JP7839831B2 - Information processing device, information processing method, and program - Google Patents

Description

This disclosure relates to an information processing device, an information processing method, and a program.

With the miniaturization of semiconductor process nodes, there is an urgent need for even higher sensitivity in the inspection of semiconductor wafers, photomasks, and other materials. For example, a widely known inspection method for samples such as masks involves imaging the sample (Patent Documents 1 and 2).

Furthermore, a method is known for evaluating the size of objects in captured images by calculating an evaluation value based on the pixel values of pixels in the area where the object is located, and then evaluating the object based on the magnitude of this evaluation value (Non-Patent Literature 1). This method proposes calculating the evaluation value by subtracting the background component from the sum of the pixel values within a predetermined area containing an integer number of pixels at the location where the object is located in the image.

Japanese Patent Publication No. 2022-161475 Japanese Patent Publication No. 2023-117036

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.

In the inspection of samples such as photomasks, inspection may be performed using multiple inspection devices. However, when imaging a sample with multiple inspection devices, the pixel size may differ due to differences in the devices themselves or their models. If the pixel size differs, even when imaging the same-sized area of the sample, the number of pixels that reflect the image of the same object will differ.

On the other hand, it is desirable to be able to evaluate the same object in the same way even when the pixel sizes of the images captured by the inspection device differ. Therefore, it is necessary to calculate an evaluation value that is equivalent or approximated to an equivalent degree. However, when calculating the evaluation value, even if one image contains an integer number of pixels within a predetermined area of size, due to differences in pixel size, it is conceivable that in the other image, only a portion of the pixels may be included within the same predetermined area of size. In this case, it is not possible to simply calculate the evaluation value as proposed in Non-Patent Document 1.

Therefore, a method is needed to calculate evaluation values that are equivalent or approximate to an equivalent degree, even when the pixel sizes of the images captured by the inspection device differ.

The information processing apparatus according to this disclosure comprises: a data reading unit for reading target image data of a sample; a reference frame setting unit for setting a reference frame defined to include an integer number of pixels of reference image data with different pixel sizes from the target image data; a first evaluation value calculation unit for calculating a first evaluation value based on the pixels contained within the reference frame in the target image data; a second evaluation value calculation unit for calculating 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; a third evaluation value calculation unit for calculating a third evaluation value based on the pixels contained within the reference frame and pixels partially contained within the reference frame in the target image data; and an evaluation value determination unit for determining one of the first to third evaluation values as the evaluation value calculated for the reference frame based on the relative magnitudes of the first to third evaluation values.

The information processing device according to this disclosure comprises: a data reading unit for reading target image data of a sample; a reference frame setting unit for setting a reference frame defined to include an integer number of pixels of reference image data with different pixel sizes from the target image data; a first evaluation value calculation unit for calculating a first evaluation value based on the pixels contained within the reference frame in the target image data; a second evaluation value calculation unit for calculating 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; and an evaluation value determination unit for determining the second evaluation value as the evaluation value calculated for the reference frame.

The information processing method according to this disclosure reads target image data obtained by imaging a sample, sets a reference frame defined to include an integer number of pixels of reference image data with different pixel sizes from 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 and pixels partially contained within the reference frame in the target image data, and determines one of the first to third evaluation values as the evaluation value calculated for the reference frame based on the relative magnitudes of the first to third evaluation values.

The program according to this disclosure causes a computer to perform the following steps: read target image data obtained by imaging a sample; set a reference frame defined to include an integer number of pixels of reference image data with different pixel sizes from the target image data; calculate a first evaluation value based on the pixels contained within the reference frame in the target image data; calculate 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; calculate a third evaluation value based on the pixels contained within the reference frame and pixels partially contained within the reference frame in the target image data; and determine one of the first to third evaluation values as the evaluation value calculated for the reference frame based on the relative magnitudes of the first to third evaluation values.

According to this disclosure, regardless of the pixel size of the image data, it is possible to calculate the evaluation value of an object within the image data based on the pixels inside and around a predetermined region.

