JP2020060419A - Foreign matter monitoring device and foreign matter monitoring method - Google Patents

Abstract

To provide a foreign matter monitoring device and foreign matter monitoring method capable of appropriately detecting the distribution of foreign matters in a manufacturing area.SOLUTION: A foreign matter monitoring device 1 includes: a foreign matter detection unit 2 for detecting the amount of foreign matters in a plurality of measurement places in a manufacturing area; a foreign matter distribution generation unit 3 for generating a foreign matter distribution image showing the distribution of the amount of foreign matters in the manufacturing area, on the basis of the detection result; an abnormal region detection unit 4 for detecting the abnormal region in which the amount of foreign matters exceeds a previously determined threshold on the basis of the foreign matter distribution image; and a notification unit 5 for reporting the abnormal region.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a foreign matter monitoring device and a foreign matter monitoring method.

  There are many steps in the manufacture of semiconductors and the like. If a foreign substance is mixed in in any of the steps, the quality of the product is deteriorated.

  In Patent Document 1, information on the quantity of foreign substances, which is environmental information of a clean room, is compiled into a database, and the relationship between the cleanliness in each manufacturing processing apparatus and the periphery of each manufacturing processing apparatus and the product quality is obtained, and the obtained relationship is obtained. Based on the above, a method for creating a standard for quality monitoring or quality control is described.

JP, 2012-47878, A

  However, in Patent Document 1, although the cleanliness level in the clean room is measured using a particle counter or the like, the distribution of foreign matter in the clean room cannot be properly detected.

  Therefore, an object of the present invention is to provide a foreign matter monitoring device and a foreign matter monitoring method that can appropriately detect the distribution of foreign matter in a manufacturing area.

  The foreign matter monitoring apparatus of the present invention creates a foreign matter distribution image showing the distribution of the number of foreign matter in the manufacturing area based on the result of the foreign matter detection unit that detects the number of foreign matter at a plurality of measurement points in the manufacturing area. A foreign matter distribution creating unit, an abnormal region detecting unit that detects an abnormal region in which the number of foreign substances exceeds a predetermined threshold based on the foreign substance distribution image, and a notification unit that notifies the abnormal region.

  According to the present invention, it is possible to appropriately detect the distribution of foreign matter in the manufacturing area.

FIG. 3 is a diagram showing a configuration of a foreign matter monitoring device according to the first embodiment. FIG. 3 is a diagram illustrating a configuration of a foreign matter detection unit 2 according to the first embodiment. It is a figure showing a manufacturing area. FIG. 6 is a diagram showing an example of foreign matter detection information according to the first embodiment. FIG. 5 is a diagram illustrating an example of a normal distribution according to the first embodiment. FIG. 6 is a diagram illustrating an example of a foreign substance distribution image according to the first embodiment. 5 is a flowchart showing an operation procedure of foreign matter monitoring device 1 of the first embodiment. FIG. 6 is a diagram illustrating a configuration of a foreign matter detection unit 2 according to the second embodiment. 9 is a flowchart showing a foreign matter detection procedure according to the second embodiment. It is a figure showing the example of a picked-up image. FIG. 9 is a diagram illustrating an example of foreign substance detection information according to the second embodiment. FIG. 9 is a diagram showing a configuration of a foreign matter detection unit 2 according to the third embodiment. 16 is a flowchart showing a learning procedure of the foreign matter type determination model of the third embodiment. (A)-(c) is a figure showing the example of a picked-up image. 9 is a flowchart showing a foreign matter detection procedure according to the third embodiment. FIG. 16 is a diagram illustrating an example of foreign substance detection information according to the third embodiment. FIG. 16 is a diagram illustrating an example of a normal distribution according to the fourth embodiment. FIG. 16 is a diagram illustrating an example of a foreign substance distribution image according to the fourth embodiment. It is a figure showing the structure of the foreign material monitoring apparatus 1 of Embodiment 5. It is a flowchart showing the procedure of airflow analysis. It is a figure for demonstrating an air flow vector. It is a figure showing the structure of the foreign material monitoring system 100 of Embodiment 6. 20 is a flowchart showing an operation procedure of foreign matter monitoring system 100 of the sixth embodiment. It is a figure showing the time change of the quantity of the foreign material in measurement location RA1 contained in an abnormal area. FIG. 28 is a diagram showing an event indicated by one or more extracted in-area event data in the sixth embodiment. It is a figure showing the structure of the foreign material monitoring system 200 of Embodiment 7. It is a figure showing the time change of the quantity of the foreign material in measurement location RA1 contained in an abnormal area. FIG. 28 is a diagram showing an event indicated by the extracted in-area event data in the seventh embodiment. It is a figure showing the structure of the foreign material monitoring system 300 of Embodiment 8. It is a figure showing the time change of the quantity of the foreign material in measurement location RA1 contained in an abnormal area. FIG. 27 is a diagram illustrating an event indicated by one or more extracted in-area event data according to the eighth embodiment. It is a figure showing the structure of the foreign material monitoring system 400 of Embodiment 9. It is a figure showing the time change of the quantity of the foreign material in measurement location RA1 contained in an abnormal area. FIG. 28 is a diagram showing an extracted frame image in the ninth embodiment. It is a figure showing the structure of the foreign material monitoring system 500 of Embodiment 10. 21 is a flowchart showing an operation procedure of foreign matter monitoring system 500 of the tenth embodiment. It is a figure showing the example of quality control information. It is a figure showing an example of the hardware constitutions of the foreign material monitoring apparatus 1. It is a figure showing another example of the hardware constitutions of the foreign material monitoring apparatus 1.

Hereinafter, embodiments will be described with reference to the drawings.
Embodiment 1.
FIG. 1 is a diagram showing the configuration of the foreign matter monitoring apparatus according to the first embodiment.

  The foreign matter monitoring device 1 includes a foreign matter detection unit 2, a foreign matter distribution creation unit 3, an abnormal area detection unit 4, and a notification unit 5.

The foreign substance detection unit 2 detects the number of foreign substances at a plurality of measurement points in the manufacturing area.
FIG. 2 is a diagram showing a configuration of the foreign matter detection unit 2 according to the first embodiment. FIG. 3 is a diagram showing a manufacturing area.

  As shown in FIG. 2, the foreign matter detection unit 2 includes a plurality of particle counters PC1 to PCN and a foreign matter detection information creation unit 52.

