JP2021143832A - Void detection device, void detection method, and void detection program - Google Patents

Abstract

To provide a void detection device capable of detecting voids with high accuracy.SOLUTION: A void detection device 20 detects voids of a sample based on a replica to which the shape of a surface of the sample is transferred. The void detection device 20 includes an input unit 210 and a detection unit 220. The input unit 210 accepts input of altitude information indicating the height of the replica at each of a plurality of positions in a detection target area of the replica. The detection unit 220 detects voids in a region of the detection target area where the height represented by the input altitude information is higher than a void altitude threshold value.SELECTED DRAWING: Figure 3

Description

The present invention relates to a void detection device, a void detection method, and a void detection program.

A void detection device that detects voids in a sample based on a replica in which the shape of the surface of the sample is transferred is known. A scanning electron microscope (SEM) is often used to observe the replica. However, when a scanning electron microscope is used, treatment for increasing the conductivity of the surface of the replica must be performed. Therefore, it is considered preferable to use an optical microscope for observing the replica.

For example, the void detection device described in Patent Document 1 has a reflectance (in this example, brightness) according to the reflectance of light on the surface of the replica at each of a plurality of positions in the detection target region of the replica. Reflectivity information representing the above is input, and voids are detected in a region where the reflectance represented by the input reflectance information is higher than the reflectance threshold value.

Japanese Unexamined Patent Publication No. 2008-209344

Replicas are often manufactured as follows: First, the surface of the sample is polished using abrasive grains. Next, the surface of the sample is corroded by applying the corrosive liquid to the surface of the sample. Next, a replica film including a base material layer made of a resin such as polyethylene and a transfer layer made of cellulose acetate is attached to a sample via a solvent (for example, methyl acetate or acetone) that dissolves the transfer layer. As a result, the shape of the surface of the sample is transferred to the transfer layer by softening the transfer layer. After the transfer layer has hardened, the replica film is stripped from the sample. In this way, replicas are manufactured.

By the way, in the replica, there are a convex portion formed by transferring the void and a concave portion formed by transferring the carbide (in other words, the precipitated carbide) deposited on the surface of the sample. do.

However, the reflectivity tends to be high in both the convex portion and the concave portion due to the fact that light is likely to be scattered. Therefore, precipitated carbides are likely to be erroneously detected as voids. As described above, the void detection device has a problem that the accuracy in detecting voids tends to be low.

One of the objects of the present invention is to detect voids with high accuracy.

On one side, the void detector detects voids in the sample based on a replica to which the shape of the surface of the sample has been transferred. The void detection device includes an input unit and a detection unit. In the input unit, altitude information indicating the height of the replica is input for each of a plurality of positions in the detection target area of the replica. The detection unit detects voids in a region of the detection target region where the height represented by the input altitude information is higher than the void altitude threshold value.

In another aspect, the void detection method detects voids in the sample based on a replica to which the shape of the surface of the sample has been transferred. In the void detection method, altitude information indicating the height of the replica is input for each of a plurality of positions in the detection target area of the replica, and the height represented by the input altitude information in the detection target area is input. This includes detecting voids in a region higher than the void altitude threshold.

In another aspect, the void detection program is a program that causes a computer to perform a process of detecting the voids of the sample based on a replica to which the shape of the surface of the sample is transferred. In the above process, altitude information indicating the height of the replica is input for each of a plurality of positions in the detection target area of the replica, and the height represented by the input altitude information in the detection target area is void. Includes detecting voids in regions above the altitude threshold.

Voids can be detected with high accuracy.

It is a block diagram which shows the structure of the void detection system of 1st Embodiment. It is a block diagram which shows the structure of the void detection apparatus of 1st Embodiment. It is a block diagram which shows the function of the void detection apparatus of 1st Embodiment. It is a flowchart which shows the void detection process executed by the void detection apparatus of 1st Embodiment. It is a flowchart which shows the void area extraction process executed by the void detection apparatus of 1st Embodiment. It is a block diagram which shows the function of the void detection apparatus of 2nd Embodiment. It is explanatory drawing which shows an example of the peeling area, the missing area, the mixing area, and the abrasive grain adhesion area which occur in a replica. It is a flowchart which shows the void detection process executed by the void detection apparatus of 2nd Embodiment. It is a flowchart which shows the 1st transcription abnormality region extraction processing executed by the void detection apparatus of 2nd Embodiment. It is a flowchart which shows the 2nd transcription abnormality region extraction processing executed by the void detection apparatus of 2nd Embodiment. It is a flowchart which shows the 3rd transcription abnormality region extraction process performed by the void detection apparatus of 2nd Embodiment. It is a block diagram which shows the function of the void detection apparatus of the 1st modification of 2nd Embodiment. It is a flowchart which shows the void detection process executed by the void detection apparatus of the 1st modification of 2nd Embodiment. It is a flowchart which shows the 4th transcription abnormality region extraction process performed by the void detection apparatus of the 1st modification of 2nd Embodiment.

Hereinafter, embodiments of the present invention relating to the void detection device, the void detection method, and the void detection program will be described with reference to FIGS. 1 to 14.

<First Embodiment>
(Overview)
The void detection device of the first embodiment detects the void of the sample based on the replica to which the shape of the surface of the sample is transferred. The void detection device includes an input unit and a detection unit. In the input unit, altitude information indicating the height of the replica is input for each of a plurality of positions in the detection target area of the replica. The detection unit detects voids in a region of the detection target region where the height represented by the input altitude information is higher than the void altitude threshold value.

For example, the height of the replica is detected by using a confocal laser scanning microscope. The height of the replica reflects the convex part of the replica with high accuracy. Therefore, according to the void detection device, it is possible to prevent the precipitated carbide from being erroneously detected as a void. In other words, voids can be detected with high accuracy.
Next, the void detection device of the first embodiment will be described in more detail.

(composition)
As shown in FIG. 1, the void detection system 1 includes a microscope device 10 and a void detection device 20.

The microscope device 10 includes a confocal laser scanning microscope. A confocal laser microscope uses a confocal (in other words, confocal) optical system. In this example, the confocal laser scanning microscope changes the position of the focal point in the height direction of the replica, and acquires the position and the intensity of the reflected light (in this example, the brightness) in association with each other, and obtains the obtained brightness. Detects the height of the replica based on the position where is maximum.
In this way, the microscope device 10 detects the height of the replica by using a confocal laser scanning microscope. The microscope device 10 may detect the height of the replica using a microscope different from the confocal laser scanning microscope.

The microscope device 10 outputs altitude information. The altitude information represents the height of the detected replica for each of the plurality of pixel positions in the detection target area. In other words, the altitude information is information in which the pixel position and the height of the replica at the pixel position are associated with each other for each of the plurality of pixel positions. In this example, the detection target area has a rectangular shape. Further, in this example, the plurality of pixel positions are grid-like positions having equal intervals in the first direction and equal intervals in the second direction orthogonal to the first direction. In this example, the height direction of the replica is the third direction orthogonal to the first direction and the second direction.