This figure shows an example of an optical device having an information processing device according to Embodiment 1. This diagram schematically shows the reference image data and the target image data. This diagram shows the case where the reference frame in the reference image data is applied to the target image data. This figure shows an example of the configuration of the information processing device according to Embodiment 1. This is a magnified view of the target image data in Figure 3. This diagram schematically illustrates Case 1, an example of the relationship between a reference frame and an object. This diagram schematically illustrates Case 2, an example of the relationship between a reference frame and an object. This diagram schematically illustrates Case 3, an example of the relationship between a reference frame and an object. This is a flowchart showing the evaluation value calculation operation in the information processing device according to Embodiment 1. This figure shows an example of a computer configuration for realizing an information processing device.

The specific configuration of the embodiments will be described below with reference to the drawings. The following description illustrates preferred embodiments of the present disclosure, and the scope of the present disclosure is not limited to these embodiments. In the following description, components denoted by the same reference numerals refer to substantially equivalent components.

Embodiment 1
The information processing device according to Embodiment 1 will now be described. The information processing device according to this embodiment is configured to calculate the evaluation value of an object in target image data, which has a different pixel size from the reference image data, in a manner appropriate to the size of the object.

Furthermore, in the following, image data may be data that has not yet been converted into an image, such as so-called RAW data, acquired by imaging devices such as cameras. Alternatively, image data may be an image generated by performing a predetermined process on numerical data such as RAW data.

Figure 1 shows an example of an optical apparatus having an information processing device according to Embodiment 1. The optical apparatus 1000 includes an imaging device 1100 and an information processing device 100 according to Embodiment 1. The imaging device 1100 outputs target image data (IMG) acquired by imaging the sample 1200 to the information processing device 100.

The information processing device 100 calculates an evaluation value for the area containing the target object in the target image data (IMG). Here, the reference frame, which is the area containing the pixels used to calculate the evaluation value, is predetermined based on the pixel size in reference image data, which is captured by a reference imaging device and has a different pixel size than the target image data. In this example, the reference frame is defined as a rectangular area containing an integer number of pixels in the reference image data. However, the reference frame is not limited to this; it may also be an area of a shape other than a rectangle, such as a cross shape, containing an integer number of pixels in the reference image data.

The information processing device 100 applies a reference frame to target image data with pixel sizes different from the reference image data, as described below, and calculates an evaluation value based on the pixels inside the reference frame, or the pixels inside and around the reference frame.

Next, we will explain the problems arising from the difference in pixel size between the reference image data and the target image data. Figure 2 is a schematic diagram showing the reference image data and the target image data. The same object present on the sample is present in both the reference image data REF and the target image data IMG. In Figure 2, the pixel values of each pixel are displayed in grayscale. The white pixels near the center of the reference image data REF and the target image data IMG indicate the pixels in which the object is captured.

To evaluate an object within image data, an evaluation value is calculated based on the pixel values of the pixels in which the object is captured. Here, the evaluation value is calculated as the sum of the pixel values of pixels within a predetermined reference frame. Note that, when calculating the sum of the pixel values within the reference frame, the background component may be removed from the sum of the pixel values, as described in Non-Patent Document 1, for example.

In the reference image data REF shown in Figure 2, a reference frame F with a size of 2x2, or 2x2 = 4 pixels, is predetermined. In the reference image data REF, the reference frame F is positioned so that its upper-left corner coincides with the upper-left corner of a single pixel. By sequentially changing the pixel that serves as the reference for the positioning of the reference frame F, the entire reference image data REF can be swept by the reference frame F. This allows for the calculation of evaluation values for objects captured within the reference image data REF.

However, if the models of the imaging devices used to capture the sample differ, or if there are differences between devices of the same type, as shown in Figure 2, the pixel size of the reference image data (REF) and the target image data (IMG) obtained by imaging the same object may differ. For example, even when imaging a sample with different imaging devices that have the same optical system structure, the pixel size of the imaging devices may not match due to manufacturing errors, etc. Furthermore, it is conceivable that the pixel size may differ between older and newer imaging devices due to differences in resolution.

Even in this case, it is desirable to be able to perform the same evaluation for the same object. Therefore, evaluation values calculated from image data acquired with different devices should be equivalent or as close as possible. However, when attempting to calculate evaluation values for an object using multiple images with different pixel sizes, the following problems arise.

