Foreign matter composite image generation device, foreign matter composite image generation method, and data structure

Abstract

To more easily generate a defective image in which inspection parameters can be efficiently set. A foreign matter composite image generation device generates a foreign matter composite image obtained by compositing an image of a foreign matter on an image of a substrate in order to adjust an inspection parameter of an inspection program applied to inspection of the presence or absence of the foreign matter on the substrate on the basis of the image of the substrate, the foreign matter composite image generation device is provided with: a base information acquisition unit that acquires a substrate image, which is an image of the substrate, a foreign matter image, which is an image of the foreign matter, and foreign matter-related information, which is information related to the foreign matter image; and a compositing unit that composes the foreign matter image at a compositing position determined according to a predetermined rule of the substrate image to generate the composite foreign matter image.

Description

Foreign object composite image generation device, foreign object composite image generation method and data structure Technical Field

[0001] This invention relates to an apparatus for generating composite images of foreign objects, a method for generating composite images of foreign objects, and a data structure. Background Technology

[0002] Regarding substrate inspection apparatuses used to inspect the soldering condition of mounting components soldered onto printed circuit boards (hereinafter referred to as "substrate"), there is an increasing demand for full-surface foreign object inspection to ensure the overall quality of the substrate.

[0003] Therefore, a technique has been proposed to transfer the features of defects to a good product image and generate a simulated defective image (for example, see Patent Document 1).

[0004] In the inspection of foreign objects on the entire surface of a substrate, the entire surface of the substrate is the inspection area, and it is unknown where and what kind of defects occur. Therefore, in order to improve the inspection quality, it is important to determine where and what kind of defects are transferred to generate simulated defect images.

[0005] Regarding this point, in the technology described in Patent Document 1, since the user needs to explicitly specify the transfer coordinates of the defects, trial and error is required before generating a simulated defect image that improves inspection quality. Furthermore, even assuming the user manually generates a simulated defect image, there is a possibility that it may not become the simulated defect image that improves inspection quality. That is, even if inspection parameters are adjusted based on such a generated simulated defect image, over-detection or under-detection may still occur.

[0006] Existing technical documents

[0007] Patent documents

[0008] Patent Document 1: Japanese Patent Application Publication No. 2022-108855 Summary of the Invention

[0009] The problem that the invention aims to solve

[0010] The present invention was made in view of the problems mentioned above, and its object is to provide a technique for generating defective images that can more easily and efficiently set inspection parameters.

[0011] Methods for solving problems

[0012] The present invention, which addresses the aforementioned problems, is a foreign object composite image generation apparatus that generates a foreign object composite image. This foreign object composite image is obtained by compositing an image of a foreign object onto an image of a substrate, based on an image of the substrate, to adjust the inspection parameters of an inspection procedure used to check for the presence or absence of foreign objects on the substrate. The apparatus is characterized by comprising: a basic information acquisition unit that acquires an image of the substrate (i.e., a substrate image), an image of the foreign object (i.e., a foreign object image), and information associated with the foreign object image (i.e., foreign object association information); and a compositing unit that composites the foreign object image at a compositing position on the substrate image determined according to a predetermined rule to generate the foreign object composite image.

[0013] Therefore, by synthesizing a foreign object image at a synthesis position on the substrate image according to a prescribed rule suitable for adjusting inspection parameters, a foreign object composite image as a defective image can be generated, thus making it easier to generate defective images that allow for efficient setting of inspection parameters.

[0014] Furthermore, in this invention, the rule may also be based on an indicator value that includes whether or not there are any missed detections in the inspection.

[0015] Therefore, it is possible to generate composite images of foreign objects at composite locations on the substrate that are prone to missed detection, thus making it easier to generate defective images that allow for efficient setting of inspection parameters.

[0016] Alternatively, in this invention, the rule may be based on an index value that includes the difference between the measured value of the foreign object and the inspection parameter.

[0017] Therefore, it is possible to generate composite images of foreign objects at composite locations that take into account the ease of detection of foreign objects, thus making it easier to generate defective images that allow for efficient setting of inspection parameters.

[0018] Alternatively, in this invention, the rule may be based on an index value including the difference between the average color of the foreign object and the color of the position where the foreign object image should be synthesized from the substrate image, and the synthesis unit determines the synthesis position in ascending order of the difference.

[0019] Therefore, it is possible to generate composite images of foreign objects in locations where foreign objects are difficult to detect, thus making it easier to generate defective images that allow for efficient setting of inspection parameters.

[0020] Alternatively, in this invention, the synthesis location can be selected from the periphery of the substrate pads, the resist, or any of the automatically set parameters.

[0021] Therefore, in addition to automatically setting the synthesis position, the synthesis position can be limited to locations with high detection requirements, such as the periphery of the substrate pads or the resist, depending on the selection, thereby reducing the processing load when the synthesis position is predetermined.

[0022] Alternatively, in this invention, the foreign object composite image generation device may include a display unit that displays a confirmation screen, which includes: a foreign object image display area that displays the foreign object image in association with the foreign object association information; and a composite content display area that displays the composite position of each foreign object image displayed in the foreign object image display area and the number of foreign object images being composited.