  The particle counter PCi (i = 1 to N) counts the number of foreign matters for each size of foreign matter. For example, the particle counter PCi has a number of foreign matter of 0.1 to 0.5 μm of 10,000, a number of foreign matter of 0.5 to 1 μm of 2,000, and a number of foreign matter of 1 to 10 μm of 15 for each size of foreign matter. Ask for the quantity.

  As shown in FIG. 3, the particle counter PCi is installed at the corresponding measurement point RAi. The measurement points RAi are on the work benches WR1 to WR6, on the floor, in the vicinity of any of the manufacturing apparatuses AP1 to AP5, and the like. It is desirable that the measurement location is a location where foreign matter that actually adheres to the product and makes the product defective is accumulated, or a location where the change in the number of minute foreign matter is large.

  The foreign matter that can be detected by the particle counter PCi is a minute foreign matter that is flowing in the air stream as managed in a clean room, a fiber accumulated in a work area of a non-clean room, a resin scrap, or a few μm of human origin. It is a foreign substance that exceeds.

  The particle counter PCi counts the number of foreign substances for each time zone and each size of foreign substances. The width of the time zone can be set to, for example, 1 minute or 10 minutes.

4A to 4C are diagrams showing an example of foreign substance detection information according to the first embodiment.
As shown in FIGS. 4A to 4C, the foreign matter detection information creation unit 52 creates foreign matter detection information representing the number of foreign matter in each measurement location at each time slot and size of each foreign matter.

  FIG. 4A shows foreign substance detection information at the measurement location RA1. FIG. 4B is foreign matter detection information at the measurement location RA2. FIG. 4C shows foreign substance detection information at the measurement location RA3.

  The foreign substance distribution creation unit 3 creates a foreign substance distribution image showing the distribution of the number of foreign substances in the manufacturing area based on the result of detection of the number of foreign substances.

  The foreign matter distribution creation unit 3 generates a foreign matter distribution image representing the distribution of foreign matter for each time zone and each size of foreign matter based on the foreign matter detection information. That is, one foreign matter distribution image is generated for one set of time zone and foreign matter size section. For example, the pixel value of the foreign matter distribution image in the time zone T1 and the foreign matter size category S1 represents the number of foreign matter in the time zone T1 and the foreign matter size category S1 at the location corresponding to the pixel.

  Alternatively, the foreign matter distribution creation unit 3 may generate a foreign matter distribution image representing the distribution of foreign matter for each size of foreign matter based on the foreign matter detection information. That is, one foreign matter distribution image is generated for each size of foreign matter. For example, the pixel value of the foreign matter distribution image in the foreign matter size section S1 represents the total number of foreign matter in the foreign matter size section S1 in all time zones at the location corresponding to the pixel.

  Alternatively, the foreign substance distribution creation unit 3 may generate a foreign substance distribution image representing the distribution of foreign substances for each time period based on the foreign substance detection information. That is, one foreign matter distribution image is generated for each time period. For example, the pixel value of the foreign matter distribution image in the time zone T1 represents the total number of foreign matter in all the foreign matter size categories in the time zone T1 at the location corresponding to the pixel.

  Alternatively, the foreign matter distribution creation unit 3 may generate a foreign matter distribution image representing the foreign matter distribution in all time zones and all time zones based on the foreign matter detection information. That is, one foreign substance distribution image is generated. The pixel value of the foreign substance distribution image represents the sum of the numbers of foreign substances in all the sizes of the foreign substances at the time corresponding to the pixel.

  In the first embodiment, the foreign matter distribution creation unit 3 creates a two-dimensional foreign matter quantity estimation distribution centered on each measurement point. The foreign matter distribution creation unit 3 creates a foreign matter distribution image of the entire manufacturing area by synthesizing all the created foreign matter quantity estimation distributions. One pixel of the foreign substance distribution image corresponds to one section when the entire manufacturing area is divided into two-dimensional sections of the same size. The pixel value of the foreign substance distribution image is a value of a combined distribution obtained by combining all the foreign substance quantity estimation distributions. As the two-dimensional foreign matter quantity estimation distribution, for example, a distribution obtained by multiplying the two-dimensional normal distribution by the foreign matter quantity at the measurement point as shown in the following formula can be used.

Here, one direction of the manufacturing area is an X direction, and a direction perpendicular to the X direction is a Y direction. μ _X and μ _Y represent the positions of measurement points. The standard deviations σ _X and σ _Y can be set to “1”, for example. P is the quantity of foreign matter at the measurement location.

FIG. 5 is a diagram showing an example of the foreign substance quantity estimation distribution according to the first embodiment.
When the number of foreign matters at the measurement location RA1 is P (RA1), the foreign matter quantity estimation distribution N1 centered on the measurement location RA1 is created. The value at the measurement point RA1 of the foreign substance quantity estimation distribution N1 is P (RA1). When the number of foreign matter at the measurement location RA2 is P (RA2), the foreign matter quantity estimation distribution N2 centered on the measurement location RA2 is created. The value at the measurement point RA2 of the foreign substance quantity estimation distribution N2 is P (RA2). The two-dimensional distance between the measurement point RA1 and the measurement point RA2 is D (1,2).

  The foreign matter distribution creation unit 3 creates a combined distribution CD by combining the created foreign matter quantity estimation distribution N1 and the created foreign matter quantity estimation distribution N2. The value of the composite distribution CD becomes the pixel value of the foreign substance distribution image.

FIG. 6 is a diagram showing an example of the foreign substance distribution image according to the first embodiment.
It is considered that the method of creating the foreign matter distribution of the first embodiment is effective for the foreign matter of large size having the property of low diffusibility. By the foreign matter distribution creation unit 3 clarifying the foreign matter distribution, it is possible to estimate at which position and at what timing the foreign matter that affects the quality defect of the product occurs.

  The abnormal area detection unit 4 detects an abnormal area in which the number of foreign particles exceeds a predetermined threshold value based on the created foreign particle distribution image. That is, the abnormal area detection unit 4 extracts one or more pixels having a pixel value exceeding the threshold value from the pixels of the foreign substance distribution image, and sets the extracted one or more pixels as the abnormal area. Different thresholds may be used depending on the position, time zone, or size of the foreign matter.

  The notification unit 5 notifies the abnormal area. For example, the notification unit 5 can display the abnormal area in the foreign substance distribution image on the display device in a color different from that of other areas. In addition, the notification unit 5 may issue a sound or an alarm when an abnormal area is detected. As a result, it is possible to make known the change in the quantity of foreign substances, and thus it is possible to prevent, for example, production of low-quality products.