The void detection device 20 detects the void of the sample based on the replica to which the shape of the surface of the sample is transferred. In this example, the void detection device 20 detects the void of the sample based on the altitude information output by the microscope device 10.

As shown in FIG. 2, the void detection device 20 includes a processing device 21, a storage device 22, an input device 23, an output device 24, and a connecting device 25, which are connected to each other via a bus BU. The void detection device 20 may be represented as an information processing device or a computer. For example, the void detection device 20 is a personal computer, a smartphone, a mobile phone, a tablet terminal, or a server device. Further, the void detection device 20 may be composed of a plurality of devices connected so as to be able to communicate with each other. Further, the void detection device 20 may be integrated with the microscope device 10.

The processing device 21 controls the storage device 22, the input device 23, the output device 24, and the connection device 25 by executing the void detection program stored in the storage device 22. As a result, the processing device 21 realizes the functions described later.

In this example, the processing device 21 is a CPU (Central Processing Unit). The processing device 21 may include an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), or a DSP (Digital Signal Processor) in place of the CPU or in addition to the CPU.

In this example, the storage device 22 includes a volatile memory and a non-volatile memory. For example, the storage device 22 includes at least one of a RAM (Random Access Memory), a ROM (Read Only Memory), a semiconductor memory, an organic memory, an HDD (Hard Disk Drive), and an SSD (Solid State Drive).

The input device 23 inputs information from the outside of the void detection device 20. In this example, the input device 23 includes a keyboard and a mouse. The input device 23 may include a microphone.
The output device 24 outputs information to the outside of the void detection device 20. In this example, the output device 24 includes a display. The output device 24 may include a speaker.
The void detection device 20 may include a touch panel type display that constitutes both the input device 23 and the output device 24.

The connecting device 25 is connected to the microscope device 10 so that information can be exchanged. In this example, the connecting device 25 is connected to the microscope device 10 via a cable capable of transmitting an electric signal. The connecting device 25 may be wirelessly connected to the microscope device 10.
The connecting device 25 sends control information for controlling the microscope device 10 to the microscope device 10, and also receives output information (in this example, altitude information) sent from the microscope device 10.

(function)
As shown in FIG. 3, the function of the void detection device 20 includes an input unit 210, a detection unit 220, and an output unit 230.

The input unit 210 includes an altitude information input unit 211. The altitude information input unit 211 inputs altitude information indicating the height of each of the plurality of pixel positions in the detection target area of the replica.
The input unit 210 corrects the input altitude information. In this example, the correction of altitude information includes plane correction. The correction of altitude information may be omitted.

Planar correction corrects the height of the replica in the detection target area so as to remove the planar gradient. In this example, the plane correction includes a first-direction plane correction and a second-direction plane correction. The plane correction may include only one of the first-direction plane correction and the second-direction plane correction.

In the first direction plane correction, the input unit 210 acquires the first altitude change approximation function. In this example, the first altitude change approximation function is a linear function.
The first altitude change approximation function approximates the change in the height of the replica in the row pixel position group of interest in the first direction. The row of interest pixel position group is a plurality of pixel positions having a first reference pixel position in the second direction and lining up in a row in the first direction among the plurality of pixel positions in the detection target area. In this example, the second direction is orthogonal to the first direction.

For example, the first altitude change approximation function is obtained using the least squares method. In this example, the first reference pixel position is the center pixel position in the second direction in the detection target area. The first reference pixel position may be determined based on the information input by the user.

Further, in the first direction plane correction, the input unit 210 determines the first correction reference altitude. For example, the first correction reference altitude is the average value of the heights of the replicas in the row pixel position group of interest. The first correction reference altitude may be determined based on the information input by the user.

Further, in the first direction plane correction, the input unit 210 acquires the correction amount for each of the plurality of pixel positions in the first direction in the detection target area, and determines the pixel positions in the first direction. Each of the heights of the replicas in the row pixel position group that has and is lined up in a row in the second direction is corrected by subtracting the correction amount from the height. The correction amount is a value obtained by subtracting the determined first correction reference altitude from the value corresponding to the pixel position in the first direction of the first altitude change approximation function.
In this way, the input unit 210 performs the first-direction plane correction.

In the second direction plane correction, the input unit 210 acquires the second altitude change approximation function. In this example, the second altitude change approximation function is a linear function.
The second altitude change approximation function approximates the change in the height of the replica in the pixel position group of the column of interest in the second direction. The row of interest pixel position group is a plurality of pixel positions having a second reference pixel position in the first direction and lining up in a row in the second direction among the plurality of pixel positions in the detection target area.

For example, the second altitude change approximation function is obtained using the least squares method. In this example, the second reference pixel position is the central pixel position in the first direction in the detection target area. The second reference pixel position may be determined based on the information input by the user.

Further, in the second direction plane correction, the input unit 210 determines the second correction reference altitude. For example, the second correction reference altitude is the average value of the heights of the replicas in the pixel position group of the column of interest. The second correction reference altitude may be determined based on the information input by the user.

Further, in the second direction plane correction, the input unit 210 acquires the correction amount for each of the plurality of pixel positions in the second direction in the detection target area, and determines the pixel positions in the second direction. Each of the heights of the replicas in the row pixel position group that has and is lined up in a row in the first direction is corrected by subtracting the correction amount from the height. The correction amount is a value obtained by subtracting the determined second correction reference altitude from the value corresponding to the pixel position in the second direction of the second altitude change approximation function.
In this way, the input unit 210 performs the second direction plane correction.

The input unit 210 determines the reference plane altitude based on the corrected altitude information. In this example, the input unit 210 determines the average value of the heights of the replicas in the detection target area as the reference plane altitude. The input unit 210 may determine the mode of the height of the replica in the detection target area as the reference plane altitude.

The detection unit 220 includes a void region extraction unit 221. The void region extraction unit 221 executes the void region extraction process based on the altitude information corrected by the input unit 210.

In the void region extraction process, the void region extraction unit 221 executes background removal. In the void region extraction process, background removal may be omitted.
The background removal corrects the heights of the plurality of subregions included in the detection target region so as to remove the difference between the subregions of the background components among the heights of the replicas in the subregions.

In the background removal, the void region extraction unit 221 extracts a plurality of partial regions in the detection target region in which the height of the replica is within the reference altitude range and is larger than the reference area. In this example, each of the reference altitude range and the reference area has a predetermined value. At least one of the reference altitude range and the reference area may be determined based on the information input by the user.

Further, in the background removal, the void region extraction unit 221 acquires the partial region reference altitude for each of the extracted plurality of partial regions, and determines the height of the replica in the partial region from the height. Correct by reducing the area reference altitude. For example, the subregion reference altitude is the average value of the heights of the replicas in the subregion. The subregion reference altitude may be the mode of the height of the replica in the subregion.
In this way, the void region extraction unit 221 removes the background.

