JP2003042934A - Dust detector for clean room, dust detection system, measurement instrument for fine particulate, and method of detecting dust - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a dust detection system capable of accurately detecting dust. SOLUTION: The image of a dust collection sheet 11 which accumulated with the dust on a surface 11s is transmitted through the diffusion sheet 12 in dust detector 10, and photographed by a CCD camera 15 via a lens 13 and a lens 14. The image data of the image, photographed by the dust detector 10, are transmitted to an analysis computer 30 via a cable 18. The image data is processed by a double circumferential difference calculus in the computer 30, to detect the number and a shape of the dust.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a dust detection device used for managing dust generation in a clean room or the like.

[0002]

2. Description of the Related Art In recent years, various precision instruments such as liquid crystal displays (LCD) and semiconductor devices have been manufactured. In these precision instruments, dust contamination can cause equipment failure, so they are manufactured in locations such as clean rooms where dust control is performed. In a clean room,
Many kinds of dust are generated, such as dust adhering to the materials of manufactured products, threads coming out of the worker's clothes and wastes falling from the worker's body, rubber of the conveyor for flowing the product, etc. Therefore, in order to prevent the generation of dust, it is necessary to detect these dusts, specify the type of the dusts, and take measures to prevent the generation of dusts. As a specific example of a device for detecting dust, there is a cyclone dust collecting device that sucks the external atmosphere and counts the fine particles adsorbed on the filter.

[0003]

However, in the case of measuring by sucking air like a dust collector for a cyclone, turbulence of the air flow occurs in the clean room during measurement, and the source of dust is accurately grasped. However, it is not efficient because it is necessary to count the particles adhering to the filter one by one. Therefore, in order to prevent the generation of dust, it is desired to develop a method capable of detecting dust accurately and efficiently. The present invention has been made based on such a technical problem, and an object thereof is to provide a dust detection device, a dust detection system, a particle measuring instrument, and a dust detection method capable of accurately detecting dust. To do.

[0004]

According to the above object, a dust detection device for a clean room according to the present invention has a light transmitting portion having an incident surface for receiving falling dust and an emitting surface for emitting light incident from the surface. And an image capturing unit that captures the light emitted from the light transmitting unit. With this dust detection device, an image of dust on the incident surface can be captured, and dust can be easily detected from this image. Further, in this dust detection device, since the image is picked up from the emission surface side, the dust is prevented from falling on the incident surface side, and the air flow at the measurement location is not disturbed. Can measure accurately.

The dust detection device can be constructed by laminating the image pickup section and the light transmission section. In this case, since the light transmission part and the imaging part are continuous, the dust is reflected relatively clearly in the captured image, and therefore the detection of the dust is easy. Here, the term “stacked” may mean that another layer is interposed between the image pickup section and the light transmission section, or that there may be a slight gap between the image pickup section and the light transmission section. .

Further, the dust detection device may have a structure having a deflecting unit for deflecting the light emitted from the light transmitting unit and a lens for forming an image of the deflected light on the image pickup unit. By providing the deflecting unit, the image capturing unit does not have to be provided directly below the light transmitting unit, so that the height of the dust detection device itself can be suppressed. Further, since the image formed on the imaging unit can be made into an appropriate size by the lens, the measurement area for detecting dust can be adjusted. In the case of such a configuration, when the light diffusing unit is disposed between the light transmitting unit and the image capturing unit, it is possible to reduce the uneven brightness of light that occurs when the light source relies on external light. Since the background surface can be brought close to uniform illuminance, it becomes easy to detect dust.

The present invention can also be viewed as a dust detection system. The dust detection system includes an image capturing unit that captures transmitted light of a measurement unit that places dust on its surface, and a contrast capturing unit that captures the contrast of the captured image.
Based on the magnitude relationship of the contrast of the image, a detection unit that detects dust existing in the measurement unit, and a totalization result output unit that analyzes the dust identified by the detection unit and outputs the dust state data. Is characterized by. By collecting the detected dust, a predetermined location (place where the measurement unit is installed)
It is possible to understand the degree of dust generation in the area. Here, the dust state data means data indicating the state of dust that has occurred, such as the shape and size of dust, and the distribution according to the number and type of dust.

The dust detection system of the present invention can be considered from another point of view. Specifically, the dust detection system is a dust detection system that includes a plurality of dust detection devices and an analysis computer that collects the data output from the dust detection devices. The analysis computer is provided with a dust receiving unit that can be placed on the image capturing unit, an image capturing unit that captures an image of the dust receiving unit, and a transmitting unit that transmits the image data of the image captured by the image capturing unit to the analysis computer. It is characterized by further comprising: identification means for analyzing the image data sent from the detection device to identify dust, totaling means for totaling the identified dust, and display means for displaying data based on the totalized result. . As described above, by using a plurality of dust detection devices, it is possible to simultaneously measure dust at a plurality of points, and the measurement results can be integrated and aggregated by an analysis computer. It should be noted that the analysis computer can be composed of one unit or a plurality of units. The dust detection device and the analysis computer can be connected by wire or wirelessly. It is preferable that the dust detection system further includes a sorting unit that sorts the dusts identified by the identifying unit by type, and the display unit displays the aggregation result by type, for example, in a histogram or the like. By referring to such a result, it becomes easy to identify the cause of dust generation. However, the data based on the aggregated result displayed by the display means is not particularly limited, and for example, the aggregated result may be displayed as a numerical value as it is, and whether or not the result satisfies the reference value depending on the aggregated result. It may also indicate the determination made such as.