[0023] Therefore, by confirming the screen, users can visually identify the foreign object images used in the generation of the foreign object composite image through the display associated with the foreign object information. In addition, they can confirm the composite content, including the composite location of each foreign object image and the number of foreign object images used in the composite.

[0024] Alternatively, in this invention, the basic information acquisition unit may obtain the foreign object image and the foreign object association information from the foreign object library, and the foreign object library may store the foreign object image and the foreign object association information together.

[0025] Therefore, it is possible to obtain foreign object images and foreign object image association information used in the generation of foreign object composite images from a pre-prepared foreign object library, thus making it easier to generate defective images that can efficiently set inspection parameters.

[0026] Alternatively, the foreign object storage facility may also be included in this invention.

[0027] Furthermore, this invention is a method for generating a foreign object composite image. The method generates a foreign object composite image by synthesizing an image of a foreign object onto an image of a substrate, based on an image of the substrate to adjust the inspection parameters of an inspection procedure used to check for foreign objects on the substrate. The method includes the following steps: obtaining an image of the substrate (i.e., a substrate image); obtaining an image of the foreign object (i.e., a foreign object image); obtaining information associated with the foreign object image (i.e., foreign object association information); and synthesizing the foreign object image at a synthesis position on the substrate image determined according to a predetermined rule to generate the foreign object composite image.

[0028] Therefore, by synthesizing a foreign object image at a synthesis position on the substrate image according to a prescribed rule suitable for adjusting inspection parameters, a foreign object composite image as a defective image can be generated, thus making it easier to generate defective images that allow for efficient setting of inspection parameters.

[0029] Alternatively, the rule can be based on an indicator value that includes whether or not any checks are missed.

[0030] Therefore, it is possible to generate composite images of foreign objects at composite locations on the substrate that are prone to missed detection, thus making it easier to generate defective images that allow for efficient setting of inspection parameters.

[0031] Alternatively, in this invention, the rule may be based on an index value that includes the difference between the measured value of the foreign object and the inspection parameter.

[0032] Therefore, it is possible to generate composite images of foreign objects at composite locations that take into account the ease of detection of foreign objects, thus making it easier to generate defective images that allow for efficient setting of inspection parameters.

[0033] Alternatively, in this invention, the rule may be based on an index value including the difference between the average color of the foreign object and the color of the position where the foreign object image should be synthesized from the substrate image, and the synthesis position may be determined in ascending order of the difference.

[0034] Therefore, it is possible to generate composite images of foreign objects in locations where foreign objects are difficult to detect, thus making it easier to generate defective images that allow for efficient setting of inspection parameters.

[0035] Alternatively, in this invention, the synthesis location can be selected from the periphery of the substrate pads, the resist, or any of the automatically set parameters.

[0036] Therefore, in addition to automatically setting the synthesis position, the synthesis position can be limited to locations with high detection requirements, such as the periphery of the substrate pads or the resist, depending on the selection, thereby reducing the processing load when the synthesis position is predetermined.

[0037] Alternatively, in this invention, the foreign object image and the foreign object association information may be obtained from a foreign object database, which stores the foreign object image and the foreign object association information together.

[0038] Therefore, it is possible to obtain foreign object images and foreign object image association information used in the generation of foreign object composite images from a pre-prepared foreign object library, thus making it easier to generate defective images that can efficiently set inspection parameters.

[0039] Furthermore, this invention is a data structure that associates a foreign object image with foreign object association information. The foreign object image is an image of a foreign object existing on a substrate. The foreign object association information includes information associated with the foreign object image. The foreign object association information is used to determine the synthesis position of the synthesized foreign object image when synthesizing the foreign object image on the image of the substrate to generate a synthesized foreign object image. The synthesized foreign object image is used to adjust the inspection parameters of the inspection procedure applied in the inspection of whether there are foreign objects on the substrate based on the image of the substrate.

[0040] Therefore, the following data structure is provided: This data structure is obtained by associating foreign object association information with foreign object images. The foreign object association information contains information associated with the foreign object images, and the foreign object association information is used to determine the composite position of the foreign object image on the substrate image when generating a composite foreign object image. Therefore, it is easier to generate defective images, i.e., composite foreign object images, that can efficiently set inspection parameters.

[0041] Invention Effects

[0042] According to the present invention, it is easier to generate defective images that allow for efficient setting of inspection parameters. Attached Figure Description

[0043] Figure 1 is a functional block diagram of the inspection system according to an embodiment of the present invention.

[0044] Figure 2 is an overall structural diagram of the production line according to an embodiment of the present invention.

[0045] Figure 3 is a diagram showing the hardware structure of an inspection device or the like according to an embodiment of the present invention.

[0046] Figure 4 is a flowchart illustrating the steps of generating and processing a composite image of foreign matter defects according to an embodiment of the present invention.

[0047] Figure 5 is a diagram showing an example of a basic image setting screen according to an embodiment of the present invention.

[0048] Figure 6 is a diagram showing an example of a preparation screen for generating a poorly synthesized image according to an embodiment of the present invention.