In the void region extraction process, the void region extraction unit 221 extracts the void region from the detection target region. In this example, the void region extraction unit 221 extracts the region of the detection target region where the height of the replica after the background removal is executed is higher than the void altitude threshold value as the void region. The void altitude threshold value is a value higher by the first margin amount from the reference plane altitude determined by the input unit 210. For example, the first margin amount is 0.010 μm to 2.0 μm. In this example, the first margin amount has a predetermined value. The first margin amount may be determined based on the information input by the user.

Further, in the void region extraction process, the void region extraction unit 221 acquires a void area for each of the extracted void regions. The void area is the area of the void region. In addition, in the void region extraction process, the void region extraction unit 221 acquires the void volume for each of the extracted void regions. The void volume is a value obtained by integrating the height of the replica over the void region.

Further, in the void region extraction process, the void region extraction unit 221 deletes the noise region from the extracted void region. In the void region extraction process, the noise region is a region in the extracted void region where the void volume is equal to or less than the first void volume threshold value. In this example, the first void volume threshold has a predetermined value. The first void volume threshold value may be determined based on the information input by the user.

Further, in the void region extraction process, the noise region is a void region extracted in place of or in addition to the region in which the void volume is equal to or less than the first void volume threshold value in the extracted void region. It may be a region in which the void area is equal to or less than the void area threshold value. Further, in the void region extraction process, the deletion of the noise region may be omitted.

Further, in the void region extraction process, the void region extraction unit 221 corrects the shape of the extracted void region. In this example, the correction of the shape of the void region includes a closing process and an expansion process. In the correction of the shape of the void region, at least one of the closing process and the expansion process may be repeatedly executed a plurality of times. The correction of the shape of the void region may include at least one of the closing process and the expansion process. Further, the correction of the shape of the void region may be omitted.

In the closing process, the expansion process is executed, and then the contraction process is executed. The expansion process is a process of adding a pixel position adjacent to the void region among the pixel positions not included in the void region to the void region. The shrinkage process is a process of deleting from the void region the pixel positions adjacent to the pixel positions not included in the void region among the pixel positions included in the void region.

In addition, in the void region extraction process, the void region extraction unit 221 deletes the missing particle adhering region from the shape-corrected void region. For example, the missing particle adhesion region is a region in which particles such as carbides (in other words, precipitated carbides) precipitated on the surface of the sample are attached to the transfer layer of the replica due to being missing from the sample (in other words, particles). It is a region where a convex portion is formed in the replica due to the adhesion of the particles).

In the void region extraction process, the missing particle adhesion region is a region in the shape-corrected void region where the void volume is equal to or less than the second void volume threshold value. In this example, the second void volume threshold has a predetermined value. The second void volume threshold value may be determined based on the information input by the user. The second void volume threshold is larger than the first void volume threshold.
In the void region extraction process, a process of correcting the shape of the void region (for example, an expansion process) may be executed after the deletion of the missing particle adhering region. Further, in the void region extraction process, the deletion of the missing particle adhesion region may be omitted.

The output unit 230 estimates the degree of deterioration of the sample based on the void region detected by the detection unit 220. In this example, the storage device 22 stores in advance the deterioration degree void area ratio relationship in which the deterioration degree of the sample and the void area ratio are associated with each other.

The output unit 230 acquires the total void area and the evaluation area based on the void region detected by the detection unit 220. The total void area is the sum of the void areas. The evaluation area is the area of the area (in this example, the detection target area) that is the target of the void region extraction process. The output unit 230 acquires the void area ratio, which is the ratio of the acquired total void area to the acquired evaluation area. The output unit 230 estimates the degree of deterioration of the sample based on the acquired void area ratio and the deterioration degree void area ratio relationship stored in the storage device 22.

The output unit 230 outputs detection result information based on the void region detected by the detection unit 220. The output of the detection result information may be omitted.
In this example, the detection result information includes the estimated degree of deterioration of the sample. In addition to the degree of deterioration of the sample, or instead of the degree of deterioration of the sample, the detection result information includes the void area, the void volume, the maximum void height, the absolute maximum void length, the evaluation area, the total number of voids, and the total void area. , The total void volume, the void area ratio, the maximum void area, the average void area, the total number of connected voids, and the average void length may be included.

The maximum void height is the maximum height of the replica in the void region. The absolute maximum void length is the maximum length of the void region in any direction. The total number of voids is the total number of void regions. The total void volume is the sum of the void volumes. The maximum void area is the maximum value of the void area. The void average area is the average value of the void area. The total number of connected voids is the total number of connected void regions. The connecting void region is a void region in which the absolute maximum void length is equal to or larger than a predetermined connecting void threshold value (for example, 1 μm to 10 μm). The void average length is the average value of the void absolute maximum length.

(motion)
Next, the operation of the void detection system 1 will be described with reference to FIGS. 4 and 5.
First, the void detection device 20 sends control information to the microscope device 10 according to the information input by the user. As a result, the microscope device 10 detects the height of the replica with respect to each of the plurality of pixel positions in the detection target region of the replica by using the confocal laser scanning microscope. The microscope device 10 outputs altitude information indicating the height of the replica detected for each of the plurality of pixel positions in the detection target area.

On the other hand, the processing device 21 of the void detection device 20 executes the void detection process shown in FIG.
Specifically, the processing device 21 is input with the altitude information output from the microscope device 10 (step S101).
Next, the processing device 21 executes plane correction on the input altitude information (step S102).

Next, the processing device 21 determines the reference plane altitude based on the altitude information on which the plane correction is executed (step S103).
Next, the processing device 21 executes a void region extraction process (step S104).
In the void region extraction process, the processing device 21 executes the process shown in FIG.
Specifically, the processing device 21 executes background removal on the altitude information for which plane correction has been executed (step S201).
Next, the processing device 21 extracts the void region based on the altitude information on which the background removal was executed and the determined reference plane altitude (step S202).
Next, the processing device 21 deletes the noise region from the extracted void region (step S203).
Next, the processing device 21 corrects the shape of the void region after the noise region is deleted (step S204).
Next, the processing device 21 deletes the missing particle adhesion region from the void region after the shape is corrected (step S205).
In this way, the processing device 21 executes the void region extraction process.
Next, the processing device 21 estimates the degree of deterioration of the sample based on the void region after the missing particle adhesion region is deleted (step S105).
Next, the processing device 21 outputs the detection result information based on the void region after the missing particle adhesion region is deleted (step S106).

As described above, the void detection device 20 of the first embodiment detects the void of the sample based on the replica to which the shape of the surface of the sample is transferred. The void detection device 20 is input with altitude information indicating the height of each of the plurality of pixel positions in the detection target area of the replica. The void detection device 20 detects voids in a region of the detection target region where the height represented by the input altitude information is higher than the void altitude threshold value.

In this example, the void detection system 1 uses a confocal laser scanning microscope. This will detect the height of the replica. The height of the replica reflects the convex part of the replica with high accuracy. Therefore, according to the void detection device 20, it is possible to prevent the precipitated carbide from being erroneously detected as a void. In other words, voids can be detected with high accuracy.