FIG. 7 is a flowchart showing an operation procedure of foreign substance monitoring apparatus 1 of the first embodiment.
In step S201, the foreign matter detection unit 2 detects the number of foreign matter at a plurality of measurement points in the manufacturing area.

  In step S202, the foreign substance distribution creation unit 3 creates a foreign substance distribution image showing the distribution of the number of foreign substances in the manufacturing area based on the result of detection of the number of foreign substances.

  In step S203, the abnormal area detection unit 4 determines whether there is an abnormal area in which the number of foreign particles exceeds a predetermined threshold value, based on the created foreign object distribution image. If the abnormal area is detected, the process proceeds to step S204.

In step S204, the notification unit 5 notifies the abnormal area.
As described above, according to the present embodiment, it is possible to know the distribution of the quantity of foreign matter for each classification of foreign matter size in the manufacturing area. As a result, the size of the foreign matter that affects the product can be narrowed down and effective countermeasures can be taken.

Embodiment 2.
The particle size that can be managed by the particle counter PCi is usually 5 μm or less. In an assembly process and a substrate mounting process performed in a place other than a clean room, it is difficult to detect a larger foreign substance (hereinafter referred to as dust falling) that hardly accumulates in the air and accumulates.

  In the second embodiment, a photographing device such as a camera or a microscope is used to detect the dustfall.

FIG. 8 is a diagram showing a configuration of the foreign matter detection unit 2 according to the second embodiment.
As shown in FIG. 8, the foreign matter detection unit 2 includes a plurality of imaging devices IM1 to IMN, an image processing unit 53, and a foreign matter detection information creation unit 62.

  The image capturing device IMi (i to 1 to N) is installed at the measurement location RAi, as in the particle counter PCi of the first embodiment.

  FIG. 9 is a flowchart showing a foreign matter detection procedure according to the second embodiment. FIG. 10 is a diagram illustrating an example of a captured image. FIG. 11 is a diagram showing an example of foreign substance detection information according to the second embodiment.

  In step S101, each of the plurality of imaging devices IM1 to IMN images the vicinity of the installed measurement location RAi to generate a captured image as shown in FIG. The captured image is an RGB image.

  In step S102, the image processing unit 53 identifies a set CR of pixels whose red component value (hereinafter, R value) of the captured image exceeds a threshold value TR. The image processing unit 53 identifies a set CG of pixels in which the value of the green component (hereinafter, G value) of the captured image exceeds the threshold value TG. The image processing unit 53 identifies a set CB of pixels in which the value of the blue component (hereinafter, B value) of the captured image exceeds the threshold value TB.

  In step S103, the image processing unit 53 calculates the number of pixels included in the pixel set CR as the number of foreign matters having red as a main color component. The image processing unit 53 calculates the number of pixels included in the pixel set CG as the number of foreign substances whose main color component is green. The image processing unit 53 calculates the number of pixels included in the pixel set CB as the number of foreign matters having blue as a main color component.

  In step S104, the foreign matter detection information creation unit 62 creates foreign matter detection information representing the number of foreign matter in the main color components of each foreign matter at each measurement time point at each measurement point, as shown in FIG. FIG. 11A shows foreign matter detection information of the measurement location RA1. FIG. 11B shows foreign object detection information at the measurement location RA2. FIG. 11C shows foreign substance detection information at the measurement location RA3.

  As described above, according to the present embodiment, it is possible to know the distribution of the number of foreign substances for each main color component of the foreign substances in the manufacturing area.

Embodiment 3.
FIG. 12 is a diagram showing a configuration of the foreign matter detection unit 2 according to the third embodiment.

  As shown in FIG. 12, the foreign matter detection unit 2 includes a plurality of imaging devices IM1 to IMN, an image processing unit 54, a foreign matter type determination unit 91, and a foreign matter detection information creation unit 59.

  The imaging device IMi is installed at the measurement location RAi, as in the particle counter PCi of the first embodiment.

The image processing unit 54 is realized by a computer or the like.
FIG. 13 is a flowchart showing the learning procedure of the foreign matter type determination model of the third embodiment. 14A to 14C are diagrams showing examples of captured images.

In step S500, ia, ib, and ic are set to 1.
In step S501, a foreign substance αia of type α is generated in the vicinity of one of the plurality of imaging devices IM1 to IMN in the manufacturing area (hereinafter referred to as imaging device IMk).

  In step S502, the image capturing apparatus IMk captures the vicinity of the installed measurement location and generates a captured image including the foreign matter αia of the type α as shown in FIG. 14A.

  In step S503, if ia = NA is not satisfied, the process proceeds to step S504. If ia = NA, the process proceeds to step S505.

  In step S504, ia is incremented and the process returns to step S501.

  Through steps S501 to S504, NA captured images when a foreign substance of type α is generated are generated.

In step S505, a foreign substance βib of type β is generated in the vicinity of the imaging device IMk.
In step S506, the image capturing apparatus IMk captures the vicinity of the installed measurement location and generates a captured image including the foreign matter βib of type β as shown in FIG. 14B.

  If ib = NB is not true in step S507, the process proceeds to step S508. If ib = NB, the process proceeds to step S509.

  In step S508, ib is incremented, and the process returns to step S505.

  Through steps S505 to S508, NB captured images when a foreign substance of type β is generated are generated.

In step S509, a foreign substance γic of type γ is generated in the vicinity of the imaging device IMk.
In step S510, the image capture device IMk captures an image of the vicinity of the installed measurement location, and generates a captured image including the foreign substance γic of type γ as illustrated in FIG. 14C.

  In step S511, if ic = NC is not satisfied, the process proceeds to step S512. If ic = NC, the process proceeds to step S513.

  In step S512, ic is incremented and the process returns to step S509.

  Through steps S509 to S512, NC captured images when a foreign substance of type γ is generated are generated.

  In step S <b> 513, the foreign matter type determination unit 91 determines NA captured images when a foreign matter of type α occurs, NB captured images when a foreign matter of type β occurs, and foreign matter of type γ occurs. A foreign substance type determination model by a neural network in which an input is a captured image and an output is a foreign substance type is learned using NC captured images.

  FIG. 15 is a flowchart showing a foreign matter detection procedure according to the third embodiment. FIG. 16 is a diagram showing an example of foreign substance detection information according to the third embodiment.

  In step S601, each of the plurality of imaging devices IM1 to IMN captures an image of the vicinity of the installed measurement location and generates a captured image.