[0049] Figure 7 is a diagram showing an example of a preparation screen for generating a poorly synthesized image according to a modified example of the present invention. Detailed Implementation

[0050] [Application Example]

[0051] Hereinafter, application examples of the present invention will be described with reference to the accompanying drawings.

[0052] Figure 1 is a functional block diagram of inspection system 1. Figure 2 is an overall structural diagram of production line 2 including inspection system 1. Figure 3 is a hardware structural diagram of inspection device 10, etc.

[0053] Production line 2 is a surface mount line for soldering electronic components onto a printed circuit board. It includes a solder printing device X1, a placement machine X2, a reflow oven X3, a solder printing inspection device Y1, a component inspection device Y2, a visual inspection device Y3, an X-ray inspection device Y4, a management server 20, and a teaching terminal 30. The solder printing inspection device Y1, component inspection device Y2, visual inspection device Y3, X-ray inspection device Y4, management server 20, and teaching terminal 30 constitute inspection system 1. The inspection device 10 shown in Figure 1 is a collective term for the solder printing inspection device Y1, component inspection device Y2, visual inspection device Y3, and X-ray inspection device Y4.

[0054] The inspection device 10 captures images of the substrate via the imaging unit 12. The inspection unit 13 uses an inspection program that includes prescribed inspection logic and has learned an AI model (hereinafter also referred to as "AI model") to determine whether the image is good or bad based on the captured image. The captured image is stored in the image storage unit 14, and the image obtained by combining the image with the image of the good or bad inspection result is stored in the inspection result storage unit 15. In addition, the inspection device 10 includes a control unit 11 for controlling each part in order to perform the prescribed inspection process.

[0055] The management server 20 includes a workpiece information storage unit 21, an inspection parameter storage unit 22, a substrate image storage unit 23, an inspection result storage unit 24, a foreign object image storage unit 25, a foreign object image metadata storage unit 26, and a foreign object database 27. In the foreign object image storage unit 25, images (foreign object images) are cut out from images of substrates determined to be NG (not good) during inspection and visual inspection by the inspection device 10. The foreign object image metadata stored in the foreign object image metadata storage unit 26 is information associated with the foreign object image and is stored in association with the foreign object image stored in the foreign object image storage unit 25. The foreign object image metadata includes the inspection time, the type of foreign object (defect type), the location of the foreign object (defect location), the process in which the foreign object was generated, and the location (base) where the substrate to be extracted was produced. Defect types include, for example, solder balls, debris, chip scattering, and component scattering.

[0056] The workpiece information storage unit 21 is a designated area of ​​the memory 20d that stores information such as batch number and supplier related to the workpiece, such as the substrate, which is the object of inspection. The inspection parameter storage unit 22 is a designated area of ​​the memory 20d that stores parameters such as threshold values ​​for the inspection logic used in each inspection device. The substrate image storage unit 23 is a designated area of ​​the memory 20d that stores images of the substrate captured in each inspection device. The inspection result storage unit 24 is a designated area of ​​the memory 20d that stores the inspection results from each inspection device. The foreign object image storage unit 25 is a designated area of ​​the memory 20d that stores images of foreign objects. The foreign object image metadata storage unit 26 is a designated area of ​​the memory 20d that stores metadata related to the foreign object image, i.e., foreign object image metadata, associated with the foreign object image. The foreign object image and its associated foreign object image metadata constitute the foreign object library 27.

[0057] The teach pendant 30 is a device for users to set parameters such as thresholds of the prescribed inspection logic used in the inspection device 10, and feature quantities of the AI ​​model. The teach pendant 30 includes an inspection parameter adjustment unit 31 for adjusting the aforementioned parameters, a transfer unit 32, and an image readout unit 33. Additionally, the teach pendant 30 includes an inspection unit 34 that inspects (tests) the model image using the currently set logic and thresholds.

[0058] Figure 4 shows a flowchart illustrating the steps involved in generating poorly synthesized images.

[0059] First, foreign object transfer settings are performed according to each substrate ID and field of view No. (step S1). The substrate ID is identification information used to determine the substrate. The image obtained by photographing a substrate is divided into multiple fields of view, and a field of view No. is assigned to each field of view to determine its identity. The foreign object image is stored in the foreign object image storage unit 25 of the foreign object library 27 constituting the management server 20, and is determined by the foreign object ID.

[0060] Start the transfer process according to the user's instructions (step S2).

[0061] Here, regarding the transfer of foreign object images, steps S6 to S10 are performed according to each substrate ID (steps S3 to S13), each field of view No. (steps S4 to S12), and each foreign object ID (steps S5 to S11).

[0062] In step S6, the transfer unit 32 determines whether the defective transfer area is around the solder pad, the automatic transfer, or the resist transfer area, and branches the processing according to which of these the defective transfer areas is.

[0063] If the defective transfer area is around the solder pad, calculate the omissions and deviations at each coordinate point within the area around the solder pad (step S7). The deviation refers to the difference between the measured value of the foreign object registered in the foreign object library 27 and the inspection parameters.

[0064] When the defective transfer area is automated, calculate whether there are any missed detections or deviations at each coordinate point across the entire surface of the substrate (step S8).

[0065] If the defective transfer area is resist, calculate whether there are any missed detections or deviations at each coordinate point within the resist area (step S9).