From a further viewpoint, the present invention can be understood as a measuring device for measuring fine particles. In the measurement of the fine particles, the fine particles contained in the image are grasped by detecting the contrast of the captured image.
The brightness value of the image may be detected to detect the contrast. The outline of this procedure is as follows. (a) An image to be evaluated is divided into a plurality of pixels. (b) The brightness values of a plurality of pixels existing at a position n pixels away from a specific pixel (target pixel) and surrounding the target pixel are integrated, and the brightness value of the target pixel is subtracted from the integrated value. Perform this process for all the divided pixels,
Obtain brightness information. (c) The luminance values of a plurality of pixels existing at a position separated by m (n <m) pixels from the target pixel and surrounding the target pixel are integrated, and the brightness value of the target pixel is subtracted from the integrated value. This process is executed for all divided pixels to obtain luminance information. (d) By using the brightness information based on the two brightness information obtained in (b) and (c) above, a quantitative evaluation close to a sensory test can be achieved. Here, the brightness information obtained in (b) and (c) is the difference value between the pixel of interest and the pixels existing around it. That is,
The brightness information obtained by (b) and (c) is a value that should be called an ambient difference value, and the brightness information obtained by (d) is based on two ambient difference values, and is therefore called a double ambient difference value. To be done. Hereinafter, a method of obtaining the surrounding difference value and the double surrounding difference value is referred to as a surrounding difference method and a double surrounding difference method.

Specifically, the measuring device has a flat measuring surface on which fine particles can be placed and an image pickup section for picking up the image of the measuring surface, which is housed in one housing, and a picked-up image. An image processing unit that divides the image data regarding the image data, an arithmetic unit that performs a calculation process represented by the following formula (1) on all the divided images, and obtains P (x, y) that is brightness information,

[Formula 5] (However, pi, po, α, and I (x, y) are defined as follows. Pi: The first pixel of the image data that is line-symmetric with respect to the position indicated by (x, y). Luminance value po of the pixels forming the group: Among the image data, a second pixel group which is line-symmetrical with respect to the position indicated by (x, y) and which is located outside the first pixel group. Luminance value I (x, y) of the constituent pixel: luminance value a, b, α of the pixel at the position indicated by (x, y) and a positive number) An image based on the luminance information obtained by the calculating means Contrast detection means for detecting the contrast of the, the identification means for identifying the particles present on the measurement surface based on the magnitude relationship of the contrast, and the result output means for outputting the identification result by the identification means. To do.

Here, P (x, y) is calculated, but p (x, y) is calculated.
i: the luminance value of the pixels forming the first pixel group that is line-symmetrical with respect to the position indicated by (x, y) in the image data, po: (x, y) in the image data It is a luminance value of a pixel which is line-symmetrical with respect to the position shown by and which constitutes a second pixel group located outside the first pixel group. Therefore, P (x, y) is the above-mentioned double ambient difference value, and the luminance information can be obtained.
In addition, the first pixel group is configured by pixels existing at positions separated by n pixels (n is an integer of 1 or more) from the pixels existing at the position (x, y), and the second pixel group Pixel group
m pixels from the pixel existing at the position indicated by (x, y)
It can be composed of pixels existing at positions separated by (m> n integer). Then, by appropriately setting n and m, it is possible to extract various forms of uneven brightness. Further, the coefficients a and b respectively define weighting when adding the inner difference value and the outer difference value. Therefore, a and b satisfy a + b = 1.0, and a and b will be determined according to the characteristics of the image to be evaluated. Here, it is allowed that a or b becomes 0 (zero). In addition, the α can be determined according to the value of the m. For example, if the value of m becomes large, I (x, y) can be ignored with respect to the sum of po. In that case, it is also possible to carry out the arithmetic processing by setting α to 0 (zero).

Alternatively, as a measuring device for measuring fine particles,
A measurement surface imaging unit that accommodates a flat measurement surface on which fine particles can be placed and an imaging unit that captures the measurement surface in one housing, an image processing unit that divides image data relating to the captured image, and a division unit. Arithmetic means for executing arithmetic processing represented by the following equation (2) on the image data thus obtained;

[Formula 6] (However, pi: luminance value of pixels forming a pixel group surrounding a pixel existing at a position indicated by (x, y) in the image data, I (x, y): indicated by (x, y) (Brightness value of pixel at position) Contrast detection unit that detects the contrast of the captured image based on the brightness information obtained by the calculation unit, and distinguishes the fine particles present on the measurement surface based on the magnitude relationship of the contrast of the captured image And a result output means for outputting the identification result of the identification means.

Further, the present invention can be understood as a dust detection method. The dust detection method is performed based on a step of capturing an image of a flat portion on which dust is placed, a division step of dividing image data of the captured image into a plurality of pixels, and a distribution of luminance values of the plurality of pixels. A target pixel setting step of setting a pixel, a difference calculation step of calculating a difference between a brightness value of the target pixel and a sum of brightness values of pixels forming a pixel group surrounding the target pixel, and the target pixel is dust based on the difference. It is characterized by including a determination step of determining whether or not it represents, and a totaling step of totaling the objects determined as dust in the determination step. When this dust detection method has a brightness difference detection step of detecting the brightness difference between the pixel of interest and the pixel group when light is transmitted,
When the difference is equal to or larger than a predetermined threshold value in the determination step, it can be determined that the pixel of interest represents dust.

The present invention also provides, as a dust detection method in a clean room, a step of receiving dust that has fallen in the clean room for a predetermined period of time, a step of capturing a transmission image of the flat section that has received the dust, and an image of the transmitted transmission It has a step of storing image data of an image and a step of analyzing the image data to detect dust. In this case, the method further includes a step of capturing an initial transmission image of the flat portion before the dust is received by the flat portion, and the step of detecting the dust is compared with the initial transmission image and the transmission image within a predetermined time. The received dust can be detected more accurately. It should be noted that when the image data is subjected to the shading processing, dust in the image is more clearly exposed, which facilitates detection. In addition, in the step of detecting dust, the shadow formed by the dust on the flat portion,
The dust can be detected by identifying the uneven brightness in the flat portion and extracting the shadow data of the dust.

[0015]

BEST MODE FOR CARRYING OUT THE INVENTION The present invention will be described below in detail based on the embodiments shown in the accompanying drawings. FIG. 1 is a diagram for explaining the configuration of the dust detection system according to the first embodiment. As shown in FIG. 1, a dust detection system for measuring dust (measuring device for measuring fine particles) 1
Is a dust detector (dust detector) 10 and a cable 18
The analysis computer 30 is connected to the dust detector 10 via the. Although not shown,
A plurality of dust detectors 10 are connected to the analysis computer 30.

