may be composed of multiple physically separated devices. Some of the multiple devices may be portable devices, and other devices may be stationary devices.

[0078] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0079] Each drawing is merely an example for describing one or more embodiments. Each drawing may relate not only to one particular embodiment, but also to one or more other embodiments. As will be understood by those skilled in the art, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings to create, for example, an embodiment not explicitly shown or described. Not all features or steps shown in any one drawing are necessary to describe an exemplary embodiment, and some features or steps may be omitted. The order of steps described in any drawing may be changed as appropriate. [Explanation of symbols]

[0080] 1 Data reading section 2 Reference frame setting section 3. Inclusion area evaluation value calculation section 4. Reference frame conversion evaluation value calculation section 5. Extended area evaluation value calculation unit 6 Evaluation value determination unit 100 Information processing device 1000 optical equipment 1100 Imaging device 1200 samples 9000 computers 9001 processor 9002 ROM 9003 RAM 9004 Storage section 9005 Communication Interface 9006 User Interface 9007 Bus A1 Inclusion area A2 expansion area E1 Inclusion Area Score E2 Reference Frame Conversion Evaluation Value E3 Extended Area Evaluation Value F. Frame of Reference IMG Target image data OBJ Object REF Reference image data

Claims

1. a data reading unit that reads target image data obtained by imaging a sample; a reference frame setting unit that sets a reference frame for the target image data, the reference frame being defined to include an integer number of pixels of reference image data that are different in pixel size from the target image data; a first evaluation value calculation unit that calculates a first evaluation value based on pixels included in the reference frame in the target image data; a second evaluation value calculation unit that calculates a second evaluation value based on the first evaluation value and a predetermined coefficient based on the relationship between the area of ​​the reference frame and the area of ​​a region made up of pixels included in the reference frame in the target image data; a third evaluation value calculation unit that calculates a third evaluation value based on the pixels included in the reference frame and pixels partially included in the reference frame in the target image data; an evaluation value determination unit that determines one of the first to third evaluation values ​​as an evaluation value calculated for the reference frame based on a magnitude relationship between the first to third evaluation values; Information processing device.

2. The evaluation value determination unit If the third evaluation value is greater than the second evaluation value, the second evaluation value is determined to be the evaluation value calculated for the reference frame; When the third evaluation value is smaller than the second evaluation value, If the difference between the third evaluation value and the first evaluation value is smaller than a predetermined value, the first evaluation value is determined to be the evaluation value calculated for the reference frame; If the difference between the third evaluation value and the first evaluation value is greater than a predetermined value, the third evaluation value is determined to be the evaluation value calculated for the reference frame. The information processing device according to claim 1 .

3. the evaluation value determination unit determines the second evaluation value as the evaluation value calculated for the reference frame when the third evaluation value and the second evaluation value are equal. The information processing device according to claim 2 .

4. When the third evaluation value is equal to or less than the second evaluation value and the difference between the third evaluation value and the first evaluation value is equal to the predetermined value, the evaluation value determination unit determines the first evaluation value or the third evaluation value as the evaluation value calculated for the reference frame. The information processing device according to claim 2 .

5. When the third evaluation value is smaller than the second evaluation value and the difference between the third evaluation value and the first evaluation value is equal to the predetermined value, the evaluation value determination unit determines the first evaluation value or the third evaluation value as the evaluation value calculated for the reference frame. The information processing device according to claim 3 .

6. the first and third evaluation value calculation units calculate the first and third evaluation values, respectively, based on a sum of pixel values ​​of pixels to be calculated; 3. The information processing device according to claim 1 or 2.

7. the first and third evaluation value calculation units calculate the first and third evaluation values, respectively, by removing background components from the sum of pixel values ​​of the pixels to be calculated. The information processing device according to claim 6 .

8. the reference frame is a rectangular region, the reference frame setting unit sets the reference frame so that a vertex of the rectangle coincides with a vertex of one of the pixels.

3. The information processing device according to claim 1 or 2.

9. The predetermined coefficient is a value obtained by dividing the area of ​​a region made up of the pixels included in the reference frame in the target image data by the area of ​​the reference frame.

3. The information processing device according to claim 1 or 2.

10. the predetermined coefficient is a value obtained by dividing the area of ​​a region made up of pixels included in the reference frame in the target image data by the area of ​​the reference frame; the second evaluation value calculation unit calculates the second evaluation value by dividing the first evaluation value by the predetermined value; 3. The information processing device according to claim 1 or 2.

11. a data reading unit that reads target image data obtained by imaging a sample; a reference frame setting unit that sets a reference frame for the target image data, the reference frame being defined to include an integer number of pixels of reference image data that are different in pixel size from the target image data; a first evaluation value calculation unit that calculates a first evaluation value based on pixels included in the reference frame in the target image data; a second evaluation value calculation unit that calculates a second evaluation value based on the first evaluation value and a predetermined coefficient based on the relationship between the area of ​​the reference frame and the area of ​​a region made up of pixels included in the reference frame in the target image data; an evaluation value determination unit that determines the second evaluation value as an evaluation value calculated for the reference frame, Information processing device.

12. Read the target image data of the sample, a reference frame defined to include an integer number of pixels of reference image data having a pixel size different from that of the target image data is set for the target image data; calculating a first evaluation value based on pixels included in the reference frame in the target image data; calculating a second evaluation value based on the first evaluation value and a predetermined coefficient based on the relationship between the area of ​​the reference frame and the area of ​​the region made up of pixels included in the reference frame in the target image data; calculating a third evaluation value based on the pixels included in the reference frame and pixels partially included in the reference frame in the target image data; determining one of the first to third evaluation values ​​as the evaluation value calculated for the reference frame based on the magnitude relationship between the first to third evaluation values; Information processing methods.

13. A process of reading target image data obtained by capturing an image of a sample; a process of setting a reference frame for the target image data, the reference frame being defined so as to include an integer number of pixels of reference image data having a pixel size different from that of the target image data; A process of calculating a first evaluation value based on pixels included in the reference frame in the target image data; a process of calculating a second evaluation value based on the first evaluation value and a predetermined coefficient based on the relationship between the area of ​​the reference frame and the area of ​​a region made up of pixels included in the reference frame in the target image data; a process of calculating a third evaluation value based on the pixels included in the reference frame and pixels partially included in the reference frame in the target image data; determining one of the first to third evaluation values ​​as an evaluation value calculated for the reference frame based on a magnitude relationship between the first to third evaluation values; program.

Images