Figure 3 shows the case where the reference frame F in the reference image data REF is applied to the target image data IMG. In Figure 3, the reference frame F in the reference image data REF contains four pixels. In contrast, in the target image data IMG, each side of the reference frame F is 4.1 times the length of the pixel side. Therefore, when the upper left corner of the reference frame F is aligned with the upper left corner of one pixel in the target image data IMG, the reference frame F contains 4 × 4 = 16 pixels, and the right and bottom sides of the reference frame F traverse 9 pixels.

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 included. Consequently, it becomes impossible to simply calculate the evaluation value as in the reference image data REF.

Therefore, the following describes an information processing device that calculates an appropriate evaluation value using pixel values, even when pixels are only partially included within the reference frame F, as shown in the target image data IMG in Figure 3.

Figure 4 shows an example of the configuration of the information processing device according to Embodiment 1. The information processing device 100 includes a data reading unit 1, a reference frame setting unit 2, a unit for calculating the inclusion area evaluation value 3, a unit for calculating the reference frame converted evaluation value 4, a unit for calculating the extended area evaluation value 5, and an evaluation value determination unit 6.

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

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

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

In this embodiment, the region consisting of pixels contained within the reference frame F, that is, pixels that lie inside the reference frame F without being crossed by it, is referred to as the containing region A1. The region including the containing region A1 and the pixels that the reference frame F crosses, that is, the region including the area within the reference frame F and the portion of the pixels outside the reference frame F that the reference frame F crosses, is referred to as the extended region A2.

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

The reference frame conversion evaluation value calculation unit 4 calculates the reference frame conversion evaluation value E2 based on the inclusion region evaluation value E1 and a predetermined coefficient α based on the relationship between the area of the inclusion region A1, which consists of pixels contained within the reference frame F, and the area of the reference frame F. For example, the reference frame conversion evaluation value calculation unit 4 calculates the reference frame conversion evaluation value E2 as the value obtained by dividing the total value of the pixels contained within the reference frame F by the predetermined coefficient α. In this case, for example, the coefficient α may be the value obtained by dividing the area of the inclusion region A1 by the area of the reference frame F. In the example in Figure 5, it is assumed that the length of each side of the reference frame F is 4.1 times the length of the pixels of the target image data IMG. Therefore, 16 / (4.1 * 4.1) = 0.9518..., so for example, 0.95 may be used as the coefficient α.

The standard frame conversion evaluation value calculation unit 4 may pre-store information indicating the coefficient α. Furthermore, information indicating the coefficient α may be provided to the standard frame conversion evaluation value calculation unit 4 via various input means or communication means. The standard frame conversion evaluation value calculation unit 4 may also acquire information indicating the coefficient α stored in a storage device (not shown) directly or via a reading means (not shown) as needed.

The extended region evaluation value calculation unit 5 calculates the sum of the pixel values contained within the extended region A2 as the extended region evaluation value E3. In the example in Figure 5, the extended region evaluation value E3 is calculated for the pixel values of the 25 pixels contained within the extended region A2. The extended region evaluation value calculation unit 5 may also calculate other statistical values, such as the average of the pixel values of the pixels contained within the extended region A2, as the extended region evaluation value E3.

The evaluation value determination unit 6 determines the relative magnitudes of the inclusion area evaluation value E1, the reference frame equivalent evaluation value E2, and the extended area evaluation value E3. Based on the determination result, the evaluation value determination unit 6 selects the evaluation value E for the pixels within the reference frame F.

Next, the relationship between the reference frame F, the object OBJ, and the evaluation value calculated by the evaluation value determination unit 6 will be explained. In this embodiment, the size of the object OBJ is estimated based on the relative sizes of the inclusion area evaluation value E1, the reference frame conversion evaluation value E2, and the expanded 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, and the evaluation value E is calculated accordingly.

Case 1
Assume that the object OBJ is small enough to be contained within the containment area A1. Figure 6 schematically shows 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 containment area A1 and the extended area A2. Therefore, the containment area evaluation value E1 and the extended area evaluation value E3 can be expected to be the same value or an approximate value that can be considered the same value. On the other hand, the reference frame conversion evaluation value E2 will be a larger value than the containment area evaluation value E1 and the extended area evaluation value E3.