The dust detector 10 is a dust collecting plate (light transmitting portion, measuring portion, dust receiving means, flat portion) on which dust that falls freely is placed.
11, a diffusing plate (light diffusing unit, light diffusing means) 12 stacked below the dust collecting plate 11, a mirror (deflecting unit) 13 that reflects the light transmitted through the dust collecting plate 11 and the diffusing plate 12, Lens 1
4 and a CCD camera (image pickup unit, image pickup means) 15 for picking up an image.

The dust collecting plate 11 is configured to transmit light. In the present embodiment, the dust placed on the surface 11s of the dust collecting plate 11 is measured. The surface 11s of the dust collecting plate 11 is flat so that an image of dust can be easily captured. Further, in the case of measuring dust several times, it is preferable to use a dust collecting plate 11 having high strength in consideration of removing dust on the surface 11s before the measurement. Examples thereof include sapphire glass and crystal glass.

The diffusion plate 12 diffuses the light transmitted through the dust collecting plate 11, and is not particularly limited as long as it can diffuse the light. As a specific example, an acrylic plate or the like having a thickness of about 2 to 3 mm can be mentioned. The mirror 13 reflects the light transmitted through the diffusion plate 12 toward the CCD camera 15. It should be noted that instead of the mirror 13, a lens, a deflector, or the like that can deflect incident light in a predetermined direction can be applied.

The lens 14 forms an image of the light reflected by the mirror 13 on the CCD camera 15. The lens 14 is not particularly limited, but it is preferable that the depth of field can be set from the dust collecting plate 11 to the diffusing plate 12, and it is more preferable to focus on the surface 11s of the dust collecting plate 11. It is possible. As a specific example, a target lens including a plurality of lenses such as a Gauss lens and a Tessa lens capable of correcting aberration due to the color of light can be cited.

The CCD camera 15 may be of a black-and-white type or a color type, but in the present embodiment, a black-and-white type which is advantageous in terms of the current image pickup capability is adopted. In addition, in the present embodiment, the CCD camera 15 images the entire area of the dust collecting plate 11. The image picked up by the CCD camera 15 is converted into an image signal, and a cable 18 is supplied from an interface (sending means) not shown.
Is sent to the analysis computer 30 via.

The analysis computer 30 is the CCD camera 1
An image processing unit (contrast grasping unit, image processing unit, which performs a predetermined process described later in detail on the image captured in 5).
(Calculation unit) 31, a determination unit (dust detection unit, unevenness detection unit) 32 for determining how much dust is present in the captured image based on the processed image, and a totalization unit for totaling those determined as dust (Aggregating means, sorting means) 33, CC
An image capturing control unit 34 that controls image capturing in the D camera 15,
A monitor (aggregation result output means, display means) 35 that appropriately displays the determination result of the determination unit 32, the image captured by the image processing unit 31, the processed image, and the like is provided.

When the dust detection system 1 having the above-mentioned configuration is used, the dust detection system 1 is installed at a place for detecting dust (hereinafter referred to as a detection place), and an image of the surface 11s of the dust collecting plate 11 is taken as a CCD.
An image is captured by the camera 15, and the captured image data is processed by the analysis computer 30 to measure dust on the surface 11s. Specifically, first, the dust detector 10
At the place where dust is detected (hereinafter, detection place)
The surface 11s of 1 is directed upward, and the surface 11s
Install so that it is horizontal. And the surface 11s
The dust on the top is imaged by the CDD camera 15 as an image, and the captured image data is processed by the image processing unit 31 to measure the dust on the top. Here, the dust that is naturally dropped on the surface 11s is measured. In addition,
The generated dust falls not only at the place where the dust is generated, but the place where the dust falls falls under the influence of, for example, the airflow in the clean room. The detection place can be set as appropriate, but it is installed near the product to be worked, such as near the worker or on the workbench. Further, the setting place of the analysis computer 30 is not particularly limited, and may be in the vicinity of the dust detector 10 or in a place apart from the dust detector 10. The plurality of dust detectors 10 are installed at different detection locations.

In capturing an image, first, the light from the light source passes through the dust collecting plate 11. At this time, light does not pass where dust exists on the surface 11s, so that the dust appears as a shadow in the obtained image of the surface 11s. A normal light such as a fluorescent lamp installed in a clean room as a detection place is used as the light source, but an imaging light source may be provided in the vicinity of the dust detector 10. However, in this case, it is preferable to install it in a position where the air flow in the clean room is not changed.

The light transmitted through the dust collecting plate 11 is diffused by the diffusion plate 12. Depending on the type and state of the light source, the brightness of the incident light on the dust collecting plate 11 may vary depending on the location, which causes "uneven brightness". Since the brightness unevenness hinders the detection of dust, the brightness unevenness is reduced by diffusing the incident light by the diffusion plate 12. At this time, the difference in luminance between the place where dust is present and the place where dust is not present has a higher contrast than the uneven brightness, so even if the brightness is changed by the diffuser, it will not significantly affect the detection of dust. Absent.

The light transmitted through the diffusion plate 12 is laterally deflected by the mirror 13. Further, the light deflected by the mirror 13 is condensed by the lens 14 to be CCD.
It is captured by the image sensor portion of the camera 15. At this time, since the size of the formed image can be adjusted by adjusting the focus of the lens 14, the image sensor portion of the CCD camera 15 does not have to have the same area as the surface 11s, which is the measurement area. . Therefore, in the present embodiment, the measurement area on the surface 11s can be made larger than that of the image sensor portion of the CCD camera 15.

Further, the dust detector 10 is provided with a mirror 13, and by deflecting the incident light by the mirror 13, the CCD camera 15 does not have to be provided directly below the dust collecting plate 11. Therefore, the height dimension of the dust detector 10 can be made relatively small, and the surface 11s, which is the measurement surface, can be positioned closer to the product to be worked.

The image data thus picked up by the CCD camera 15 is sent to the analysis computer 30 via the cable 18, and the image data is stored in a predetermined memory in the image processing section 31. In the detection of dust, the obtained image of the surface 11s is analyzed, the uneven brightness in the image is eliminated, and only the dust on the surface 11s is detected. This principle will be described below.