[0066] Then, the transfer positions are determined according to the order of the number of missed detections from most to least, or the order of the deviation from smallest to largest, and the set number of foreign object images are transferred (step S10).

[0067] After performing the above-described steps S6 to S10 according to each substrate ID (steps S3 to S13), each field of view No. (steps S4 to S12), and each foreign object ID (steps S5 to S11), the output unit 30f of the teaching terminal 30 displays the generated result image and allows the user to confirm it (step S14).

[0068] Then, the user determines whether the quality of the resulting image is good (step S15). If the quality of the resulting image is determined to be problematic (NG), return to step S1; if it is determined to be without problems (OK), proceed to step S15.

[0069] In step S16, the user adjusts the inspection parameters via the inspection parameter adjustment unit 31. Regarding parameter adjustment, for example, the value of the inspection parameter column 140 in the basic image setting screen 100 shown in FIG5 can be changed by operating the input unit 30e.

[0070] Then, the user performs a simulation test on the result image based on the adjusted inspection parameters (step S17).

[0071] Based on the results of the simulation test, the user determines whether the parameters are optimal (step S18).

[0072] If the user determines that the inspection parameters are not optimal, return to step S16, readjust the inspection parameters, and if they are determined to be optimal, apply the inspection parameters to the mass production inspection (step S19).

[0073] Then, mass production inspection is performed based on the optimized inspection parameters (step S20).

[0074] Therefore, it is possible to easily generate foreign object defect images that allow for efficient setting of inspection parameters.

[0075] Example 1

[0076] The inspection system 1 of Embodiment 1 of the present invention will now be described in more detail with the aid of accompanying drawings. However, the structure of the apparatus and system described in this embodiment should be appropriately modified according to various conditions. That is, the scope of the present invention is not limited to the following embodiments.

[0077] Figure 1 is a functional block diagram of inspection system 1. Figure 2 is an overall structural diagram of production line 2, which includes inspection system 1.

[0078] Production line 2 is a surface mount line for soldering electronic components onto the surface of a printed circuit board. It mainly includes three processes: solder printing, component mounting, and reflow (solder welding).

[0079] Production line 2 includes: a solder printing device X1, a placement machine X2, and a reflow oven X3 as manufacturing equipment; a solder printing inspection device Y1, a component inspection device Y2, a visual inspection device Y3, and an X-ray inspection device Y4 as inspection equipment; a management server 20; and a teaching terminal 30. The solder printing inspection device Y1, component inspection device Y2, visual inspection device Y3, X-ray inspection device Y4, management server 20, and teaching terminal 30 constitute inspection system 1. The inspection device 10 shown in Figure 1 is a general term for the solder printing inspection device Y1, component inspection device Y2, visual inspection device Y3, and X-ray inspection device Y4; in practice, it corresponds to each of the visual inspection device Y3 and other inspection devices, or combinations thereof.

[0080] On production line 2, starting from the upstream side, a solder printing unit X1, a placement machine X2, and a reflow oven X3 are sequentially arranged. The solder printing unit X1 is a device that prints solder paste onto the electrode portions (called pads) of a printed circuit board using a screen printer. The placement machine X2 is a device used to pick up electronic components to be mounted on the substrate and place them onto the corresponding solder paste; it is also called a chip placement machine. The reflow oven X3 is a heating device used to heat and melt the solder paste, then cool it to solder the electronic components onto the substrate. When there are many or many types of electronic components mounted on the substrate, multiple placement machines X2 are sometimes installed on the surface mount production line.

[0081] In addition, an inspection system 1 is installed on production line 2. This inspection system 1 includes an inspection device that inspects the condition of the substrate at the exit of each process from solder printing to component installation to reflow, automatically detecting defects or the possibility of defects. In addition to automatically classifying good and defective products, the inspection system 1 also has the function of providing feedback (e.g., changes in the installation procedure) to the operation of manufacturing equipment such as the solder printing device X1 that constitutes production line 2 based on the inspection results and their analysis results.

[0082] In the inspection system 1, starting from the upstream side, there are solder printing inspection device Y1, component inspection device Y2, appearance inspection device Y3 and X-ray inspection device Y4.

[0083] The inspection device 10 shown in Figure 1 is a general term for the solder printing inspection device Y1, the component inspection device Y2, the appearance inspection device Y3, and the X-ray inspection device Y4. In practice, it corresponds to each of the appearance inspection devices, such as the appearance inspection device Y3, or combinations thereof. Furthermore, the inspection device 10 is not limited to the production line 2 illustrated in Figure 2. Additionally, the inspection system 1 also includes a management server 20 and a teaching terminal 30, which will be described later.

[0084] Solder printing device X1, mounting machine X2, reflow oven X3, solder printing inspection device Y1, component inspection device Y2, appearance inspection device Y3, X-ray inspection device Y4, management server 20, and teaching terminal 30 are connected to each other via network NW in a communicative manner.