Further, the void detection device 20 of the first embodiment detects voids in a region of the detection target region where the void volume is larger than the first void volume threshold value.

Even if the height of the replica is higher than the void altitude threshold, if the volume of the region is too small, there is a high probability that the region is a region other than the void (in other words, noise). Therefore, in the void detection device 20, the void is detected in a region where the void volume is larger than the first void volume threshold value. According to this, voids can be detected with high accuracy.

Further, the void detection device 20 of the first embodiment detects voids in a region whose volume is larger than the second void volume threshold value in the detection target region. The second void volume threshold is larger than the first void volume threshold.

By the way, particles such as carbides (in other words, precipitated carbides) precipitated on the surface of the sample may adhere to the transfer layer of the replica due to being missing from the sample. As a result, in the replica, a convex portion having a volume smaller than that of the void may be formed. In this case, such a convex portion may be erroneously detected as a void. Therefore, in the void detection device 20, the void is detected in a region where the volume is larger than the second void volume threshold value. According to this, voids can be detected with high accuracy.

When the microscope device 10 includes an imaging optical system and a solid-state image sensor and outputs a captured image by imaging a replica using the imaging optical system and the solid-state image sensor, the output unit 230 uses the microscope device 10 to output a captured image. An image representing the extracted void region may be combined with the output captured image, and the combined image may be output.

Further, the void detection system 1 may continuously detect voids in a plurality of detection target regions connected in a grid pattern. In this case, the microscope device 10 continuously outputs altitude information to a plurality of detection target regions connected in a grid pattern. Further, the void detection device 20 continuously detects voids in a plurality of detection target regions. In this case, the void detection device 20 may synthesize an image in which images of a plurality of detection target regions are connected in a grid pattern (in other words, a tiling image) and output the combined image.

<Second Embodiment>
Next, the void detection device of the second embodiment will be described. The void detection device of the second embodiment is different from the void detection device of the first embodiment in that it detects voids in a region other than the transcription abnormality region. Hereinafter, the differences will be mainly described. In the description of the second embodiment, those having the same reference numerals as those used in the first embodiment are the same or substantially the same.

(composition)
The microscope device 10 of the second embodiment includes an imaging optical system and a solid-state image sensor in addition to the configuration of the microscope device 10 of the first embodiment, and images a replica using the imaging optical system and the solid-state image sensor. By doing so, the color on the surface of the replica is detected. In this example, the solid-state image sensor is a color CCD image sensor. CCD is an abbreviation for Charge-Coupled Device.

The microscope device 10 outputs color information. The color information represents the color on the surface of the replica detected for each of the plurality of pixel positions in the detection target area. In other words, the color information is information in which the pixel position and the color on the surface of the replica at the pixel position are associated with each other for each of the plurality of pixel positions. In this example, the color information may be represented as a captured image.

In this example, the color is represented by a color space such as an HLS color space or an RGB color space. In the HLS color space, a color is represented by three components consisting of a hue component, a saturation component, and a brightness (lightness, luminance, or integrity) component. Further, in the RGB color space, a color is represented by three components including a red (Red) component, a green (Green) component, and a blue (Blue) component.

The microscope device 10 uses a cofocal optical system to change the focal position in the height direction of the replica with respect to each of the plurality of pixel positions in the detection target region, and also changes the intensity of the reflected light (in this example). , Brightness), the maximum value (in other words, the maximum brightness) of the acquired brightness may be acquired, and color information representing the acquired maximum brightness may be output. In this case, the color information may be represented as an extended focus image (in other words, a full focus image). The extended focus image is information in which the pixel position and the maximum brightness at the pixel position are associated with each other for each of the plurality of pixel positions.

The microscope device 10 may output a composite image of the captured image and the extended focus image as color information. Further, in the void detection device 20A of the second embodiment, a captured image and an extended focus image are input from the microscope device 10, the input captured image and the extended focus image are combined, and the combined image is used as color information. You may.

(function)
As shown in FIG. 6, the function of the void detection device 20A of the second embodiment is an input unit instead of the input unit 210 and the detection unit 220 among the functions of the void detection device 20 of the first embodiment. It includes 210A and a detection unit 220A.

The input unit 210A includes a color information input unit 212A in addition to the functions of the input unit 210 of the first embodiment. The color information input unit 212A inputs color information representing the color on the surface of the replica at each of the plurality of pixel positions in the detection target region of the replica.

In addition to the functions of the detection unit 220 of the first embodiment, the detection unit 220A includes a first transcription abnormality region extraction unit 222A, a second transcription abnormality region extraction unit 223A, and a third transcription abnormality region extraction unit 224A. include.

By the way, as shown in FIG. 7A, when the replica film is peeled off from the sample, the transfer layer LT may be peeled off from the base material layer LB. In the region (in other words, the peeled region) RP where the transfer layer LT is peeled from the base material layer LB, the reflectivity tends to be high as in the void due to the tendency of light to be scattered. Therefore, the peeled region RP is likely to be erroneously detected as a void. On the other hand, in the replica, the peeled region RP has a higher replica height than the other regions.

Therefore, the detection unit 220A is a first portion of the detection target area in which the height of the replica is higher than the first altitude threshold value, based on the altitude information input by the altitude information input unit 211. Voids are detected in regions other than the transcription abnormality region. The first altitude threshold is higher than the void altitude threshold.

In this example, the first transcription abnormality region extraction unit 222A executes the first transcription abnormality region extraction process based on the altitude information corrected by the input unit 210.

In the first transcription abnormality region extraction process, the first transcription abnormality region extraction unit 222A extracts the first transcription abnormality region from the detection target region. In this example, the first transcription abnormality region extraction unit 222A extracts a region in which the height of the replica is higher than the first altitude threshold value as the first transcription abnormality region among the detection target regions. The first altitude threshold value is a value higher than the void altitude threshold value by the amount of the second margin. For example, the second margin amount is 0.50 μm to 5.0 μm. In this example, the second margin amount has a predetermined value. The second margin amount may be determined based on the information input by the user.

Further, in the first transcription abnormality region extraction process, the first transcription abnormality region extraction unit 222A acquires the first transcription abnormality volume for each of the extracted first transcription abnormality regions. The first transcription abnormality volume is a value obtained by integrating the height of the replica over the first transcription abnormality region.

Further, in the first transcription abnormality region extraction process, the first transcription abnormality region extraction unit 222A deletes the noise region from the extracted first transcription abnormality region. In the first transcription abnormality region extraction process, the noise region is a region in the extracted first transcription abnormality region in which the first transcription abnormality volume is equal to or less than the first transcription abnormality volume threshold value. In this example, the first transcription abnormal volume threshold has a predetermined value. The first transcription abnormality volume threshold value may be determined based on the information input by the user.