FIG. 2 is a diagram showing a flow of image processing executed when detecting dust. First, in the image processing unit 31, the image is processed in the procedure as shown in FIG.
Perform detection. Note that, in the following processing, detailed description of a well-known image processing method will be omitted. The detection method according to the present embodiment is performed by capturing an image to be inspected (step S101), dividing processing of captured image data (step S103), smoothing processing (step S105), application of a double surrounding difference method (step S107), Singular value data extraction processing (step S109) and image expansion / contraction processing (step S111) are performed in a series of steps.
The contents of each step will be described below in order of steps.

Step S101: Image Capture First, the CCD camera 15 captures an image of the surface 11s. The image data of the captured image is sent to the analysis computer 30 via the cable 18. FIG. 3 shows an image of the surface 11s taken. As shown in FIG. 3, small whitish dots are observed in places in this image. These are mainly dust particles on the surface 11s. Then, the transmitted image data is subjected to shading correction (shading processing) in the image processing unit 31. By this shading correction, the inconsistency of the conversion characteristics between the original brightness of the measurement image and the image data, which is mainly caused by the peripheral light reduction in the CCD camera 15 and the non-uniformity of the image sensor, is removed. At this time, by changing the luminance, the one with high luminance is corrected to red and the one with low luminance is corrected to green. An image obtained by the above processing is shown in FIG. In Figure 4,
Dust appears more clearly than that shown in FIG. On the other hand, uneven brightness appears more markedly in the entire image than that shown in FIG.

Step S103: Divide Captured Image Data The image data obtained in step S101 is divided into a matrix. This division of the image data is a preliminary process for executing the double surrounding difference method in step S107 described later. Here, it is assumed that the pixel is divided into n × m pixels. The divided pixels are two-dimensional coordinates
The position can be specified by (x, y). In this embodiment, since the CCD camera 15 is used as the image pickup means, n × m corresponds to the number of pixels (resolution) of the CCD camera 15. However, the maximum number of divisions is CCD
It is the number of pixels (resolution) of the camera 15, but it does not prevent the number of divisions being less than this.

Step S105: Smoothing process The image data divided in step S103 is subjected to a smoothing process for removing noise. For example, 2 × 2 filtering is performed.

Step S107: Application of Double Surrounding Difference Method For the image data from which noise has been removed in Step S105, the double surrounding difference method having the following contents is executed on the assumption that the brightness value of each pixel is obtained. . 2 in FIG.
6 is a flowchart showing a processing procedure of a heavy surrounding difference method.
In step S107, as shown in FIG.
The inner difference value indicated by (3) is calculated (step S20).
1). Next, the outer difference value represented by the following equation (4) is calculated (step S203). Then, the inner difference value obtained in step S201 and the outer difference value obtained in step S203 are added to calculate a double surrounding difference value represented by the following equation (5) (step S205). Less than,
Specific contents of steps S201 to S203 will be described. Note that step S201 of calculating the inner difference value
The order of step S203 for calculating the outer difference value may be changed. This double surrounding difference method is an algorithm newly developed by the present inventor. Also, equations (3) to (5)
Is an empirical formula as a result of various investigations by the present inventors. 2n + 1 (the third term) of the formula (3) and α (the second formula) of the formula (4)
Terms) and 2m + 1 (third term), and the coefficients a and b in the equation (5) are empirically derived.

[0033]

[Formula 7] (However, pi: luminance value of pixels forming a first pixel group surrounding a pixel existing at a position indicated by (x, y) in the image data, I (x, y): (x, y ), The luminance value of the pixel at the position indicated by n, n: the number of pixels from the image of interest to the first pixel group)

[0034]

[Formula 8] (However, po: luminance value of pixels forming a second pixel group surrounding a pixel existing at a position indicated by (x, y) in the image data, I (x, y): (x, y) ), The luminance value of the pixel at the position, m: the number of pixels from the target image to the second pixel group, α: a positive number of 0 or more)

[0035]

[Formula 9] (However, a, b: a positive number that satisfies a + b = 1.0)

Step S201: Calculation of Inner Difference Value The inner difference value is calculated based on the equation (3) as described above.
The contents of equation (3) will be described with reference to FIG. Note that FIG.
Indicates the image data divided in step S103. In Expression (3), the first term means the total sum of the luminance of the pixels existing at the position n apart from the pixel of interest 50. In FIG. 6, the black-painted pixel is the pixel of interest 50, and the first pixel group (pixel group) 51a composed of a plurality of pixels existing at positions n apart is painted in dark gray. Note that FIG. 6 shows an example in which n = 2. Next, the second term of Expression (3) represents the luminance value of the target pixel 50. Furthermore, the third term of the equation (3) is the target pixel 5
It is the number of pixels included in a pixel column existing at a position apart from 0 by n. Depending on the position of the target pixel 50, there may be a case where no pixel exists at a position separated by n. In that case, the calculation may be performed by using the luminance values of the pixels existing at a position separated by n by mirroring. The same applies to the calculation of the outer difference value in step S203.

Step S203: Calculation of Outer Difference Value The outer difference value is calculated by the calculation based on the equation (4) as described above. In the equation (4), the first term means the sum of the luminance of the pixels existing at the position m away from the target pixel 50. Since n <m, the calculation process for equation (3) is called the calculation of the inner difference value, and the calculation process for formula (4) is called the calculation of the outer difference value. Figure 6
2A, the second pixel group (pixel group) 52a composed of a plurality of pixels existing at a position separated by m from the target pixel 50 is painted in light gray. Note that in FIG. 6, m = 4
Shows an example of. Next, the second term of Expression (4) represents the luminance value of the pixel of interest 50. This second term is common to equation (3) except that there is a coefficient α. Furthermore, the third of the formula (4)
The term is the number of pixels included in a column formed by a pixel group existing at a position m away from the target pixel 50.