[0085] Solder printing inspection apparatus Y1 is a device used to inspect the solder paste printing status on a substrate removed from solder printing apparatus X1. Hereinafter, the functional blocks of the inspection apparatus 10 shown in FIG1 will be described. In solder printing inspection apparatus Y1, an image sensor (camera) or similar imaging unit 12 captures images of the solder paste printed on the substrate. An inspection unit 13 uses an inspection program containing predetermined inspection logic and a learned AI model to determine the quality of the solder paste based on the captured images. The captured images are stored in an image storage unit 14, and images obtained from capturing the quality inspection results are associated with and stored in an inspection result storage unit 15. Furthermore, in order to perform the predetermined inspection process, solder printing inspection apparatus Y1 includes a control unit 11 that controls each part of the inspection apparatus 10.

[0086] The component inspection device Y2 is used to inspect the configuration of electronic components on a substrate removed from the mounting machine X2. The structure of the functional parts in the component inspection device Y2 is the same as that in the solder printing inspection device Y1, so detailed descriptions are omitted.

[0087] Visual inspection device Y3 is used to inspect the soldering quality of substrates removed from reflow oven X3. The structure of the functional parts in visual inspection device Y3 is the same as that in solder printing inspection device Y1, so detailed description is omitted.

[0088] X-ray inspection apparatus Y4 is a device used to inspect the soldering condition of a substrate using X-ray images. The structure of the functional parts of X-ray inspection apparatus Y4 is the same as that of solder printing inspection apparatus Y1, except that X-rays are used for imaging, so detailed descriptions are omitted.

[0089] Figure 3 is a hardware structure diagram of the inspection device 10. The inspection device 10 has the same structure as a general computer (information processing device), including a CPU (Central Processing Unit) 10a, a ROM (Read Only Memory) 10b, a RAM (Random Access Memory) 10c, a memory 10d, an input unit 10e, an output unit 10f, and a communication interface 10g. All components are connected to each other via a bus 10h in a manner enabling communication. The management server 20 and the teaching terminal 30 also have the same hardware structure as the inspection device 10; therefore, the corresponding structures of each device are shown in parentheses, and detailed descriptions related to each structure are omitted.

[0090] CPU 10a is a central processing unit that executes various programs and controls various structures, thereby realizing the aforementioned functional units. Specifically, CPU 10a reads programs from ROM 10b or memory 10d and executes the programs using RAM 10c as the working area. CPU 10a controls various structures and performs various arithmetic operations according to the programs recorded in ROM 10b or memory 10d. Here, CPU 10a and RAM 10c constitute the control unit 11. ROM 10b stores various programs and data. RAM 10c serves as the working area, temporarily storing programs or data. Storage device 10d is composed of HDD (Hard Disk Drive), SSD (Solid State Drive), or flash memory, storing various programs, including the operating system, and various data. Input unit 10e is, for example, a keyboard and / or mouse, used for various inputs. Output unit 10f is, for example, a monitor, displaying the user interface. Output unit 10f can also be a touch panel, functioning as input unit 10e. The 10g communication interface is an interface used to communicate with other devices, such as using standards like Ethernet (registered trademark), FDDI, or Wi-Fi (registered trademark).

[0091] The management server 20 is a computer device that manages the production line 2, which includes the inspection system 1. The management server 20 can be a single computer or multiple computers. Alternatively, all or part of the functions of the management server 20 can be installed in the computer built into any of the devices such as the solder printing apparatus X1 or the solder printing inspection apparatus Y1. Alternatively, some of the functions of the management server 20 can be implemented through a server on a network (such as a cloud server).

[0092] The management server 20 includes a workpiece information storage unit 21, an inspection parameter storage unit 22, a substrate image storage unit 23, an inspection result storage unit 24, a foreign object image storage unit 25, a foreign object image metadata storage unit 26, and a foreign object database 27. Each of these storage units is composed of a memory 20d.

[0093] The workpiece information storage unit 21 is a designated area of ​​the memory 20d that stores information such as batch number and supplier related to the workpiece, such as the substrate, which is the object of inspection. The inspection parameter storage unit 22 is a designated area of ​​the memory 20d that stores parameters such as threshold values ​​for the inspection logic used in each inspection device. The substrate image storage unit 23 is a designated area of ​​the memory 20d that stores images of the substrate captured in each inspection device. The inspection result storage unit 24 is a designated area of ​​the memory 20d that stores the inspection results from each inspection device. The foreign object image storage unit 25 is a designated area of ​​the memory 20d that stores images of foreign objects. These images can be generated from images captured in each inspection device or can be pre-prepared images. The foreign object image metadata storage unit 26 is a designated area of ​​the memory 20d that stores metadata related to the foreign object image, i.e., foreign object image metadata, in association with the foreign object image. The foreign object image and its associated foreign object image metadata constitute the foreign object library 27. In the foreign object image storage unit 25, an image (foreign object image) is cut out from the image of a substrate determined to be NG during inspection and visual inspection by the inspection device 10. The foreign object image metadata stored in the foreign object image metadata storage unit 26 is information associated with the foreign object image and is stored in association with the foreign object image stored in the foreign object image storage unit 25. The foreign object image metadata includes, but is not limited to, the inspection time, the type of foreign object (defect type), the location of the foreign object (defect location), the process in which the foreign object was generated, and the location (base) where the substrate to be extracted was produced. Here, defect types include, for example, solder balls, debris, chip scattering, component scattering, etc., but are not limited to. Furthermore, the conditions of the manufacturing apparatus that generated the foreign object and the batch information of the substrate to be extracted can be determined based on the inspection time in the foreign object image metadata, but this information may also be included in addition to the inspection time. Here, the foreign object image metadata corresponds to the foreign object association information of the present invention. Additionally, the foreign object database 27 corresponds to the data structure of the present invention.