  In step S602, the foreign matter type determination unit 91 inputs each captured image into the learned neural network of the foreign matter type determination model, and determines the foreign matter type from the output of the neural network.

In step S603, the image processing unit 54 binarizes each captured image.
In step S604, the image processing unit 54 obtains the number of foreign substances of the determined type by counting the number of “1” pixels of each binarized captured image.

  In step S605, the foreign matter detection information creation unit 59 creates foreign matter detection information indicating the number of foreign matter for each time zone and each kind of foreign matter at each measurement location as shown in FIG. FIG. 16A shows foreign substance detection information at the measurement location RA1. FIG. 16B shows foreign substance detection information at the measurement location RA2. FIG. 16C shows foreign substance detection information at the measurement location RA3.

  As described above, according to the present embodiment, it is possible to know the distribution of the quantity of foreign matter for each type of foreign matter in the manufacturing area.

Fourth Embodiment
In the fourth embodiment, the foreign matter distribution creation unit 3 creates a two-dimensional foreign matter quantity estimation distribution centered on the measurement position where the number of foreign matter is maximum. The foreign matter distribution creation unit 3 determines the parameters of the foreign matter quantity estimation distribution so that the sum of squares of the differences between the values of the foreign matter quantity estimation distribution and the actual measurement values is minimized at all measurement points.

  The two-dimensional foreign matter quantity estimation distribution may be, for example, a distribution obtained by multiplying the two-dimensional normal distribution by the foreign matter quantity at the first measurement point, as shown in the above-mentioned formula (1). The first measurement location is a measurement location where the number of foreign matters is maximum among the plurality of measurement locations.

FIG. 17 is a diagram showing an example of the foreign substance quantity estimation distribution according to the fourth embodiment.
In the manufacturing area, the number of foreign matters at the measurement location RA1 is the maximum value P (RA1).

  A foreign substance quantity estimation distribution N1 centered on the measurement point RA1 is created. It is assumed that the number of foreign matter at the measurement location RA2 is P (RA2), the number of foreign matter at the measurement location RA3 is P (RA3), and the number of foreign matter at the measurement location RA4 is P (RA4). The value at the measurement point RA2 of the foreign matter quantity estimated distribution N1 is G (RA2), the value at the measurement point RA3 of the foreign matter quantity estimated distribution N1 is G (RA3), and the value at the measurement point RA4 of the foreign matter quantity estimated distribution N1 is G (RA4). And The parameters of the foreign substance quantity estimation distribution N1 are determined so that S in the following equation (2) is minimized. When the foreign substance quantity estimation distribution N1 is expressed by the equation (1), the standard deviations σx and σy that minimize S can be obtained.

S = {P (RB2) -G (RB2)} ² + {P (RB3) -G (RB3)} ² + {P (RB4) -G (RB4)} ² ... (2)
The foreign matter distribution creation unit 3 creates a foreign matter distribution image of the entire manufacturing area based on the foreign matter quantity estimation distribution N1 whose parameters have been determined. The value of the foreign substance quantity estimation distribution N1 becomes the pixel value of the foreign substance distribution image.

FIG. 18 is a diagram showing an example of the foreign substance distribution image according to the fourth embodiment.
It is considered that the method of creating a foreign matter distribution according to the fourth embodiment is effective for a foreign matter having a high diffusibility and a small size.

  Note that by setting a barrier such as a wall surface, the value of the probability density distribution representing the number of foreign substances across the barrier may be regarded as 0.

Embodiment 5.
FIG. 19 is a diagram showing the configuration of foreign matter monitoring apparatus 1 of the fifth embodiment.

  The foreign matter monitoring device 1 includes a foreign matter detecting unit 2, a foreign matter distribution creating unit 3, an abnormal area detecting unit 4, and a notifying unit 5, as in the first embodiment.

The foreign matter monitoring device 1 further includes an airflow analysis unit 21.
The airflow analysis unit 21 determines the airflow vector of the foreign matter based on the distribution position of the standard foreign matter and the distribution of the number of standard foreign matter in the foreign matter distribution image.

  FIG. 20 is a flowchart showing the procedure of airflow analysis. FIG. 21 is a diagram for explaining the airflow vector.

  In step S300, standard foreign matter is sprayed. For example, blue chalk powder can be used as the standard foreign material.

  In step S301, the foreign substance detecting unit 2 detects the number of foreign substances at a plurality of measurement points in the manufacturing area, as in the first embodiment.

  In step S302, similar to the first embodiment, the foreign substance distribution creation unit 3 creates a foreign substance distribution image representing the distribution of the number of foreign substances in the manufacturing area based on the result of detecting the number of foreign substances.

  In step S303, the airflow analysis unit 21 generates a first direction vector VD1 from the pixel corresponding to the position OR where the standard foreign matter is scattered to each pixel in the foreign matter distribution image. The first direction vector VD1 is a unit vector.

  In step S304, the airflow analysis unit 21 generates the second direction vector VD2 in each pixel of the foreign substance distribution image. The second direction vector VD2 is a unit vector. That is, when the pixel having the largest pixel value difference from the pixel value of the pixel P among the eight pixels adjacent to the pixel P is the pixel Q, the airflow analysis unit 21 determines the direction of the second direction vector VD2. Is the direction from the pixel with the larger pixel value of the pixels P and Q to the pixel with the smaller pixel value. For example, in the pixel (2, 2), among the eight adjacent pixels, the pixel having the largest pixel value with the pixel value of the pixel (2, 2) is (1, 1), and the pixel (1 , 1) is larger than the pixel value of the pixel (2, 2), the second direction vector VD2 in the direction from the pixel (1, 1) to the pixel (2, 2) is Is generated.

  In step S305, the airflow analysis unit 21 generates the airflow vector VA by subtracting the second direction vector VD2 from the first direction vector VD1 in each pixel of the foreign substance distribution image.

  The notification unit 5 can display the abnormal region detected by the abnormal region detection unit 4 on the display device and can also display the airflow vector VA obtained by the airflow analysis unit 21.

  According to the present embodiment, it is possible to analyze whether the abnormal region is in the direction of the airflow vector, that is, whether it is leeward.

Sixth Embodiment
FIG. 22 is a diagram showing the configuration of foreign matter monitoring system 100 of the sixth embodiment.

The foreign matter monitoring system 100 includes a foreign matter monitoring apparatus 1 and a data collecting apparatus 70.
The data collection device 70 collects and stores in-area event data that may be related to an increase in the quantity of foreign matter. In the sixth embodiment, the information indicating the change in the position of the moving body is the in-area event data.