In the present embodiment, the first pixel group 51a for calculating the inner difference value and the second pixel group 52a for calculating the outer difference value have the form shown in FIG. Other forms as shown in FIGS. 7 to 9 can also be adopted. In the example shown in FIG. 7, the first pixel group (pixel group) 51b and the second pixel group (pixel group) 52b are composed of every other consecutive pixels selected. That is, in the example shown in FIG. 6, the first pixel group 51b and the second pixel group 52b are composed of a plurality of pixels arranged at intermittent positions. According to the present embodiment shown in FIG. 6, since the calculation for one target pixel 50 is limited to the integration of the brightness values of the pixels at positions n or m apart, the load is small in the first place. Further, as shown in FIG. 6, if only the pixels at the intermittent positions are targeted, the load required for the calculation is further reduced. In the example shown in FIG. 8, a first pixel group (pixel group) 51c and a second pixel group (pixel group) are formed from a plurality of pixels existing at positions that should be called octagonal shapes or circular shapes.
52c is configured. The example shown in FIG. 9 is a modified example of FIG. 8, and the first pixel is selected from all the pixels surrounding the pixel of interest 50.
Pixel group (pixel group) 51d of the first pixel group 51d
The second pixel group (pixel group) 52d from all the pixels surrounding the
Are configured. First pixel group 51 shown in FIGS.
The a to 51d and the second pixel groups 52a to 52d are common in the following points. That is, the first pixel groups 51a-5
The 1d and the second pixel groups 52a to 52d each have an isotropic form having no directivity with respect to the target pixel 50. The first pixel groups 51a to 51d and the second pixel groups 52a to 52d are line-symmetrical (or point-symmetrical) with respect to the target pixel 50. In order to properly extract uneven brightness and changes in brightness due to dust,
Pixel groups 51a-51d and second pixel groups 52a-5
It is important to make 2d into the above-mentioned form.

Step S205: Calculation of Double Surrounding Difference Value The double surrounding difference value can be calculated by executing the arithmetic processing based on the equation (5). That is, the double surrounding difference value can be calculated by adding the inner difference value and the outer difference value respectively calculated in step S201 and step S203. Here, a and b in the equation (5) are coefficients and take a positive number of 0 or more. Step S201,
A series of processes including steps S203 and S205 is executed for all pixels of the divided image data. The double surrounding difference value eventually indicates the relative luminance of each pixel.

Here, the inner difference value obtained in step S201 and the outer difference value obtained in step S203 are added to obtain the double peripheral difference value obtained in step S205. The image is shown in FIG. This image can be displayed on the monitor 35. In this image, the above n and m are n = 1,
This is the case when m = 16. Further, it is based on the result of calculation processing with the coefficient α = 1 in the equation (4) and the coefficients a = 0.15 and b = 0.85 in the equation (5). Note that, based on the inner difference value, local uneven brightness, difference in brightness due to dust, and the like are extracted. On the other hand, based on the outer difference value, the gradient of the brightness generated on the entire screen is extracted. Then, in FIG. 10 based on the double surrounding difference value obtained by adding the inner difference value and the outer difference value, it can be seen that both the bright spot and the brightness gradient can be extracted. The graph shown in FIG. 11 is a straight line (Z) drawn in FIG.
The luminance value of the cut surface at is shown. In FIG. 11, it can be seen that the luminance value is higher in the portion where dust is considered to be present than in the surroundings.

In the present embodiment, the double surrounding difference value obtained by adding the inner difference value and the outer difference value is obtained.
The image quality of the display image can be evaluated using only the inner difference value or the outer difference value. Further, in the present embodiment, coefficient α = 1 in equation (4) and coefficient a = 0.
15, b = 0.85, but other values can be adopted. The coefficients a and b determine the weighting of the inner and outer difference values. According to the study by the present inventors, the brightness unevenness and the foreign matter can be appropriately extracted by determining the weighting of the inner difference value and the outer difference value according to the brightness unevenness or the classification of dust to be extracted. . Therefore, it is necessary to experimentally determine the coefficients a and b according to the characteristics of the image to be extracted. As for the coefficient α, the second pixel group 52a for calculating the outer difference value
It can vary depending on (~ 52d). That is, when the value of m is large, the second term of the equation (3) takes an extremely small value as compared with the first term. For example, if m = 20, the first term is the sum of the brightness values of 160 pixels, and the brightness value of one pixel (target pixel 50) is extremely smaller than this sum. Therefore, in such a case, α can be set to 0 (zero) and the second term can be ignored.

Step S109: Extraction of Singular Value Data The double ambient difference value obtained in step S107, that is, the relative luminance value of each pixel is set to a predetermined threshold value (threshold value L1 shown in FIG. 11).
Are binarized using. Here, there are two types, one having a lower luminance than the threshold L1 and one having a luminance equal to or higher than the threshold L1. The singular value data extracted by binarization is subjected to the image expansion / contraction process of step S111. Step S111: Image expansion / contraction Image expansion / contraction is performed a predetermined number of times on the singular value data obtained in step S109. The image expansion is a process of converting all the values at the boundary of the graphic component into pixel values of the background component and expanding by one pixel. On the contrary, the image contraction is a process of contracting the graphic component by one pixel. By repeating the image expansion and contraction alternately, the noise part can be removed. The result of the image data thus obtained is output (step S113). The obtained image is shown in FIG.

As described above, in the present embodiment, in the image processing in the image processing unit 31, the distribution of the luminance value of the cut surface as shown in FIG. 11 is obtained, but at this time, the peak of the luminance value due to the luminance unevenness is obtained. Therefore, the peak value of the brightness value due to the presence of dust is significantly lower, and the gradient of the brightness value is also smaller (gradual). That is, based on these points, it is possible to identify whether the peak of the brightness value detected by the image processing unit 31 is due to uneven brightness or dust.
It is possible to remove from the image data those which are determined to be uneven brightness, and to display on the monitor 35 an image in which only dust is clearly shown as white dots as shown in FIG.

Next, a specific dust detection method in the clean room using the dust detection system 1 will be described. FIG. 13 is a flowchart of the dust detection method. at first,
In order to accurately measure dust at the detection location, an image of the surface 11s before the start of measurement is taken as an initial image with the dust detector 10 installed at the detection location (step S3).
01). The initial image contains dust adhering to a place other than the detection place, scratches on the surface 11s, and the like.
It is used when comparing with the measurement image after a predetermined time has passed in the detection of dust in the subsequent step S304. It should be noted that since only the dust that has been put within the measurement time at the actual detection location is measured before the initial image is captured, the surface 1 is measured before the measurement.
It is preferable to remove the dust already present on the 1s. As a specific method, dust on the surface 11s can be wiped with a cloth or the like, or the dust can be adhesively removed with an adhesive roller or the like.