[0094] The teaching terminal 30 is a device for setting parameters such as threshold values ​​of the prescribed inspection logic used in the inspection device 10, and feature values ​​of the AI ​​model, which are set by the user. The teaching terminal 30 includes an inspection parameter adjustment unit 31 for adjusting these parameters, a transfer unit 32, and an image reading unit 33. Additionally, the teaching terminal 30 has an inspection unit 34 that inspects (tests) the model image using the currently set logic and threshold values. The teaching terminal 30 may also include a learning unit that enables the AI ​​model to learn so that the printed circuit board can be pre-determined as good or bad based on the learning data in the inspection device 10. Here, the image reading unit 33 corresponds to the basic information acquisition unit of this invention. Furthermore, the transfer unit 32 corresponds to the synthesis unit of this invention. Here, the foreign object library 27 is provided in the management server 20, but it may also be provided in the teaching terminal 30.

[0095] Figure 4 shows a flowchart illustrating the steps involved in generating poorly synthesized images.

[0096] First, foreign matter transfer settings are performed according to each substrate ID and field of view No. (step S1). The process will be described below with reference to Figures 5 and 6.

[0097] Figure 5 shows the basic image setting screen 100 displayed on the output section 30f of the teaching terminal 30. The basic image setting screen 100 displays an image 110 of the substrate that will be the object to be transferred (foreign material). A substrate ID list 120 is displayed in the upper center of the basic image setting screen 100. The substrate ID list 120 consists of a substrate ID field 120a and a save date / time field 120b. In the substrate ID field 120a, substrate IDs are displayed in rows such as 9001, 9002, etc. In the save date / time field 120b, the date and time when the image of the substrate determined by the substrate ID is saved, such as 2022 / 11 / 11 11:22:33, 2022 / 11 / 11 11:23:34, etc., are displayed in rows. Here, row 120c of substrate ID 9001 is given a background color, and the text is displayed differently from other rows, as if it were blank, indicating that substrate ID 9001 has been selected. Furthermore, the substrates ID9901 to 9007 are substrates manufactured in the past, and therefore display the storage date and time. Here, the teaching terminal 30 corresponds to the foreign object synthesis image generation apparatus of the present invention, and the output unit 30f corresponds to the display unit of the present invention.

[0098] Additionally, a simulation test result list 130 is displayed on the upper right side of the basic image setting screen 100. The simulation test result list 130 displays the simulation test results for each field of view No. of the substrate determined by the selected substrate ID. The simulation test result list 130 includes a field of view No. column 130a, a pass / fail count column 130b, a fail / miss count column 130c, and a miss / miss count column 130d. In the field of view No. column 130a, the field of view No. is displayed in each row as shown in columns 1 and 2. In the pass / fail count column 130b, the fail / miss count column 130c, and the miss / miss count column 130d, the pass / fail count, fail / miss count, and miss / miss count of the simulation test results for the image of the field of view No. displayed in the field of view No. column 130a on the left are displayed, respectively. Here, the simulated test results for the image of field of view No. 1 are displayed as follows: over-detection count 2, detected count 0, and missed detection count 0. The simulated test results for the image of field of view No. 2 are displayed as follows: over-detection count 3, detected count 0, and missed detection count 0. Simulated test results are also displayed in rows No. 3 through No. 7. Here, row 130e of field of view No. 1 is given a background color, and the text is displayed differently from other rows, as if it were blank, indicating that field of view No. 1 has been selected.

[0099] The inspection parameter bar 140 is displayed in the lower center of the basic image setting screen 100. The inspection reference is displayed at the top of the inspection parameter bar 140, and the checkbox 140a for "Full-surface foreign object inspection" is selected below it, indicating that full-surface foreign object inspection is enabled. Additionally, various inspection parameters 140b are displayed below it. Here, as inspection parameters, the color height logical formula is set to color OR height, the area (mm²) is set to 0.1, the area ratio (%) and aspect ratio (%) are set to 60, the height reference (mm) is set to 0.01, and the color margin is set to 3. Here, area, area ratio, aspect ratio, and height are physical quantities, while the color margin is a color-related inspection parameter.

[0100] On the lower right side of the basic image setting screen 100, there are a simulation test button 150 and a defective image generation button 160. The user operates the input unit 30e and presses the simulation test button 150, thereby performing a simulation test in the inspection unit 34. By operating the input unit 30e and pressing the defective image generation button 160, as described later, the image reading unit 33 reads the foreign object image from the foreign object library 27, and the transfer unit 32 transfers the foreign object image to the image of the selected field of view No. Here, the substrate ID and field of view No. used for transferring the foreign object image can be manually selected, but automatic selection is also possible.