Further, in the first transcription abnormality region extraction process, the noise region is replaced with or said that the first transcription abnormality volume of the extracted first transcription abnormality region is equal to or less than the first transcription abnormality volume threshold. In addition to the region, the extracted first transcription abnormality region may be a region in which the first transcription abnormality area is equal to or less than the first transcription abnormality area threshold. The first transcription abnormality area is the area of the first transcription abnormality region. In this case, in the first transcription abnormality region extraction process, the first transcription abnormality region extraction unit 222A acquires the first transcription abnormality region for each of the extracted first transcription abnormality regions.
In the first transcription abnormality region extraction process, the deletion of the noise region may be omitted.

Further, in the first transcription abnormality region extraction process, the first transcription abnormality region extraction unit 222A corrects the shape of the extracted first transcription abnormality region. In this example, the correction of the shape of the first transcription abnormality region is the same as the correction of the shape of the void region in the void region extraction process, except that the first transcription abnormality region is used instead of the void region. The correction of the shape of the first transfer abnormality region may include a fill-in-the-blank process. The fill-in-the-blank process is a process of adding the pixel positions surrounded by the first transfer abnormality region to the first transfer abnormality region among the pixel positions not included in the first transfer abnormality region. Further, the correction of the shape of the first transfer abnormality region may be omitted.

In the void region extraction unit 221 of the second embodiment, the first transcription abnormality region after the shape is corrected by the first transcription abnormality region extraction unit 222A is a void from the processing target regions excluded from the detection target region. Extract the area. Therefore, according to the void detection device 20A, it is possible to prevent the peeled region RP from being erroneously detected as a void. In other words, voids can be detected with high accuracy.

Further, as shown in FIG. 7B, when the replica film is peeled off from the sample, a part of the transfer layer remains attached to the surface of the sample, so that a part of the transfer layer LT is left. May be missing. In the region where a part of the transfer layer LT is missing (in other words, the missing region) RL, the reflectivity tends to be high as in the case of voids due to the tendency of light to be scattered. Therefore, the missing region RL is likely to be erroneously detected as a void. On the other hand, in the replica, the missing area RL has a lower replica height than the other areas.

Therefore, the detection unit 220A is a second portion of the detection target area in which the height of the replica is lower than the second altitude threshold value, based on the altitude information input by the altitude information input unit 211. Voids are detected in regions other than the transcription abnormality region. The second altitude threshold is lower than the void altitude threshold.

In this example, the second transcription abnormality region extraction unit 223A executes the second transcription abnormality region extraction process based on the altitude information corrected by the input unit 210.

In the second transcription abnormality region extraction process, the second transcription abnormality region extraction unit 223A extracts the second transcription abnormality region from the detection target region. In this example, the second transcription abnormality region extraction unit 223A extracts a region in the detection target region where the height of the replica is lower than the second altitude threshold value as the second transcription abnormality region. The second altitude threshold value is a value lower than the reference plane altitude by the amount of the third margin. In this example, the third margin amount is 0.010 μm to 5.0 μm. In this example, the third margin amount has a predetermined value. The third margin amount may be determined based on the information input by the user.

Further, in the second transcription abnormality region extraction process, the second transcription abnormality region extraction unit 223A acquires the second transcription abnormality volume for each of the extracted second transcription abnormality regions. The second transcription abnormality volume is a value obtained by integrating the height of the replica over the second transcription abnormality region.

Further, in the second transcription abnormality region extraction process, the second transcription abnormality region extraction unit 223A deletes the noise region from the extracted second transcription abnormality region. In the second transcription abnormality region extraction process, the noise region is a region in the extracted second transcription abnormality region in which the second transcription abnormality volume is equal to or less than the second transcription abnormality volume threshold value. In this example, the second transcription abnormal volume threshold has a predetermined value. The second transcription abnormality volume threshold value may be determined based on the information input by the user.

Further, in the second transcription abnormality region extraction process, the noise region is replaced with or the region of the extracted second transcription abnormality region in which the second transcription abnormality volume is equal to or less than the second transcription abnormality volume threshold. In addition to the region, the extracted second transcription abnormality region may be a region in which the second transcription abnormality area is equal to or less than the second transcription abnormality area threshold. The second transcription abnormality area is the area of the second transcription abnormality region. In this case, in the second transcription abnormality region extraction process, the second transcription abnormality region extraction unit 223A acquires the second transcription abnormality region for each of the extracted second transcription abnormality regions.
In the second transcription abnormality region extraction process, the deletion of the noise region may be omitted.

Further, in the second transcription abnormality region extraction process, the second transcription abnormality region extraction unit 223A corrects the shape of the extracted second transcription abnormality region. In this example, the correction of the shape of the second transcription abnormality region is the same as the correction of the shape of the void region in the void region extraction process, except that the second transcription abnormality region is used instead of the void region. The correction of the shape of the second transfer abnormality region may include a fill-in-the-blank process. The fill-in-the-blank process is a process of adding the pixel positions surrounded by the second transfer abnormality region to the second transfer abnormality region among the pixel positions not included in the second transfer abnormality region. Further, the correction of the shape of the second transfer abnormality region may be omitted.

In the void region extraction unit 221 of the second embodiment, the second transcription abnormality region after the shape is corrected by the second transcription abnormality region extraction unit 223A is a void from the processing target regions excluded from the detection target region. Extract the area. Therefore, according to the void detection device 20A, it is possible to prevent the missing region RL from being erroneously detected as a void. In other words, voids can be detected with high accuracy.

By the way, in the replica, the peeled region RP is more likely to have a color in a specific color range than other regions.
Further, as shown in FIG. 7C, bubble BA may be mixed in the transfer layer LT in the replica. For example, in a replica, the region RM in which the bubble BA is mixed in the transfer layer LT (in other words, the mixed region) RM is more likely to have a color in a specific color range than other regions.
Further, as shown in FIG. 7D, in the replica, the abrasive grains PS used for polishing may adhere to the transfer layer LT. For example, in a replica, the region (in other words, the abrasive grain adhesion region) RA where the abrasive grains PS are attached to the transfer layer LT is more likely to have a color in a specific color range than other regions.

Therefore, the detection unit 220A is based on the color information input by the color information input unit 212A, and the third transfer is a part of the detection target area in which the color on the surface of the replica is within the color range. Voids are detected in areas other than the abnormal area.

In this example, the third transfer abnormality region extraction unit 224A executes the third transfer abnormality region extraction process based on the color information input by the color information input unit 212A.

In the third transcription abnormality region extraction process, the third transcription abnormality region extraction unit 224A extracts the third transcription abnormality region from the detection target region. In this example, the third transfer abnormality region extraction unit 224A extracts a region in the detection target region where the color on the surface of the replica is within the color range as the third transfer abnormality region. In this example, the color range is a range of values for each of the three components that make up the color space. The color range may be a range of values for each of the two components of the three components constituting the color space. Further, the color range may be a range of values for one of the three components constituting the color space. In this case, the color range may be the range of luminance.
In this example, the color range has a predetermined value. The color range may be determined based on the information input by the user.