  The data collection device 70 includes a plurality of beacons BA1 to BAN and an in-area event data storage unit 72.

  Each beacon BAi (i = 1 to N) transmits a signal including identification information for identifying itself and position information indicating its own position. The beacon BAi (i = 1 to N) is mounted on the mobile body.

  The in-area event data storage unit 72 stores a plurality of in-area event data. The in-area event data includes identification information and position information included in the signal transmitted from the beacon BAi, and the time of the position information. The time of the position information is the time when the data collection device 70 receives the signal from the beacon BAi including the position information.

  The foreign matter monitoring device 1 includes a foreign matter detecting unit 2, a foreign matter distribution creating unit 3, an abnormal area detecting unit 4, and a notifying unit 5, as in the first embodiment.

The foreign matter monitoring apparatus 1 further includes an abnormality factor analysis unit 71.
The abnormality factor analysis unit 71 selects one or more areas related to the time when the abnormal region is detected and the position of the abnormal region from the plurality of in-area event data stored in the in-area event data storage unit 72. Extract internal event data.

  FIG. 23 is a flowchart showing the operation procedure of foreign matter monitoring system 100 of the sixth embodiment.

  In step S701, the foreign substance detection unit 2 detects the number of foreign substances at a plurality of measurement points in the manufacturing area, as in the first embodiment.

  In step S702, the foreign substance distribution creation unit 3 creates a foreign substance distribution image representing the distribution of the number of foreign substances in the manufacturing area based on the result of detecting the number of foreign substances, as in the first embodiment.

  In step S703, the abnormal area detection unit 4 determines whether or not there is an abnormal area in which the number of foreign particles exceeds a predetermined threshold value based on the created foreign particle distribution image, as in the first embodiment. To do. If an abnormal area is detected, the process proceeds to step S705.

  In step S704, the beacons BAi (i = 1 to N) in the data collection device 70 each transmit a signal including identification information for identifying itself and position information indicating the position of itself. The in-area event data storage unit 72 stores in-area event data including identification information and position information included in the signal transmitted from the beacon BAi, and the time of the position information. The time of the position information is the time when the signal from the beacon BAi is received.

  In step S <b> 705, the abnormality factor analysis unit 71 selects the time at which the abnormal region is detected and the abnormal region from the plurality of in-area event data stored in the in-area event data storage unit 72 in the data collection device 70. Extract in-area event data related to position. For example, the abnormality factor analysis unit 71 extracts the in-area event data whose difference from the time when the abnormal region is detected is within ΔT and whose difference from the position of the abnormal region is within ΔR from the plurality of in-area event data. To do. Furthermore, the abnormality factor analysis unit 71 may analyze the abnormality factor based on the extracted in-area event data.

  In step S706, the notification unit 5 notifies the extracted in-area event data together with the abnormal area. For example, the notification unit 5 may display an image based on the in-area event data.

  FIG. 24 is a diagram showing the change over time in the quantity of foreign matter at the measurement point RA1 included in the abnormal area.

  At the measurement point RA1, at time t1, the number of foreign substances sharply increases, and as a result, it is determined that it is an abnormal region.

  FIG. 25 is a diagram showing an event indicated by one or more extracted in-area event data in the sixth embodiment.

  In the example of FIG. 25, the in-area event data indicates that at time t1, the difference between the position of the moving body MA1 having the beacon BA1 and the position of the measurement point RA1 included in the abnormal area is within ΔR. ing.

  The abnormality factor analysis unit 71 determines that the moving body having the beacon BA1 approaches the moving body having the beacon BA1 at the measurement point RA1 included in the abnormal region at the time t1 when the number of foreign substances increases at the time t1. It may be assumed that it is related to the increase in the number of foreign substances.

  For example, as shown in FIG. 25, the notification unit 5 indicates that the moving body having the beacon BA1 approaches the measurement location RA1 at time t0 to time t1 and moves away from the measurement location RA1 at time t1 to time t2. The displayed image and the graph showing the change over time in the number of foreign substances as shown in FIG. 24 may be displayed.

  According to the present embodiment, when the quantity of foreign matter fluctuates, the factors contributing to the fluctuation of the quantity of foreign matter can be presented. As a result, for example, a worker who urges a dust source such as a person or a trolley to leave the site, urges interruption of a work process in which foreign matter is mixed, or causes foreign matter to occur. It is possible to prevent the occurrence of product defects by instructing the user to perform correct work.

Embodiment 7.
FIG. 26 is a diagram showing the configuration of foreign matter monitoring system 200 of the seventh embodiment.

The foreign matter monitoring system 200 includes the foreign matter monitoring apparatus 1 and the data collecting apparatus 70.
The data collection device 70 collects and stores in-area event data that may be related to fluctuations in the quantity of foreign matter. In the seventh embodiment, the information indicating the opening and closing of the door is the in-area event data.

  The data collection device 70 includes an opening / closing sensor DR and an in-area event data storage unit 72.

  The opening / closing sensor DR detects opening / closing of the door TS. The open / close sensor DR outputs a first detection signal when detecting that the door TS is closed. The open / close sensor DE outputs a second detection signal when detecting that the door TS is opened.

  The in-area event data storage unit 72 stores a plurality of in-area event data. The event data in each area is the time when the door TS is closed or the time when the door TS is opened. The time when the door TS is closed can be the time when the data collection device 70 receives the first detection signal. The time when the door TS is opened can be the time when the data collection device 70 receives the second detection signal.

  The foreign matter monitoring device 1 includes a foreign matter detecting unit 2, a foreign matter distribution creating unit 3, an abnormal area detecting unit 4, and a notifying unit 5, as in the first embodiment.

The foreign matter monitoring apparatus 1 further includes an abnormality factor analysis unit 71.
The abnormality factor analysis unit 71 extracts the in-area event data related to the time when the abnormal region is detected from the plurality of in-area event data stored in the in-area event data storage unit 72.

  FIG. 27 is a diagram showing a temporal change in the quantity of foreign matter at the measurement point RA1 included in the abnormal area.

  At the measurement point RA1, at time t1, the number of foreign substances sharply increases, and as a result, it is determined that it is an abnormal region.

  FIG. 28 is a diagram showing an event indicated by the extracted in-area event data in the seventh embodiment.