After the initial image is captured in step S301, the image capturing is stopped and the dust detector 1
0 is left as it is for a predetermined period of time to freely collect dust that has fallen on the surface 11s (step S302). If it is determined that the predetermined time has elapsed, the process proceeds to the next step S303, and if it is determined that the predetermined time has not elapsed, the process waits until the predetermined time elapses. Here, the predetermined time for receiving the dust, that is, the measurement time can be set as appropriate, but is, for example, about one hour or several hours to several days. After a lapse of a predetermined time, an image of the surface 11s is taken as a measurement screen in the same manner as in step S301 (step S3).
03), the image data of the captured image is also stored in a predetermined memory in the analysis computer 30.

Next, by analyzing the image data,
Dust on the surface 11s is detected within a predetermined time (step S304). In step S304, first, the initial image and the measured image are compared, and data indicating a portion that has not changed within a predetermined time is excluded. As a result, it is possible to eliminate dust that has fallen from the measurement image other than during the predetermined time, scratches on the dust collecting plate 11, the diffusion plate 12, and the mirror 13. Note that this comparison may be performed after the processing by the double difference method shown in FIGS. 2 and 5 is completed. Also, when the surface 11s is obviously not dirty,
If you do not need to subtract the initial image from the measurement image,
It is not necessary to measure and compare the initial image.

Subsequently, in step S304, the image processing unit 31 performs the image processing shown in FIGS. 2 and 5 on the image data obtained by the above comparison processing. As a result of the processing, when there is a part determined to be dust or uneven brightness in the processed image, the image processing unit 31 calculates the brightness value distribution of the part and calculates the peak value of the brightness value. , It is determined based on the brightness distribution whether or not it exceeds a preset threshold L1. Here, if the peak value is low enough to exceed the predetermined threshold value L1, it is determined that this part is due to dust, and if the peak value is not low enough to exceed the threshold value L1, it is determined that the position is due to uneven brightness, Remove the unevenness. Then, the measurement result data is output, assuming that the dust determined has fallen within a predetermined time.

The dust measurement result data obtained as described above is displayed on the monitor 35 (step S305). Here, it is preferable to classify the respective dusts based on the result data, and to count the number of dusts for each predetermined classification. By categorizing, it becomes easy to identify the dust generation source in each category. The classification of dust is not particularly limited, for example, a method of classifying dust according to the shape and area of the taken dust. The counting result can be displayed on the screen by a histogram or the like showing the number of dusts for each classification.

As described above, the dust detection system 1 quantitatively evaluates the distribution of the brightness value of the surface 11s of the dust collecting plate 11 in the dust detector 10 to detect the place where dust or uneven brightness occurs. . Then, it is possible to distinguish between dust and uneven brightness based on the peak value or the slope of the brightness value. In addition, it is possible to quantitatively evaluate uneven brightness and local changes in brightness due to dust. Further, by using the double ambient difference method, it is possible to extract a brightness gradient and local brightness unevenness occurring therein, and by eliminating the extracted brightness unevenness, dust can be measured more accurately. . Further, according to the double surrounding difference method, the load of the arithmetic processing is small, and therefore the time of the arithmetic processing can be saved. Therefore, by using the dust detection system 1, the generated dust can be detected easily, accurately and efficiently. Further, since the plurality of dust detectors 10 are connected to the analysis computer 30, it is possible to simultaneously measure dust at a plurality of detection locations. Further, the measurement of dust can be centrally managed by the analysis computer 30, and the comprehensive dust generation in the clean room can be grasped. In addition, it is not necessary to install the analysis computer 30 at a plurality of places, so that it does not take up more space than the conventional dust detection device.

The structure of the dust detection system 1 and the dust detection procedure as described above are not necessarily limited to those shown in FIG. 13 and can be changed as appropriate. For example, the dust detector 10 may be configured such that the mirror 13 is not provided and the image sensor of the CCD camera 15 faces the dust collecting plate 11 and the diffusing plate 12. In addition, CCD camera 1
The images of the dust collecting plate 11 may be sequentially captured by scanning 5 under the diffusion plate 12. In addition, by providing multiple polarizing plates such as mirrors, CC
The installation location of the D camera 15 can be adjusted appropriately.

Further, in the present embodiment, when the dust detector 10 includes the diffusion plate 12, the image processing shown in FIGS. 2 and 5 need not be performed. The diffusing plate 12 scatters the incident light, and by shading processing, the uneven brightness is converted to such an extent that it does not hinder the detection of dust. On the other hand, the dust detector 10 may not include the diffusion plate 12. However, at this time, it may not be possible to accurately detect dust due to uneven brightness.
It is preferable to perform the image processing shown in.

FIG. 14 is a diagram for explaining the structure of the dust detection system according to the second embodiment. 14
The dust detection system 2 shown in is the same as the dust detection system 1 of the first embodiment in that it is composed of the dust detector 20 and the analysis computer 30 connected via the cable 18. The internal structure of the dust detector 20 is
It is different from that of the dust detector 10 of the first embodiment. Hereinafter, the dust detector 20 will be described in detail.

The dust detector 20 is provided with a protective plate (light transmitting portion, measuring portion, dust receiving means, flat portion) 21 and a CCD provided below the protective plate 21 for imaging the surface 21s of the protective plate 21.
The camera 22 and the inside of the outer frame 23 are provided.
The protective plate 21 transmits light and is provided under the protective plate 21.
It protects the CD camera 22, and, for example, the same structure as the dust collecting plate 11 of the first embodiment can be used.
The CCD camera 22 may have the same structure as the CCD camera 15 of the first embodiment. In the dust detector 20, the size of the image sensor in the CCD camera 22 is the area where dust can be measured as it is.

In the dust detection system 2, the first system shown in FIG.
The processing is performed in the same manner as the flow of the image processing executed when detecting dust in the embodiment. First, the dust on the surface 21s of the protective plate 21 is directly imaged by the CCD camera 22. FIG. 15 shows an image directly captured.
The data of the captured image is then subjected to shading processing, and becomes an image as shown in FIG. Furthermore, when the double surrounding difference value in step S107 is calculated and image processing is performed, an image as shown in FIG. 17 is obtained. The graph shown in FIG. 18 shows the luminance value of the cut surface along the straight line (Z) drawn in FIG. As the final processing,
After calculating the singular value data in step S109,
When the image expansion and contraction processing in step S111 is performed, the image shown in FIG.
The image is as shown in FIG. Such image processing is performed based on the measurement method shown in FIG. 13, and the dust detection system 2 of the second embodiment measures the dust that has fallen in a predetermined time.