Therefore, if the standard frame conversion evaluation value E2 is greater than the inclusion area evaluation value E1 and the expanded area evaluation value E3, it can be determined that the object OBJ is of a size that is included within the inclusion area A1. In this case, the standard frame conversion evaluation value E2 is considered to be overestimating the area occupied by the object OBJ. Therefore, in Case 1, by using the inclusion area evaluation value E1 or the expanded 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 standard frame F can be suitably reflected.

Case 2
Let's consider a case where the object OBJ does not fit within the containment area A1, but is large enough to be contained within the reference frame F. Figure 7 schematically shows 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 containment area A1, it is expected that the extended area evaluation value E3 will be larger than the containment area evaluation value E1. Also, since the area of the part of the object OBJ that does not fit within the containment area A1 but extends into the reference frame F is small, it is expected that the reference frame equivalent evaluation value E2, calculated by dividing the containment area evaluation value E1 by the coefficient α = 0.95, will be larger than both the containment area evaluation value E1 and the extended area evaluation value E3.

Therefore, when the standard frame conversion evaluation value E2 is the largest and the inclusion area evaluation value E1 is the smallest, i.e., E1 < E3 < E2, it can be determined that the object OBJ is not contained within the inclusion area A1, but is contained within the standard frame F. In this case, the inclusion area evaluation value E1 is considered to underestimate the area occupied by the object OBJ, and the standard frame conversion evaluation value E2 is considered to overestimate the area occupied by the object OBJ. Therefore, in Case 2, by using the expanded 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 standard frame F can be suitably reflected.

Case 3
Let's consider the case where the object OBJ extends outside the reference frame F. Figure 8 schematically shows 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 area within the reference frame F and occupies all or part of the extended area A2. Therefore, the extended area evaluation value E3 is greater than the inclusion area evaluation value E1 and the reference frame conversion evaluation value E2. Note that, as in Case 2, the reference frame conversion evaluation value E2 is greater than the inclusion area evaluation value E1.

Therefore, when the extended area evaluation value E3 is the largest and the inclusion area evaluation value E1 is the smallest, i.e., E1 < E2 < E3, it can be determined that the object OBJ extends beyond the reference frame F. In this case, the inclusion area evaluation value E1 is considered to be underestimating the area occupied by the object OBJ. Furthermore, the extended area evaluation value E3 is considered to be an overestimation of the object OBJ within the reference frame F because it incorporates many pixel values from outside the reference frame F. Therefore, in Case 3, by using the reference frame-converted evaluation value E2 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.

Next, the evaluation value calculation operation in the information processing device 100 will be described. Figure 9 is a flowchart showing the evaluation value calculation operation in the information processing device according to Embodiment 1.

Step S1
The data reading unit 1 reads the target image data (IMG) that will be used to calculate the evaluation value.

Step S2
The reference frame setting unit 2 sets a reference frame F in the pixel arrangement of the target image data IMG. In this example, the reference frame F is positioned so that its upper left corner coincides with the upper left corner of one pixel. In this example, 16 pixels are contained within the reference frame F, and these 16 pixels constitute the containment area A1. On the right and below the containment area A1, the reference frame F crosses 9 pixels. Therefore, the extended area A2 contains 16 + 9 = 25 pixels.

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

Step S4
The reference frame equivalent evaluation value calculation unit 4 calculates the reference frame equivalent evaluation value E2 by dividing the sum of the pixel values contained within the reference frame F by a predetermined coefficient α. Here, α is set to 0.95.

Step S5
The extended region evaluation value calculation unit 5 calculates the total value of pixels included in the extended region A2 as the extended region evaluation value E3.

Step S6
The evaluation value determination unit 6 determines whether the expanded area evaluation value E3 is greater than the standard frame converted evaluation value E2. This allows the evaluation value determination unit 6 to determine whether the size of the object OBJ falls under case 3.

Step S7
If the expanded area evaluation value E3 is greater than the standard frame converted evaluation value E2, that is, if the size of the object OBJ corresponds to case 3, the evaluation value determination unit 6 selects the standard frame converted evaluation value E2 as the evaluation value E.