  In the example of FIG. 28, the in-area event data indicates that the door TS has changed from the closed state to the open state at time t1. That is, it is indicated that the operation of opening the door TS is started at time t1.

  At the measurement point RA1 including the abnormal area, the abnormality factor analysis unit 71 is the time when the operation of opening the door TS is started at the time t1 when the number of foreign matters is increased. It may be inferred that it is related to the increase in quantity.

Eighth embodiment.
FIG. 29 is a diagram showing the configuration of foreign matter monitoring system 300 of the eighth embodiment.

The foreign matter monitoring system 300 includes the foreign matter monitoring apparatus 1 and the data collecting apparatus 70.
The data collection device 70 collects and stores in-area event data that may be related to fluctuations in the quantity of foreign matter. In the eighth embodiment, the information indicating the change in the position of the moving body is the in-area event data.

  The data collection device 70 includes a plurality of distance sensors DS1 to DSN and an in-area event data storage unit 72.

  The distance sensor DSi (i = 1 to N) is installed at the measurement location RAi where the imaging device or the particle counter is installed.

  The distance sensor DSi detects the distance to the moving body and outputs a signal representing the distance to the moving body.

  The in-area event data storage unit 72 stores a plurality of in-area event data. The in-area event data includes identification information for identifying the distance sensor DSi, distance information from the distance sensor DSi to the moving body, and time of the distance information. The time of the distance information can be the time when the data collection device 70 receives the signal from the distance sensor DSi.

  The foreign matter monitoring device 1 includes a foreign matter detecting unit 2, a foreign matter distribution creating unit 3, an abnormal area detecting unit 4, and a notifying unit 5, as in the first embodiment.

The foreign matter monitoring apparatus 1 further includes an abnormality factor analysis unit 71.
The abnormality factor analysis unit 71 includes one or more times associated with the time when the abnormal region is detected and the position of the abnormal region from the plurality of in-area event data stored in the in-area event data storage unit 72. Extract the event data in the area.

  FIG. 30 is a diagram showing a temporal change in the quantity of foreign matter at the measurement location RA1 including the abnormal area.

  At the measurement point RA1, at time t1, the number of foreign substances sharply increases, and as a result, it is determined that it is an abnormal region.

  FIG. 31 is a diagram showing an event indicated by one or more extracted in-area event data in the eighth embodiment.

  In the example of FIG. 31, the distance sensor DS1 is installed at the measurement location RA1, and the in-area event data shows that at time t1, the difference between the location of the moving body MA1 and the location of the measurement location RA1 including the abnormal area is ΔR. It shows that it is within the range.

  At the measurement point RA1 including the abnormal area, the abnormality factor analysis unit 71 is the time when the moving body MA1 approaches at the time t1 when the number of foreign matter increases. Therefore, the moving body MA1 approaches and the number of foreign matter increases. May be inferred to be related to.

Ninth Embodiment
FIG. 32 is a diagram showing the configuration of foreign matter monitoring system 400 according to the ninth embodiment.

The foreign matter monitoring system 400 includes the foreign matter monitoring apparatus 1 and the data collecting apparatus 70.
The data collection device 70 collects and stores frame images that may be related to fluctuations in the quantity of foreign matter. In the eighth embodiment, the image representing the movement of the worker is the in-area event data.

  The data collection device 70 includes a plurality of cameras CM1 to CMN and a frame image storage unit 92.

  The cameras CMi (i to 1 to M) take pictures of the surrounding area and generate a video composed of a plurality of frame images.

  The frame image storage unit 92 stores a plurality of frame images included in the images generated by the cameras CM1 to CMM and the times of the frame images.

  The foreign matter monitoring device 1 includes a foreign matter detecting unit 2, a foreign matter distribution creating unit 3, an abnormal area detecting unit 4, and a notifying unit 5, as in the first embodiment.

The foreign matter monitoring apparatus 1 further includes an abnormality factor analysis unit 71.
The abnormality factor analysis unit 71 extracts one or more frame images associated with the time when the abnormal region is detected and the position of the abnormal region from the plurality of frame images stored in the frame image storage unit 92. To do.

  FIG. 33 is a diagram showing the change over time in the quantity of foreign matter at the measurement location RA1 including the abnormal area.

  At the measurement point RA1, at time t1, the number of foreign substances sharply increases, and as a result, it is determined that it is an abnormal region.

FIG. 34 is a diagram showing the extracted frame image in the ninth embodiment.
The abnormality factor analysis unit 71 extracts the frame images before and after the time t1 included in the image of the camera that captures the abnormal area among the cameras CM1 to CMN.

  In the example of FIG. 34, in the extracted frame image, before and after time t1, the worker PA1 performed an abnormal operation different from the normal operation at a location within ΔR from the measurement location RA1 including the abnormal area. It is shown.

  According to the present embodiment, the administrator can analyze whether or not the operation of the worker is abnormal when the number of foreign substances increases.

Embodiment 10.
FIG. 35 is a diagram showing the configuration of foreign matter monitoring system 500 of the tenth embodiment.

The foreign matter monitoring system 500 includes the foreign matter monitoring apparatus 1 and the quality control apparatus 80.
The quality control device 80 collects and stores quality control information indicating a production process of a product manufactured in the manufacturing area and quality of the product.

The quality management device 80 includes an information input unit 83 and a quality management information storage unit 82.
The information input unit 83 inputs quality control information indicating the manufacturing process of the product and the quality of the product by the operation of the administrator.

  The quality control information storage unit 82 stores the plurality of quality control information input from the information input unit 83.

  The foreign matter monitoring device 1 includes a foreign matter detecting unit 2, a foreign matter distribution creating unit 3, an abnormal area detecting unit 4, and a notifying unit 5, as in the first embodiment.

The foreign matter monitoring device 1 further includes an abnormality factor analysis unit 81.
The abnormality factor analysis unit 81 extracts quality control information related to the time when the abnormal region is detected and the position of the abnormal region from the plurality of quality control information stored in the quality control information storage unit 82. .

  FIG. 36 is a flowchart showing the operation procedure of foreign matter monitoring system 500 of the tenth embodiment.

  In step S801, the foreign substance detection unit 2 detects the number of foreign substances at a plurality of measurement points in the manufacturing area, as in the first embodiment.

  In step S802, the foreign substance distribution creation unit 3 creates a foreign substance distribution image representing the distribution of the number of foreign substances in the manufacturing area based on the result of detection of the number of foreign substances, as in the first embodiment.