Further, in the third transcription abnormality region extraction process, the third transcription abnormality region extraction unit 224A acquires the third transcription abnormality region for each of the extracted third transcription abnormality regions. The third transcription abnormality area is the area of the third transcription abnormality region.

Further, in the third transcription abnormality region extraction process, the third transcription abnormality region extraction unit 224A deletes the noise region from the extracted third transcription abnormality region. In the third transcription abnormality region extraction process, the noise region is a region in the extracted third transcription abnormality region in which the third transcription abnormality area is equal to or less than the third transcription abnormality area threshold value. In this example, the third transcription abnormality area threshold has a predetermined value. The third transcription abnormality area threshold value may be determined based on the information input by the user.

Further, in the third transcription abnormality region extraction process, the noise region is replaced with or the region of the extracted third transcription abnormality region in which the third transcription abnormality area is equal to or less than the third transcription abnormality area threshold. In addition to the region, it may be a region in the extracted third transcription abnormality region in which the third transcription abnormality volume is equal to or less than the third transcription abnormality volume threshold. The third transcription abnormality volume is a value obtained by integrating the height of the replica over the third transcription abnormality region. In this case, in the third transcription abnormality region extraction process, the third transcription abnormality region extraction unit 224A acquires the third transcription abnormality volume for each of the extracted third transcription abnormality regions.
In the third transcription abnormality region extraction process, the deletion of the noise region may be omitted.

Further, in the third transcription abnormality region extraction process, the third transcription abnormality region extraction unit 224A corrects the shape of the extracted third transcription abnormality region. In this example, the correction of the shape of the third transcription abnormality region is the same as the correction of the shape of the void region in the void region extraction process, except that the third transcription abnormality region is used instead of the void region. The correction of the shape of the third transfer abnormality region may include a fill-in-the-blank process. The fill-in-the-blank process is a process of adding the pixel positions surrounded by the third transfer abnormality region to the third transfer abnormality region among the pixel positions not included in the third transfer abnormality region. Further, the correction of the shape of the third transfer abnormality region may be omitted.

In the void region extraction unit 221 of the second embodiment, the third transcription abnormality region after the shape is corrected by the third transcription abnormality region extraction unit 224A is a void from the processing target regions excluded from the detection target region. Extract the area. Therefore, according to the void detection device 20A, it is possible to prevent the peeling region RP, the mixing region RM, or the abrasive grain adhesion region RA from being erroneously detected as voids. In other words, voids can be detected with high accuracy.

The third transfer abnormality region extraction unit 224A may extract the third transfer abnormality region for each of a plurality of color ranges different from each other.

The output unit 230 of the second embodiment uses the area of the region to be subjected to the void region extraction processing (in this example, the processing target region) as the evaluation area. The processing target region is a region in which the first transcription abnormality region, the second transcription abnormality region, and the third transcription abnormality region are excluded from the detection target region.

Therefore, according to the void detection device 20A of the second embodiment, the void area ratio can be obtained with high accuracy, so that the degree of deterioration of the sample can be estimated with high accuracy.

(motion)
Next, the operation of the void detection system 1 of the second embodiment will be described with reference to FIGS. 8 to 11.
First, the void detection device 20A sends control information to the microscope device 10 according to the information input by the user. As a result, the microscope device 10 detects the height of the replica with respect to each of the plurality of pixel positions in the detection target region of the replica by using the confocal optical system. The microscope device 10 outputs altitude information indicating the height of the replica detected for each of the plurality of pixel positions in the detection target area.

Further, the microscope device 10 detects the color on the surface of the replica with respect to each of the plurality of pixel positions in the detection target region of the replica by using the imaging optical system and the solid-state imaging device. The microscope device 10 outputs color information representing a color on the surface of the replica detected for each of a plurality of pixel positions in the detection target region.

On the other hand, the processing device 21 of the void detection device 20A executes the process shown in FIG. 8 instead of the process shown in FIG.
In the process shown in FIG. 8, in the process shown in FIG. 4, step S1011A is added between steps S101 and S102, and steps S1031A to steps S1031A to step S104 are inserted between steps S103 and S104. This is the process to which S1034A is added.

Specifically, the processing device 21 is input with the altitude information output from the microscope device 10 (step S101).
Next, the processing device 21 is input with the color information output from the microscope device 10 (step S1011A).
Next, the processing device 21 executes plane correction on the input altitude information (step S102).

Next, the processing device 21 determines the reference plane altitude based on the altitude information on which the plane correction is executed (step S103).

Next, the processing device 21 executes the first transcription abnormality region extraction process (step S1031A). In the first transcription abnormality region extraction process, the processing device 21 executes the process shown in FIG.
Specifically, the processing device 21 uses a region where the height of the replica is higher than the first altitude threshold value as the first transcription abnormality region based on the altitude information on which the plane correction is executed and the first altitude threshold value. Extract (step S301A).
Next, the processing device 21 deletes the noise region from the extracted first transfer abnormality region (step S302A).
Next, the processing device 21 corrects the shape of the first transfer abnormality region after the noise region is deleted (step S303A).
In this way, the processing device 21 executes the first transcription abnormality region extraction process.

Next, the processing device 21 executes the second transcription abnormality region extraction process (step S1032A). In the second transcription abnormality region extraction process, the processing device 21 executes the process shown in FIG.
Specifically, the processing device 21 uses a region where the height of the replica is lower than the second altitude threshold value as the second transcription abnormality region based on the altitude information on which the plane correction is executed and the second altitude threshold value. Extract (step S401A).
Next, the processing device 21 deletes the noise region from the extracted second transfer abnormality region (step S402A).
Next, the processing device 21 corrects the shape of the second transfer abnormality region after the noise region is deleted (step S403A).
In this way, the processing device 21 executes the second transcription abnormality region extraction process.

Next, the processing device 21 executes a third transcription abnormality region extraction process (step S1033A). In the third transcription abnormality region extraction process, the processing device 21 executes the process shown in FIG.
Specifically, the processing apparatus 21 extracts a region on the surface of the replica whose color is within the color range as a third transfer abnormality region based on the color information and the color range (step S501A).
Next, the processing device 21 deletes the noise region from the extracted third transfer abnormality region (step S502A).
Next, the processing device 21 corrects the shape of the third transfer abnormality region after the noise region is deleted (step S503A).
In this way, the processing device 21 executes the third transcription abnormality region extraction process.

Next, the processing device 21 sets a processing target area, which is a target area for the void region extraction processing (step S1034A). In this example, the processing device 21 has a first transcription abnormality region after the shape is corrected in the first transcription abnormality region extraction processing and a second transcription abnormality after the shape is corrected in the second transcription abnormality region extraction processing. A region excluding the region and the third transcription abnormality region after the shape is corrected in the third transcription abnormality region extraction process from the detection target region is set as the processing target region.