[0101] When the user presses the poorly synthesized image generation button 160, the display of the output unit 30f of the teaching terminal 30 changes to the poorly synthesized image generation preparation screen 200 shown in Figure 6.

[0102] The foreign object image bar 210 is displayed at the top of the defect image generation preparation screen 200. Above the foreign object image bar 210 is a line 211 listing the defect types. The lower section of line 211 is divided into multiple columns, arranged sequentially from left to right: solder ball bar 212, debris bar 213, chip scatter bar 214, and component scatter bar 215. Solder balls, debris, chip scatter, and component scatter are the contents of the defect types. In the solder ball bar 212, solder ball images 212a, 212b, and 212c, assigned identification numbers ID: 1, ID: 5, and ID: 9, are displayed sequentially from top to bottom. Similarly, in the debris bar 213, debris images 213a, 213b, and 213c, assigned identification numbers ID: 2, ID: 6, and ID: 10, are displayed sequentially from top to bottom. Similarly, in the chip debris column 214, chip debris images 214a, 214b, and 214c, assigned identification numbers ID: 3, ID: 7, and ID: 11, are displayed sequentially from top to bottom. Similarly, in the component debris column 215, component debris images 215a, 215b, and 215c, assigned identification numbers ID: 4, ID: 8, and ID: 12, are displayed sequentially from top to bottom. The solder ball images 212a-212c, debris images 213a-213c, chip debris images 214a-214c, and component debris images 215a-215c displayed in this foreign object image column 210 are examples of foreign object images registered in the foreign object image storage unit 25. Here, the composite defective image generation preparation screen 200 corresponds to the confirmation screen of the present invention. The foreign object image column 210 corresponds to the foreign object image display area of ​​the present invention.

[0103] On the lower left of the image generation preparation screen 200, a substrate ID list 220 is displayed. In the substrate ID list 220, substrate IDs are listed in rows such as 9001, 9002, etc. Here, row 221 for substrate ID 9001 is given a background color, and the text is displayed in a different way than other rows, indicating that substrate ID 9001 has been selected. On the right side of the substrate ID list 220, a field of view No. list 230 is displayed. In the field of view No. list 230, field of view No. is displayed in rows such as 1, 2, etc. Here, row 231 for field of view No. 1 is given a background color, and the text is displayed in a different way than other rows, indicating that field of view No. 1 has been selected.

[0104] Foreign object transfer list 240 is displayed in the lower center of the defective image generation preparation screen 200. Foreign object transfer list 240 consists of a foreign object ID column 241, a defective transfer location column 242, and a transfer number column 243. In the foreign object ID column 241, foreign object IDs such as 1, 2, etc., are displayed in each row. This foreign object ID is an ID assigned to each foreign object image in the foreign object image column 210. The defective transfer location column 242 shows the transfer position of the foreign object displayed in the foreign object ID column 241 on the left, and the selected image (here, the viewpoint No. 1 of substrate ID 9001). Here, it is displayed in each row as around the solder pad, resist, automatic, etc. Detailed settings for the transfer position will be described later. The defective transfer location column 242 is displayed as automatic by default, and its position is displayed by default if a previous generation location is registered in the foreign object library 27. In this way, by transferring the image to the location where the foreign object image registered in the foreign object library 27 was generated, it is possible to transfer to an area similar to the previous occurrence, thus generating a realistic defective image. Alternatively, the user can operate the input unit 30e to select an appropriate location from a drop-down list. Furthermore, the transfer position can be set in units of defect patterns (defect types) in the foreign object library 27. This allows for uniform setting of the transfer position. The transfer count column 243 displays the number of foreign objects displayed in the left-hand foreign object ID column 241 that have been transferred to the selected image, selected in the central defective transfer location column 242. Here, values ​​such as 10 are displayed in each row. The display of this transfer count column 243 can be set by the user operating the input unit 30e to select an appropriate value from a drop-down list, or the transfer count value can be stored internally and set automatically. Additionally, when transferring the selected foreign object, the transfer can be performed after changing the size of the foreign object or rotating it. Here, the foreign object transfer list 240 corresponds to the composite content display area of ​​this invention.

[0105] Up to this point, the processing described with reference to Figures 5 and 6 is carried out in step S1.

[0106] A generate button 250 is displayed on the lower right side of the defective image generation preparation screen 200. The user operates the input section 30e and presses the generate button 250, thereby generating a defective image of the substrate. This image is created by transferring the selected number of foreign object images (based on foreign object IDs selected in the foreign object transfer list 240) to the selected area in the images displayed in the substrate ID list 220 and the field of view No. list 230. When the transfer is complete, a new substrate ID is assigned and added to the substrate ID list 120. Here, substrate ID 9008 in the substrate ID list 120 of FIG. 5 represents the substrate image added in this way.

[0107] Returning to the flowchart in Figure 4, when the Generate button 250 is pressed, the transfer begins (step S2).

[0108] Here, regarding the transfer of foreign object images, steps S6 to S10 are performed according to each substrate ID (steps S3 to S13), each field of view No. (steps S4 to S12), and each foreign object ID (steps S5 to S11).