The dust detector 20 of the second embodiment is different from the dust detector 10 of the first embodiment in that the diffusion plate 1
Since the components such as 2 and the mirror 13 are not required, the shape is simple and the size is small. Therefore, the dust detector 20 can be easily installed, and even if it is placed on a workbench, it does not take up a space and is easy to use.

Further, in the dust detector 20, the surface 21
When capturing the image s, the area to be imaged is the same as the image sensor portion of the CCD camera 22, and the area is relatively narrow. Further, as compared with the dust detector 10 of the first embodiment, the distance from the surface 21s on which dust is placed to the image pickup unit is shorter. As a result, uneven brightness is unlikely to occur in the image on the surface 21s. Therefore, it is possible to detect dust with relatively high accuracy without performing the processing for removing the brightness unevenness by the image processing as shown in FIGS. 2 and 5 on the captured image. Specifically, the surface 21s
The method of directly detecting dust from the image of, and the method of detecting dust after shading the image of the surface 21s can be adopted.

In the dust detection system 1 or 2 of the first and second embodiments, a plurality of dust detectors 10 or 20 are connected to the analysis computer 30, but one dust detector is used. 10 or 20 may be connected to one analysis computer 30 and configured. Furthermore, the dust detector 10 and the dust detector 20 are connected to the same analysis computer 30, and the situation at the detection location (measurement area at the measurement location, installable area of the dust detector,
It is also possible to select and use the dust detector 10 or the dust detector 20 according to the state of external light). Further, it is possible to automate the detection of dust in the dust detection systems 1 and 2. For example, it is possible to set a function such that an image is automatically taken at every predetermined time and to have a function of automatically wiping the surface of the dust detector, which is the measurement unit, before the measurement.

In addition, in the above-described first and second embodiments, the dust that naturally falls at the detection location is detected. However, by sucking the air around the dust detector 10 or 20, the dust is detected. It is also possible to deliberately drop to the detection location. Other than this, the configurations described in the above embodiments can be selected or changed to other configurations without departing from the gist of the present invention.

[0059]

As described above, according to the present invention,
It is possible to detect dust efficiently and reliably in a place such as a clean room where dust is managed.

[Brief description of drawings]

FIG. 1 is a diagram for explaining a configuration of a dust detection system according to a first embodiment.

FIG. 2 is a diagram showing a flow of image processing executed when detecting dust.

FIG. 3 shows an unprocessed image taken by the dust detection system.

FIG. 4 is a diagram showing an image subjected to shading processing.

FIG. 5 shows a flowchart showing a processing procedure of a double surrounding difference method.

FIG. 6 shows divided image data.

FIG. 7 is a diagram showing another example of a first pixel group and a second pixel group.

FIG. 8 is a diagram showing still another example of the first pixel group and the second pixel group.

FIG. 9 is a diagram showing still another example of the first pixel group and the second pixel group.

FIG. 10 is a diagram showing a binarized image.

11 shows the luminance value of the cut surface along the straight line (Z) in FIG.

FIG. 12 is a diagram showing an image subjected to image expansion and contraction processing.

FIG. 13 is a flow chart of a dust detection method.

FIG. 14 is a diagram for explaining the configuration of the dust detection system in the second embodiment.

FIG. 15 is a diagram showing an unprocessed image taken by the dust detection system.

FIG. 16 is a diagram showing an image subjected to shading processing.

FIG. 17 is a diagram showing a binarized image.

FIG. 18 shows the luminance value of the cut surface along the straight line (Z) in FIG.

FIG. 19 is a diagram showing an image subjected to image expansion and contraction processing.

[Explanation of symbols]

1, 2 ... Dust detection system (measuring instrument), 10, 20 ... Dust detector (dust detecting device), 11 ... Dust collecting plate (light transmitting part,
Measuring unit, dust receiving unit, flat unit, 12 ... Diffusing plate (light diffusing unit, light diffusing unit), 13 ... Mirror (deflecting unit), 14 ...
Lenses, 15, 22 ... CCD camera (imaging unit, imaging unit), 18 ... Cable, 21 ... Protective plate (light transmitting unit, measuring unit, dust receiving unit, flat unit), 30 ... Analysis computer, 31 ... Image processing unit (Contrast grasping means, image processing means, arithmetic means), 32 ... Judging section (dust detecting means, unevenness detecting means), 33 ... Aggregating section (aggregating means, sorting means), 3
4 ... Imaging control unit, 35 ... Monitor (collection result output means, display means)

   ─────────────────────────────────────────────────── ─── Continued front page    (72) Inventor Takahashi Tanahashi             1623 1423 Shimotsuruma, Yamato-shi, Kanagawa Japan             BM Co., Ltd. Daiwa Office F term (reference) 2G059 AA05 BB01 BB02 CC19 EE01                       FF01 GG10 JJ11 JJ13 JJ26                       KK04 MM01 MM02 MM05 MM10                       NN01 PP04

Claims (19)