Step S8
If the expanded area evaluation value E3 is less than or equal to the standard frame converted evaluation value E2, that is, if the size of the object OBJ does not fall under Case 3, the evaluation value determination unit 6 determines whether the difference between the expanded area evaluation value E3 and the inclusion area evaluation value E1 is less than a predetermined value β. In other words, the evaluation value determination unit 6 determines whether -β < E3 - E1 < β, or in other words, whether |E3 - E1| < β. This allows the evaluation value determination unit 6 to determine whether the size of the object OBJ falls under Case 1.

The evaluation value determination unit 6 may pre-store information indicating a predetermined value β. Furthermore, information indicating the predetermined value β may be provided to the evaluation value determination unit 6 via various input means or communication means. The evaluation value determination unit 6 may also acquire information indicating the predetermined value β stored in a storage device (not shown) directly or via a reading means (not shown) as needed.

Step S9
If the difference between the expanded 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.

Step S10
If the difference between the expanded area evaluation value E3 and the inclusion area evaluation value E1 is greater than or equal to a predetermined value β, that is, if the size of the object OBJ corresponds to case 2, the evaluation value determination unit 6 determines the expanded area evaluation value E3 as the evaluation value E in the reference frame F.

According to the above procedure, a suitable value E can be determined from the inclusion area evaluation value E1, the reference frame conversion evaluation value E2, and the expanded area evaluation value E3, depending on the size of the object OBJ within the reference frame F.

This allows for the calculation of evaluation values equivalent to, or at least to, those of the reference image data REF, even when the target image data IMG has a different pixel size than the reference image data REF.

As a result, it is possible to evaluate objects captured in image data using evaluation values with equivalent or comparable accuracy, regardless of the imaging device used to acquire the image data.

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

In the above-described embodiment, it was explained that the evaluation value E is determined to be one of the inclusion area evaluation value E1, the reference frame converted evaluation value E2, or the expanded area evaluation value E3. In contrast, the evaluation value determination unit 6 may determine the evaluation value E as a value obtained by converting the inclusion area evaluation value E1 calculated for the inclusion area A1 to a value corresponding to the size of the reference frame F. Alternatively, the evaluation value determination unit 6 may determine the evaluation value E as the reference frame converted evaluation value E2 calculated based on the inclusion area evaluation value E1 and the coefficient α. As a result, the information processing device 100 may have a simpler configuration, comprising 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 , and an expanded area evaluation value calculation unit 5 .

In the above-described embodiment, the reference frame conversion evaluation value calculation unit 4 was described as calculating the reference frame conversion evaluation value E2 by dividing the total value of pixel values contained within the reference frame F by a coefficient α, which is the value obtained by dividing the area of the containing region A1 by the area of the reference frame F. Alternatively, although the calculation process is substantially the same, the reference frame conversion evaluation value calculation unit 4 may calculate the reference frame conversion evaluation value E2 by multiplying the total value of pixel values contained within the reference frame F by a coefficient, which is the value obtained by dividing the area of the reference frame F by the area of the containing region A1.

Although the evaluation value determination unit 6 was described as determining the relative magnitude of two values in steps S6 and S8 of Figure 9, the method for determining the relative magnitude of the values is not limited to this. The evaluation value determination unit 6 may determine whether the first value is greater than or equal to the second value, as in step S6 of Figure 9, or whether the first value is greater than or equal to the second value. Furthermore, the evaluation value determination unit 6 may determine whether the first value is less than or equal to the second value, as in step S8 of Figure 9, or whether the first value is less than or equal to the second value. In other words, when determining the relative magnitude of two values, the evaluation value determination unit 6 only needs to be able to distinguish at least when the first value is greater than the second value and when it is less than or equal to 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 greater than the second value, or the same processing as when the first value is less than the second value.

In the above-described embodiment, the data reading unit 1 was described as reading target image data (IMG) from the imaging device 1100, but this is merely an example. For example, the data reading unit 1 may read target image data (IMG) pre-stored 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 it may be provided separately from the information processing device 100. The image data and other information used by the information processing device 100 may be read as needed from the imaging device 1100 and part or all of a storage device that is part of the information processing device 100 or an external storage device.

The size and placement of the reference frame are merely examples; it can be any size as needed and placed at any position relative to the pixels of the target image data (IMG).