  In step S803, the abnormal area detection unit 4 detects an abnormal area in which the number of foreign particles exceeds a predetermined threshold value based on the created foreign object distribution image, as in the first embodiment.

  In step S804, the information input unit 83 inputs quality control information indicating the production process of the product and the quality of the product. The quality control information storage unit 82 stores the quality control information input from the information input unit 83.

  In step S805, the abnormality factor analysis unit 81 relates to the time when the abnormal region is detected and the position of the abnormal region from the plurality of quality control information stored in the quality control information storage unit 82 of the quality control device 80. Quality control information to be extracted. Further, the abnormality factor analysis unit 71 may analyze the abnormality factor based on the extracted quality control information.

  In step S806, the notification unit 5 notifies the extracted quality control information together with the abnormal area. The notification unit 5 may display the cause of abnormality estimated based on the extracted quality control information.

FIG. 37 is a diagram showing an example of quality control information.
As shown in FIG. 37, the quality control information includes a manufacturing number, work start time, work end time, equipment used, jigs used, workers, lots of materials, lots of parts, and quality of products.

  For example, it is assumed that the abnormal area A is detected in the time zone TA from 10:50 to 11:00 on October 11, 2018. It is assumed that the abnormal area A is located near the facility a3 used. The abnormality factor analysis unit 81 extracts the quality control information regarding the product of the manufacturing number 3 including the time zone TA and the facility a3 used from the quality control information of FIG.

  Further, the abnormality factor analysis unit 81 confirms that the manufactured product is defective in this quality control information, and determines that the equipment a3, the jig / tool b3 used, the worker p3, the material lot p3, and the component lot M3 are used. At least one may be analyzed as an abnormal factor. The notification unit 5 may display on the display device that at least one of the equipment a3 used, the tool b3 used, the worker p3, the material lot p3, and the component lot M3 is the cause of the abnormality.

  According to the present embodiment, when the number of foreign matters increases, the manager can know the quality control information related to the increase in the number of foreign matters.

Eleventh Embodiment
FIG. 38 is a diagram showing an example of the hardware configuration of the foreign matter monitoring apparatus 1.

  The foreign matter detection information creation unit 52, 62 or 72, the foreign matter distribution creation unit 3, the abnormal area detection unit 4, the image processing unit 53 or 54, the foreign matter type determination unit 91, the air flow analysis unit 21, the abnormal factor analysis unit 71 or 81, It is configured by the processing circuit 120. The processing circuit is a CPU (Central Processing Unit, central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, processor, DSP) that executes a program stored in the memory even if it is dedicated hardware. May be

  When the processing circuit 120 is dedicated hardware, the processing circuit 120 may be, for example, a single circuit, a composite circuit, a programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. It can be

  Foreign matter detection information creation unit 52, 62 or 72, foreign matter distribution creation unit 3, abnormal area detection unit 4, image processing unit 53 or 54, foreign matter type determination unit 91, airflow analysis unit 21, abnormal factor analysis unit 71 or 81. The functions can be realized by the corresponding processing circuits 120. Alternatively, the foreign matter detection information creation unit 52, 62 or 72, the foreign matter distribution creation unit 3, the abnormal area detection unit 4, the image processing unit 53 or 54, the foreign matter type determination unit 91, the airflow analysis unit 21, the abnormal factor analysis unit 71 or 81. It is possible to collectively realize the respective functions of 1 by the single processing circuit 120.

The notification unit 5 can be realized by the display 150.
FIG. 39 is a diagram showing a specific example of the processing circuit 120 of FIG.

  The processing circuit 120 includes a processor 130 and a memory 140. Functions of foreign matter detection information creation unit 52, 62 or 72, foreign matter distribution creation unit 3, abnormal area detection unit 4, image processing unit 53 or 54, foreign matter type determination unit 91, airflow analysis unit 21, abnormal factor analysis unit 71 or 81 Are implemented by the processor 130, software, firmware, or a combination of software and firmware. The software and firmware are described as programs and stored in the memory 140. The processor 130 realizes the function of each unit by executing the program stored in the memory 140. This program is executed by the foreign matter detection information creation unit 52, 62 or 72, the foreign matter distribution creation unit 3, the abnormal area detection unit 4, the image processing unit 53 or 54, the foreign matter type determination unit 91, the airflow analysis unit 21, the abnormal factor analysis unit 71. It can also be said that it causes a computer to execute the procedure and method of 81. Here, the memory corresponds to, for example, a nonvolatile or volatile semiconductor memory such as a RAM, a ROM, a flash memory, an EPROM, and an EEPROM, a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, and a DVD. .

  The foreign matter detection information creation unit 52, 62 or 72, the foreign matter distribution creation unit 3, the abnormal area detection unit 4, the image processing unit 53 or 54, the foreign matter type determination unit 91, the airflow analysis unit 21, the abnormal factor analysis unit 71 or 81. Each of the functions may be partially implemented by dedicated hardware, and the other part may be implemented by software or firmware.

  In this way, the processing circuit 120 can realize each function described above by hardware, software, firmware, or a combination thereof.

The notification unit 5 can be realized by the display 150.
Further, the in-area event data storage unit 72 and the frame image storage unit 92 in the data collection device 70, and the quality management information storage unit 82 in the quality management device 80 can be realized by a memory. The information input unit 83 in the quality control device 80 can be realized by an input device such as a keyboard, a touch panel, or a mouse.

  The present invention is not limited to the above embodiment, and includes, for example, the following modifications.

(1) Foreign Material Quantity Estimated Distribution In the above embodiment, the foreign material quantity estimated distribution is based on the normal distribution, but the present invention is not limited to this, and other probability distributions may be based.

(2) Two-dimensional foreign matter quantity estimation distribution In the above embodiment, the two-dimensional foreign matter quantity estimation cloth is used, but the invention is not limited to this. The foreign matter quantity estimated distribution about the X axis and the foreign matter quantity estimated distribution about the Y axis may be used.

(3) Composition of Foreign Material Quantity Estimated Distribution In the first embodiment, a plurality of foreign material quantity estimated distributions are combined to obtain a composite distribution. However, a plurality of foreign material quantity estimated distributions are not simply combined, but weighted. It may be synthesized. When the distances between the plurality of measurement points are small, the weight may be reduced.

  The embodiments disclosed this time are to be considered as illustrative in all points and not restrictive. The scope of the present invention is shown not by the above description but by the claims, and is intended to include meanings equivalent to the claims and all modifications within the scope.