Next, the processing device 21 executes a void region extraction process (step S104). In this example, in the void region extraction process, the processing device 21 uses the processing target region instead of the detection target region as the region to be extracted from the void region. In other words, in this example, the processing device 21 extracts the void region from the processing target region in the void region extraction processing.

In the void region extraction process, the processing device 21 executes the process shown in FIG.
Specifically, the processing device 21 executes background removal on the altitude information for which plane correction has been executed (step S201).
Next, the processing device 21 extracts a void region from the processing target region based on the altitude information on which the background removal is executed and the determined reference plane altitude (step S202).

Next, the processing device 21 deletes the noise region from the extracted void region (step S203).
Next, the processing device 21 corrects the shape of the void region after the noise region is deleted (step S204).
Next, the processing device 21 deletes the missing particle adhesion region from the void region after the shape is corrected (step S205).
In this way, the processing device 21 executes the void region extraction process.
Next, the processing device 21 estimates the degree of deterioration of the sample based on the void region after the missing particle adhesion region is deleted (step S105).
Next, the processing device 21 outputs the detection result information based on the void region after the missing particle adhesion region is deleted (step S106).

As described above, according to the void detection device 20A of the second embodiment, the same actions and effects as those of the void detection device 20 of the first embodiment are exhibited.
Further, in the void detection device 20A of the second embodiment, the detection unit 220A is at least a part of the detection target area in which the height represented by the input altitude information is higher than the first altitude threshold value. 1 Voids are detected in regions other than the transcription abnormality region. Further, the first altitude threshold is higher than the void altitude threshold.

According to this, it is possible to prevent the peeled region RP from being mistakenly detected as a void. In other words, voids can be detected with high accuracy.

Further, in the void detection device 20A of the second embodiment, the detection unit 220A is at least a part of the detection target area in which the height represented by the input altitude information is lower than the second altitude threshold value. 2 Voids are detected in regions other than the transcription abnormality region. Further, the second altitude threshold is lower than the void altitude threshold.

According to this, it is possible to prevent the missing region RL from being erroneously detected as a void. In other words, voids can be detected with high accuracy.

Further, in the void detection device 20A of the second embodiment, the input unit 210A inputs color information representing a color on the surface of the replica at each of the plurality of pixel positions. Further, the detection unit 220A detects voids in a region other than the third transfer abnormality region, which is at least a part of the region in which the color represented by the input color information is within the color range, in the detection target region.

According to this, it is possible to prevent the peeling region RP, the mixing region RM, or the abrasive grain adhesion region RA from being erroneously detected as voids. In other words, voids can be detected with high accuracy.

By the way, in the void detection device 20A of the second embodiment, a region in which all of the first transcription abnormality region, the second transcription abnormality region, and the third transcription abnormality region are excluded from the detection target region is set as the processing target region. Set. In the void detection device 20A of the modified example of the second embodiment, at least one of the first transcription abnormality region, the second transcription abnormality region, and the third transcription abnormality region was excluded from the detection target region. The area may be set as the processing target area.

<First modification of the second embodiment>
Next, the void detection device of the first modification of the second embodiment will be described. The void detection device of the first modification of the second embodiment has a region where the height of the replica is considerably higher than that of the void detection device of the second embodiment in the region where the color is within the color range. It differs in that it is used as a target for detecting voids. Hereinafter, the differences will be mainly described. In the description of the first modification of the second embodiment, those having the same reference numerals as those used in the second embodiment are the same or substantially the same.

(function)
As shown in FIG. 12, the function of the void detection device 20B of the first modification of the second embodiment is a detection unit instead of the detection unit 220A of the functions of the void detection device 20A of the second embodiment. Includes 220B.

The detection unit 220B is the same as the detection unit 220A except that the detection unit 220B includes the fourth transcription abnormality region extraction unit 225B instead of the third transcription abnormality region extraction unit 224A among the functions of the detection unit 220A of the second embodiment. Has the function of.

By the way, in the region of the replica to which a considerably deep void is transferred, the color tends to fall into a specific color range. Therefore, such voids may be included in the region where the color is within a specific color range.

Therefore, the detection unit 220B sets the height of the replica in the detection target area in which the color on the surface of the replica is within the color range based on the color information input by the color information input unit 212A. Voids are detected in a region other than the fourth transcription abnormality region, which is at least a part of the region lower than the third altitude threshold. The third altitude threshold is higher than the void altitude threshold.

In this example, the fourth transcription abnormality region extraction unit 225B extracts the fourth transcription abnormality region based on the color information input by the color information input unit 212A and the altitude information corrected by the altitude information input unit 211. Execute the process.

In the fourth transcription abnormality region extraction process, the fourth transcription abnormality region extraction unit 225B extracts the third transcription abnormality region, deletes the noise region, and performs the third transcription abnormality region as in the third transcription abnormality region extraction processing. The shape of is corrected. In the fourth transfer abnormality region extraction process, at least one of the deletion of the noise region and the correction of the shape of the third transfer abnormality region may be omitted.

Further, in the fourth transcription abnormality region extraction process, the fourth transcription abnormality region extraction unit 225B is a region in the third transcription abnormality region after the shape is corrected, in which the height of the replica is lower than the third altitude threshold value. Is extracted as the fourth transcription abnormality region. The third altitude threshold value is higher than the void altitude threshold value by the amount of the fourth margin. For example, the fourth margin amount is 0.50 μm to 5.0 μm. In this example, the fourth margin amount is smaller than the second margin amount. In this example, the fourth margin amount has a predetermined value. The fourth margin amount may be determined based on the information input by the user.

In the void region extraction unit 221 of the first modification of the second embodiment, the fourth transcription abnormality region extracted by the fourth transcription abnormality region extraction unit 225B is excluded from the detection target region from the processing target region. Extract the void region. Therefore, according to the void detection device 20B, it is possible to suppress that a considerably deep void is not detected. Therefore, voids can be detected with high accuracy.

The output unit 230 of the first modification of the second embodiment uses the area of the region to be subjected to the void region extraction processing (in this example, the processing target area) as the evaluation area. The processing target region is a region in which the first transcription abnormality region, the second transcription abnormality region, and the fourth transcription abnormality region are excluded from the detection target region.

Therefore, according to the void detection device 20B of the first modification of the second embodiment, the void area ratio can be obtained with high accuracy, so that the degree of deterioration of the sample can be estimated with high accuracy.

(motion)
Next, the operation of the void detection system 1 of the first modification of the second embodiment will be described with reference to FIGS. 13 and 14.
First, the void detection device 20B sends control information to the microscope device 10 according to the information input by the user. As a result, the microscope device 10 detects the height of the replica with respect to each of the plurality of pixel positions in the detection target region of the replica by using the confocal optical system. The microscope device 10 outputs altitude information indicating the height of the replica detected for each of the plurality of pixel positions in the detection target area.

Further, the microscope device 10 detects the color on the surface of the replica with respect to each of the plurality of pixel positions in the detection target region of the replica by using the imaging optical system and the solid-state imaging device. The microscope device 10 outputs color information representing a color on the surface of the replica detected for each of a plurality of pixel positions in the detection target region.