[Claims] 1. A light transmitting portion having an incident surface for receiving the dust that has fallen down, and an emitting surface for emitting light incident from the surface, and an imaging portion for imaging the light emitted from the light transmitting portion. A dust detection device for a clean room, characterized by having. 2. The dust detection device for a clean room according to claim 1, wherein the image pickup unit and the light transmission unit are laminated. 3. A deflection unit for deflecting the light emitted from the light transmission unit, and a lens for forming an image of the light deflected by the deflection unit on the imaging unit. Dust detection device for the clean room described. 4. The dust detecting device for a clean room according to claim 3, wherein a light diffusing unit is arranged between the light transmitting unit and the image pickup unit. 5. An image pickup means for picking up an image of transmitted light of a measuring section on which dust is placed, a contrast grasping means for grasping a contrast of an image picked up by the image pickup means, and a contrast of the picked-up image. A detection unit that detects dust existing in the measuring unit based on the relationship; and a totalization result output unit that analyzes the dust identified by the detection unit and outputs the dust state data. Dust detection system. 6. An image processing unit that divides image data regarding the image captured by the image capturing unit, and an arithmetic process represented by the following formula (1) is performed on all the images divided by the image processing unit. , Calculation means for obtaining P (x, y) which is luminance information, and (However, pi, po, α and I (x, y) are defined as follows. Pi: Of the image data, the first line symmetry with respect to the position indicated by (x, y) as a reference. Luminance value po of pixels forming the pixel group: a second pixel of the image data, which is line-symmetrical with respect to the position indicated by (x, y) and is located outside the first pixel group The contrast grasping means further comprises luminance values I (x, y) of pixels forming a group: luminance values a, b, α: 0 of pixels at positions indicated by (x, y) and a positive number. The dust detection system according to claim 5, wherein the contrast of the image is detected based on the brightness information obtained by the calculation means. 7. A non-uniformity detection unit for detecting non-uniformity in brightness of the image based on the brightness information obtained by the calculation unit, wherein the detection unit considers the non-uniformity in brightness detected by the non-uniformity detection unit. The dust detection system according to claim 6, wherein dust is detected by means of a dust detection system. 8. The contrast grasping means, an image processing means for dividing the image data on the image, an arithmetic means for executing the arithmetic processing represented by the following equation (2) on the divided image data, ] (However, pi: luminance value of pixels forming a pixel group surrounding a pixel existing at a position indicated by (x, y) in the image data, I (x, y): indicated by (x, y) 6. The dust detection system according to claim 5, further comprising a luminance value of a pixel at a position where the dust detection system is provided. 9. The dust detection system according to claim 5, further comprising a light diffusing unit, wherein the image pickup unit images the light diffused by the light diffusing unit. 10. A dust detection system comprising a plurality of dust detection devices and an analysis computer for collecting data output from the dust detection devices, wherein the dust detection device places dust on a surface. Dust receiving means capable of performing, an image capturing means for capturing an image of the dust receiving means, and a transmitting means for transmitting image data of the image captured by the image capturing means to the analysis computer, the analysis computer, Identification means for identifying dust by analyzing the image data sent from the dust detection device, aggregation means for aggregating the dust identified by the identification means, and displaying data based on the aggregation result by the aggregation means And a display unit for controlling the dust detection system. 11. The dust detection device according to claim 10, further comprising a sorting unit that sorts the dusts identified by the identifying unit according to types, and the display unit displays a counting result for each type. system. 12. A measuring device for measuring fine particles, comprising: a flat measuring surface on which the fine particles can be placed; and a measuring surface imaging means that accommodates an imaging unit for imaging the measuring surface in a single housing, An image processing unit that divides image data relating to the image captured by the measurement surface image capturing unit, and an arithmetic process represented by the following formula (1) is executed for all the images divided by the image processing unit, and the brightness information is obtained. An arithmetic means for obtaining a certain P (x, y), and (However, pi, po, α and I (x, y) are defined as follows. Pi: Of the image data, the first line symmetry with respect to the position indicated by (x, y) as a reference. Luminance value po of pixels forming the pixel group: a second pixel of the image data, which is line-symmetrical with respect to the position indicated by (x, y) and is located outside the first pixel group Luminance values I (x, y) of pixels forming a group: luminance values a, b, α: 0 and positive numbers of pixels at positions indicated by (x, y)) A contrast detection means for detecting the contrast of the image captured by the measurement surface imaging means based on the above, and an identification means for identifying the fine particles present on the measurement surface based on the magnitude relationship of the contrast of the captured image; Result output that outputs the identification result by the identification means A means for measuring fine particles, comprising: 13. A measuring device for measuring fine particles, comprising: a flat measuring surface on which the fine particles can be placed; and an image pickup unit for picking up an image of the measuring face, which is housed in a single housing, Image processing means for dividing the image data relating to the image taken by the measurement surface imaging means, and operation means for executing the arithmetic processing represented by the following equation (2) on the image data divided by the image processing means: Formula 4] (However, pi: luminance value of pixels forming a pixel group surrounding a pixel existing at a position indicated by (x, y) in the image data, I (x, y): indicated by (x, y) (Brightness value of pixel at position) Contrast detection unit that detects the contrast of the image captured by the measurement surface imaging unit based on the luminance information obtained by the calculation unit, and the contrast relationship between the captured image and the contrast. Based on the above, there is provided an identification means for identifying particles present on the measurement surface, and a result output means for outputting an identification result of the identification means. 14. A method of capturing an image of a flat portion on which dust is placed, a dividing step of dividing image data of the captured image into a plurality of pixels, and attention based on a distribution of luminance values of the plurality of pixels. A target pixel setting step of setting a pixel, a difference calculation step of calculating a difference between a brightness value of the target pixel and a sum of brightness values of respective pixels forming a pixel group surrounding the target pixel, and based on the difference, A dust detection method, comprising: a determination step of determining whether or not the pixel of interest represents dust, and a totaling step of totalizing the pixels determined to be dust in the determination step. 15. A brightness difference detecting step of detecting a brightness difference between the pixel of interest and the pixel group when light is transmitted, wherein the determining step includes a step of determining when the difference is equal to or more than a predetermined threshold value. 15. The dust detection method according to claim 14, wherein it is determined that the pixel of interest represents dust. 16. A step of receiving falling dust at a flat portion for a predetermined time, a step of capturing a transmission image of the flat portion that has received the dust, a step of storing image data of the captured transmission image, A method for detecting dust in a clean room, comprising: analyzing image data to detect dust. 17. The method further comprises the step of capturing an initial transmission image of the flat portion before receiving the dust, and the step of detecting the dust includes the initial transmission image,
The dust detection method in a clean room according to claim 16, wherein the dust received within the predetermined time is detected by comparing with the transmission image. 18. The method according to claim 1, further comprising a step of performing a shading process on the image data.
6. The method for detecting dust in a clean room according to 6. 19. In the step of detecting the dust, the shadow formed by the dust on the flat portion and the brightness unevenness on the flat portion are identified, and the data of the shadow of the dust is extracted to extract the dust. 17. The method for detecting dust in a clean room according to claim 16, wherein