  1 foreign matter monitoring device, 2 foreign matter detecting unit, 3 foreign matter distribution creating unit, 4 abnormal area detecting unit, 5 notifying unit, 52, 59, 62 foreign matter detection information creating unit, 53, 54 image processing unit, 70 data collecting device, 71 , 81 Abnormality factor analysis unit, 72 In-area event data storage unit, 80 Quality management device, 82 Quality management information storage unit, 83 Information input unit, 91 Foreign matter type determination unit, 92 Frame image storage unit, 100,500 Foreign matter monitoring system , 120 processing circuit, 130 processor, 140 memory, 150 display, 1300 bus, PC1 to PCN particle counter, IM1 to IMN imaging device, BA1 to BAN beacon, DS1 to DSN distance sensor, CM1 to CMM camera, DR opening / closing sensor, TS Door, WR1 to WR6 workbench, AP1 to AP5 manufacturing Location.

Claims (15)

A foreign matter detection unit that detects the number of foreign matter at a plurality of measurement points in the manufacturing area,
A foreign matter distribution creation unit that creates a foreign matter distribution image representing the distribution of the quantity of foreign matter in the manufacturing area based on the detection result;
Based on the foreign matter distribution image, an abnormal area detection unit for detecting an abnormal area in which the number of foreign matter exceeds a predetermined threshold,
A foreign matter monitoring device, comprising: a notification unit that notifies the abnormal area. The foreign matter detection unit,
Including a plurality of particle counters arranged at the plurality of measurement points,
The foreign matter monitoring apparatus according to claim 1, wherein each of the plurality of particle counters detects the number of foreign matter for each size division. The foreign matter detection unit,
A plurality of imaging devices arranged at the plurality of measurement points,
The foreign matter monitoring apparatus according to claim 1, further comprising: an image processing unit that detects the number of foreign matter for each main color component based on a photographed image generated by each of the plurality of photographing apparatuses. The foreign matter detection unit,
A plurality of imaging devices arranged at the plurality of measurement points,
The type of foreign matter included in the captured image generated by each of the plurality of photographing apparatuses is determined using a learned model that inputs the photographed image photographed by the photographing apparatus and outputs the type of foreign matter. The foreign matter monitoring device according to claim 1, further comprising a foreign matter type determination unit. The foreign matter detection unit further includes
The image processing unit that binarizes a captured image generated by each of the plurality of imaging devices, and detects an amount of the determined type of foreign matter based on the binarized captured image. The foreign matter monitoring device described. The foreign matter distribution creation unit creates a plurality of foreign matter quantity estimated distributions corresponding to the plurality of measurement points, and each of the plurality of foreign matter quantity estimated distributions has a corresponding foreign matter quantity estimated distribution as a center. The value at the center indicates the first value, which is the quantity of the foreign matter at the corresponding measurement point,
The foreign matter monitoring device according to claim 1, wherein the foreign matter distribution creating unit creates a combined distribution in which the plurality of foreign matter quantity estimation distributions are combined, and sets the value of the combined distribution as a pixel value of the foreign matter distribution image. The foreign matter distribution creation unit creates one foreign matter quantity estimation distribution centered on a first measurement location where the maximum value among the foreign matter quantities at the plurality of measurement locations is measured, and the foreign matter quantity estimation distribution is created. The value at the center of indicates the first value, which is the quantity of foreign matter at the first measurement point,
The foreign matter distribution creation unit minimizes the sum of squares of the difference between the value of the foreign matter quantity estimation distribution at each of the plurality of measurement points and the detected quantity of the foreign matter at each of the plurality of measurement points. To determine the parameters of the foreign matter quantity estimation distribution,
The foreign matter monitoring apparatus according to claim 1, wherein a value of the foreign matter quantity estimation distribution having the determined parameter is set as a pixel value of the foreign matter distribution image.   The foreign matter monitoring device according to claim 6 or 7, wherein the foreign matter quantity estimation distribution is a distribution obtained by multiplying a normal distribution by the first value. The foreign matter detection unit detects the number of standard foreign matter scattered at the first position at a plurality of measurement points in the manufacturing area,
The foreign matter distribution creation unit creates a foreign matter distribution image representing a distribution of the number of standard foreign matter in the manufacturing area based on the detection result,
The foreign matter monitoring device further includes
The foreign matter monitoring device according to claim 1, further comprising an airflow analysis unit that obtains an airflow vector of the standard foreign matter based on the first position and a distribution of the number of the standard foreign matter in the foreign matter distribution image. The foreign matter monitoring device further includes
Position information output by beacons each mounted on a movable body that can move the manufacturing area, and the time when the abnormal area is detected from among a plurality of in-area event data consisting of the time of the position information and The foreign matter monitoring device according to claim 1, further comprising an abnormality factor analysis unit that extracts one or more in-area event data related to the position of the abnormal region. The foreign matter monitoring device further includes
From the event data in a plurality of areas, each of which is the time when the door is closed or the time when the door is opened, which is obtained by the signal of the sensor that detects the opening / closing operation of the door in the manufacturing area, the abnormal area is The foreign matter monitoring apparatus according to claim 1, further comprising an abnormality factor analysis unit that extracts event data in an area related to the detected time. The foreign matter monitoring device further includes
From each of a plurality of in-area event data consisting of distance information output from a sensor that detects the distance to the moving body in the manufacturing area, and time of the distance information, the time when the abnormal area is detected, The foreign matter monitoring device according to claim 1, further comprising an abnormality factor analysis unit that extracts one or more in-area event data related to the position of the abnormal region. The foreign matter monitoring device further includes
An abnormality factor analysis unit that extracts one or more frame images related to the time when the abnormal region is detected and the position of the abnormal region from a plurality of frame images obtained by photographing the manufacturing area is provided. The foreign matter monitoring device according to claim 1. The foreign matter monitoring device further includes
Quality control relating to the time when the abnormal area is detected and the position of the abnormal area from among a plurality of quality control information representing the manufacturing process of each product manufactured in the manufacturing area and the quality of the product. The foreign matter monitoring apparatus according to claim 1, further comprising an abnormality factor analysis unit that extracts information. Detecting the quantity of foreign matter at a plurality of measurement points in the manufacturing area,
Creating a foreign matter distribution image representing the distribution of the quantity of foreign matter in the manufacturing area based on the result of the detection;
Detecting an abnormal area in which the number of foreign substances exceeds a predetermined threshold value based on the foreign substance distribution image,
And a step of notifying that the abnormal area has been detected.

Images