In the embodiments described above, the optical device according to this disclosure was primarily described as a hardware configuration, but it is not limited thereto. It is also possible to realize the optical device according to this disclosure by having a computer execute a computer program to perform any processing. These processes may be realized by having a computer, which includes at least one processor (e.g., a microprocessor, CPU, GPU, MPU, or DSP (Digital Signal Processor)), execute a program. Specifically, one or more programs containing instruction sets for causing the computer to perform algorithms related to these transmission signal processing or reception signal processing can be created and supplied to the computer.

Computer programs can be stored and supplied to a computer using various types of non-transitor computer-readable media. Non-transitor computer-readable media include various types of tangible storage media. Examples of non-transitor computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R/Ws, and semiconductor memory (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, RAMs (random access memory)). Furthermore, programs may be supplied to the computer via various types of temporary computer-readable media. Examples of temporary computer-readable media include electrical signals, optical signals, and electromagnetic waves. Temporary computer-readable media can be supplied to the computer via wired communication channels such as electric wires and optical fibers, or via wireless communication channels.

The following shows an example of a computer configuration for realizing the information processing device 100. Figure 10 is a diagram showing an example of a computer configuration 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; multiple computers may be used when performing distributed processing. As shown in Figure 10, the computer 9000 has, for example, a processor 9001, ROM (Read Only Memory) 9002, RAM (Random Access Memory) 9003, a storage unit 9004, a communication interface 9005, and a user interface 9006.

The processor 9001, ROM 9002, RAM 9003, storage unit 9004, communication interface 9005, and user interface 9006 are interconnected via bus 9007, enabling them to communicate with each other. While the operating system software necessary to run the computer is not described here, it will be appropriately implemented in computer 9000 as well.

ROM is composed of, for example, non-volatile semiconductor memory devices. ROM 9002 stores information such as various programs used by the computer 9000.

The storage unit 9004 is composed of various storage devices, such as hard disks and solid-state disks. Furthermore, the storage unit 9004 is not limited to storage devices installed in the computer 9000, but may also be an external storage device. The external storage device may be a cloud storage device connected to the computer 9000 via various communication means, such as a network. The storage unit 9004 stores information such as various programs and data used by the computer 9000.

The RAM 9003 is composed of volatile semiconductor memory devices, etc. Programs and data used by the processor 9001 are loaded into the RAM 9003 as needed from either or both of the ROM 9002 and the memory unit 9004.

The processor 9001 may be composed of, for example, a CPU (Central Processing Unit). Alternatively, the processor 9001 may include a GPU (Graphics Processing Unit) in addition to the CPU. A GPU is suitable for parallel processing of routine tasks, and by applying it to, for example, neural network processing, it is possible to 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 the data generated by the processing in the RAM 9003 or the storage unit 9004 as appropriate.

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 or wireless communication means. This allows the computer 9000 to communicate with other devices, systems, and sensors connected to the communication network.

The user interface 9006 includes, for example, a display unit that provides information to the user in a way that the user can perceive, such as through a display device, and an audio output unit that provides audio. The user interface 9006 also includes an input unit, such as a keyboard, mouse, and touch panel, that allows the user to input information into the computer 9000 through user operation. Furthermore, the user interface 9006 may include devices such as sensors that acquire information useful to the user.

Here, the computer 9000 is described as a single device, but this is merely an example. The computer 9000 may consist of multiple physically separated devices. Some of these devices may be portable, while others may be stationary.

The present disclosure has been described above with reference to embodiments, but the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure are possible, as can be understood by those skilled in the art within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

Each drawing is merely illustrative to illustrate one or more embodiments. Each drawing may be associated not only with one specific embodiment, but with one or more other embodiments. As those skilled in the art will understand, 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 embodiments not explicitly illustrated or described. Not all features or steps shown in any one drawing to illustrate an exemplary embodiment are necessarily required, and some features or steps may be omitted. The order of steps described in any of the drawings may be changed as appropriate.