On the other hand, the processing device 21 of the void detection device 20B executes the process shown in FIG. 13 instead of the process shown in FIG.
The process shown in FIG. 13 is a process in which step S1033A and step S1034A are replaced with steps S1033B and S1034B in the process shown in FIG.

Specifically, the processing device 21 executes the processing of steps S101 to S1032A as in the second embodiment.
Next, the processing device 21 executes the fourth transcription abnormality region extraction process (step S1033B). In the fourth transcription abnormality region extraction process, the processing device 21 executes the process shown in FIG.
The process shown in FIG. 14 is a process in which step S5031B is added after step S503A in the process shown in FIG.

Specifically, the processing apparatus 21 extracts a region on the surface of the replica whose color is within the color range as a third transfer abnormality region based on the color information and the color range (step S501A).
Next, the processing device 21 deletes the noise region from the extracted third transfer abnormality region (step S502A).
Next, the processing device 21 corrects the shape of the third transfer abnormality region after the noise region is deleted (step S503A).
Next, the processing device 21 extracts a region in which the height of the replica is lower than the third altitude threshold value as the fourth transcription abnormality region in the fourth transcription abnormality region after the shape is corrected (step S5031B).
In this way, the processing device 21 executes the fourth transcription abnormality region extraction process.

Next, the processing device 21 sets a processing target area, which is a target area for the void region extraction processing (step S1034B). In this example, the processing device 21 has a first transcription abnormality region after the shape is corrected in the first transcription abnormality region extraction processing and a second transcription abnormality after the shape is corrected in the second transcription abnormality region extraction processing. A region excluding the region and the fourth transcription abnormality region extracted in the fourth transcription abnormality region extraction process from the detection target region is set as the processing target region.

Next, the processing device 21 executes a void region extraction process (step S104). In this example, in the void region extraction process, the processing device 21 uses the processing target region instead of the detection target region as the region to be extracted from the void region. In other words, in this example, the processing device 21 extracts the void region from the processing target region in the void region extraction processing.

In the void region extraction process, the processing device 21 executes the process shown in FIG. 5 as in the second embodiment.
Next, the processing device 21 estimates the degree of deterioration of the sample based on the void region after the shape is corrected (step S105).
Next, the processing device 21 outputs the detection result information based on the void region after the shape is corrected (step S106).

As described above, according to the void detection device 20B of the first modification of the second embodiment, the same actions and effects as those of the void detection device 20A of the second embodiment are exhibited.
Further, in the void detection device 20B of the first modification of the second embodiment, the detection unit 220B inputs in the detection target area in which the color represented by the input color information is within the color range. Voids are detected in a region other than the fourth transcription abnormality region, which is at least a part of the region in which the height represented by the obtained altitude information is lower than the third altitude threshold. Further, the third altitude threshold is higher than the void altitude threshold.

According to this, among the regions where the color is within the color range, the region where the height of the replica is considerably high is used as a target for detecting voids. This makes it possible to prevent the detection of considerably deep voids. Therefore, voids can be detected with high accuracy.

By the way, in the void detection device 20B of the first modification of the second embodiment, the first transcription abnormality region, the second transcription abnormality region, and the fourth transcription abnormality region are all excluded from the detection target region. Is set as the processing target area. The void detection device 20B of another modification of the first modification of the second embodiment has at least one of a first transcription abnormality region, a second transcription abnormality region, and a fourth transcription abnormality region. An area excluded from the detection target area may be set as the processing target area.

The present invention is not limited to the above-described embodiment. For example, various modifications that can be understood by those skilled in the art may be made to the above-described embodiments without departing from the spirit of the present invention.

1 Void detection system 10 Microscopes 20, 20A, 20B Void detection device 21 Processing device 22 Storage device 23 Input device 24 Output device 25 Connection device 210, 210A Input unit 211 Advanced information input unit 212A Color information input unit 220, 220A, 220B Detection unit 221 Void region extraction unit 222A First transcription abnormality region extraction unit 223A Second transcription abnormality region extraction unit 224A Third transcription abnormality region extraction unit 225B Fourth transcription abnormality region extraction unit 230 Output unit BA Bubble BU bus LB Base material layer LT transfer layer PS abrasive grain RA abrasive grain adhesion area RL missing area RM mixed area RP peeling area

Claims (9)

A void detection device that detects voids in a sample based on a replica to which the shape of the surface of the sample is transferred.
An input unit for inputting altitude information indicating the height of the replica at each of a plurality of positions in the detection target area of the replica.
A detection unit that detects the void in a region of the detection target region in which the height represented by the input altitude information is higher than the void altitude threshold value.
A void detection device. The void detection device according to claim 1.
The detection unit is a region other than the first transcription abnormality region, which is at least a part of the region in which the height represented by the input altitude information is higher than the first altitude threshold value, in the detection target region. Detect and
The void detection device, wherein the first altitude threshold value is higher than the void altitude threshold value. The void detection device according to claim 1 or 2.
The detection unit is a region other than the second transcription abnormality region, which is at least a part of the region in which the height represented by the input altitude information is lower than the second altitude threshold value, in the detection target region. Detect and
The void detection device, wherein the second altitude threshold value is lower than the void altitude threshold value. The void detection device according to any one of claims 1 to 3.
Color information representing the color on the surface of the replica at each of the plurality of positions is input to the input unit.
The detection unit detects the void in a region other than the third transfer abnormality region, which is at least a part of the region in which the color represented by the input color information is within the color range, in the detection target region. , Void detector. The void detection device according to claim 4.
In the detection unit, the height represented by the input altitude information is greater than the third altitude threshold value in the region in which the color represented by the input color information is within the color range of the detection target region. The void was detected in a region other than the fourth transcription abnormality region, which is at least a part of the low region.
The void detection device, wherein the third altitude threshold value is higher than the void altitude threshold value. The void detection device according to any one of claims 1 to 5.
The detection unit is a void detection device that detects the void in a region of the detection target region whose volume is larger than the first void volume threshold value. The void detection device according to claim 6.
The detection unit detects the void in a region whose volume is larger than the second void volume threshold value in the detection target region.
A void detection device in which the second void volume threshold value is larger than the first void volume threshold value. A void detection method for detecting voids in a sample based on a replica to which the shape of the surface of the sample is transferred.
The altitude information indicating the height of the replica is input for each of the plurality of positions in the detection target area of the replica.
The void is detected in a region of the detection target region where the height represented by the input altitude information is higher than the void altitude threshold value.
Void detection methods, including that. A void detection program that causes a computer to execute a process of detecting a void of the sample based on a replica to which the shape of the surface of the sample is transferred.
The above processing
The altitude information indicating the height of the replica is input for each of the plurality of positions in the detection target area of the replica.
The void is detected in a region of the detection target region where the height represented by the input altitude information is higher than the void altitude threshold value.
Void detection program, including that.

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