1 Data reading unit 2 Reference frame setting unit 3 Inclusion area evaluation value calculation unit 4 Reference frame converted evaluation value calculation unit 5 Extended area evaluation value calculation unit 6 Evaluation value determination unit 100 Information processing device 1000 Optical device 1100 Imaging device 1200 Sample 9000 Computer 9001 Processor 9002 ROM
9003 RAM
9004 Memory Unit 9005 Communication Interface 9006 User Interface 9007 Bus A1 Inclusion Area A2 Extended Area E1 Inclusion Area Evaluation Value E2 Reference Frame Conversion Evaluation Value E3 Extended Area Evaluation Value F Reference Frame IMG Target Image Data OBJ Target Object REF Reference Image Data

Claims (13)

A data reading unit that reads the target image data captured from the sample,
A reference frame setting unit sets a reference frame for the target image data, which is defined to include an integer number of pixels of reference image data that have different pixel sizes from the target image data.
A first evaluation value calculation unit calculates a first evaluation value based on pixels included within the reference frame in the target image data,
A second evaluation value calculation unit 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.
A third evaluation value calculation unit calculates a third evaluation value based on the pixels included within the reference frame and the pixels partially included within the reference frame in the target image data.
The system includes 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 relative magnitudes of the first to third evaluation values,
Information processing device. 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 standard 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 standard frame.
The information processing apparatus according to claim 1. 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 apparatus according to claim 2. The evaluation value determination unit determines the first evaluation value or the third evaluation value as the evaluation value calculated for the reference frame when the third evaluation value is less than or equal to the second evaluation value and the difference between the third evaluation value and the first evaluation value is equal to the predetermined value.
The information processing apparatus according to claim 2. The evaluation value determination unit determines the first evaluation value or the third evaluation value as the evaluation value calculated for the reference frame 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 information processing apparatus according to claim 3. The first and third evaluation value calculation units calculate the first and third evaluation values, respectively, based on the sum of the pixel values of the pixels to be calculated.
The information processing apparatus according to claim 1 or 2. The first and third evaluation value calculation units calculate the first and third evaluation values, respectively, by removing the background component from the sum of the pixel values of the pixels to be calculated.
The information processing apparatus according to claim 6. The aforementioned reference frame is a rectangular area,
The reference frame setting unit sets the reference frame such that the vertices of the rectangle coincide with the vertices of one of the pixels.
The information processing apparatus according to claim 1 or 2. The predetermined coefficient is the value obtained by dividing the area of the region consisting of pixels contained within the reference frame in the target image data by the area of the reference frame.
The information processing apparatus according to claim 1 or 2. The predetermined coefficient is the value obtained by dividing the area of the region consisting of pixels contained within 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 coefficient .
The information processing apparatus according to claim 1 or 2. A data reading unit that reads the target image data captured from the sample,
A reference frame setting unit sets a reference frame for the target image data, which is defined to include an integer number of pixels of reference image data that have different pixel sizes from the target image data.
A first evaluation value calculation unit calculates a first evaluation value based on pixels included within the reference frame in the target image data,
A second evaluation value calculation unit 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.
The system includes an evaluation value determination unit that determines the second evaluation value as the evaluation value calculated for the reference frame,
Information processing device. The target image data obtained by imaging the sample is read,
A reference frame is defined to include an integer number of pixels from a reference image data whose pixel size differs from that of the aforementioned target image data, and this reference frame is set for the aforementioned target image data.
A first evaluation value is calculated based on the pixels contained within the reference frame in the target image data.
A second evaluation value is calculated 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.
In the aforementioned target image data, a third evaluation value is calculated based on the pixels that are included within the reference frame and the pixels that are partially included within the reference frame.
Based on the relative magnitudes of the first to third evaluation values, one of the first to third evaluation values is determined as the evaluation value calculated for the standard frame.
Information processing methods. The process involves reading the target image data captured from the sample,
A process of setting a reference frame for the target image data, defined to include an integer number of pixels from a reference image data whose pixel size differs from that of the target image data,
A process for calculating a first evaluation value based on the pixels contained within the reference frame in the target image data,
A process for calculating 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,
A process for calculating a third evaluation value based on the pixels included within the reference frame and the pixels partially included within the reference frame in the target image data,
The computer is instructed to perform a process of determining one of the first to third evaluation values as the evaluation value calculated for the reference frame, based on the relative magnitudes of the first to third evaluation values.
program.