[0109] In step S6, the transfer unit 32 determines whether the defective transfer area is around the solder pad, the automatic transfer, or the resist transfer area, and branches the processing according to which of these the defective transfer areas is.

[0110] When the defective transfer area is around the solder pad, the presence or absence of missed detections and deviation is calculated at each coordinate within the area around the solder pad (step S7). Here, the following comprehensive processing is performed: the foreign object is transferred to the substrate around the solder pad, and the generated image is simulated and tested in the inspection unit 34 using the current inspection parameters to calculate the presence or absence of missed detections and deviation, and then moved to the next coordinate. Here, deviation refers to the difference between the measured value of the foreign object registered in the foreign object library 27 and the inspection parameters. The measured value of the foreign object refers to the area, height reference, etc., displayed in the inspection parameter column 140 of Figure 5. In this way, by limiting the defective transfer area to the area around the solder pad, the search processing time for the transfer position can be shortened, and by transferring foreign objects at possible locations, a realistic defect image can be generated. Here, the presence or absence of missed detections and deviation correspond to the index values ​​of the present invention, and the conditions based on them correspond to the rules stipulated in the present invention.

[0111] When the defective transfer area is automatically transferred, the presence or absence of any missed detections and deviations is calculated at each coordinate point across the entire surface of the substrate (step S8). Here, the following comprehensive processing is performed: the foreign object is transferred to the entire surface of the substrate, and a simulation test is performed on the generated image using the current inspection parameters in the inspection unit 34 to calculate whether there are any missed detections and deviations, and then the process moves to the next coordinate. The automatic transfer of defective areas corresponds to the automatic setting of the synthesis position in this invention.

[0112] When the defective transfer area is resist, the presence of missed detections and deviations at each coordinate point within the resist area is calculated (step S9). Here, the following comprehensive processing is performed: the foreign object is transferred to the resist, and a simulation test is performed on the generated image using the current inspection parameters in the inspection unit 34 to calculate whether there are any missed detections and deviations, and then the process is moved to the next coordinate. In this way, by limiting the defective transfer area to the resist area, the search processing time for the transfer position can be shortened, and by transferring foreign objects to areas where they may exist, a realistic defective image can be generated.

[0113] Then, the transfer positions are determined according to the order of most missed detections to least missed detections or the order of deviation from least to most, and a predetermined number of foreign object images are transferred to generate a composite defective image (step S10). This allows foreign objects to be transferred to areas with a high risk of missed detections. Here, the composite defective image corresponds to the foreign object composite image of the present invention. Furthermore, the transfer position corresponds to the composite position of the present invention.

[0114] The determination of the transfer position is not limited to the methods described in steps S7 to S10 above. Alternatively, the difference between the average color of the foreign object and the color of the candidate transfer position can be calculated, and the transfer position can be determined in ascending order of the difference. Here, the candidate transfer position corresponds to the position where the foreign object image should be synthesized according to the present invention. In addition, the difference between the average color of the foreign object and the color of the candidate transfer position corresponds to the index value of the present invention, and the situation based on this corresponds to the rules specified in the present invention.

[0115] In this case, the average color of multiple substrates can be used as the color for the transfer candidate positions. That is, the difference between the average color of the foreign object and the average color of the multiple substrates is calculated, and the transfer positions are determined in ascending order of the difference. In this way, even if the color deviates depending on the substrate, the true color of that type of substrate can be considered when determining the transfer position. In this case, the average color of the foreign object and the average color of the transfer candidate positions of the multiple substrates correspond to the index value of the present invention, and the situation based on this corresponds to the rules specified in the present invention.

[0116] Alternatively, the location with the largest color deviation among multiple substrates can be determined as the transfer location. Areas where deviations occur during manufacturing, such as those in the component, resist color, and resist pattern position, are difficult to detect. Therefore, if the transfer location is determined as described above, a composite image of foreign matter can be generated, which is beneficial for adjusting inspection parameters. In this case, the color deviation among multiple substrates corresponds to the index value of the present invention, and this situation corresponds to the rules specified in the present invention.

[0117] Alternatively, the transfer position can be determined by identifying the location with the largest height deviation among the multiple substrates. Since areas where height measurement errors or deviations in the position of components or resist patterns occur during manufacturing are difficult to detect, determining the transfer position in this way allows for the generation of a composite image of foreign matter that is beneficial for adjusting inspection parameters. In this case, the height deviation of the multiple substrates corresponds to the index value of the present invention, and this situation corresponds to the rules specified in the present invention.

[0118] Alternatively, at least two of the aforementioned indicator values ​​can be combined to determine the transfer location. For example, by combining color-related and height-related indicator values, it is possible to comprehensively determine the transfer location for hard-to-inspect areas.

[0119] After performing the above-described steps S6 to S10 according to each substrate ID (steps S3 to S13), each field of view No. (steps S4 to S12), and each foreign object ID (steps S5 to S11), the output unit 30f of the teaching terminal 30 displays the generated result image and allows the user to confirm it (step S14).

[0120] Then, the user determines whether the quality of the resulting image is good (step S15). If the result image is determined to have a quality problem (NG), the user returns to step S1; if it is determined to be without problems (OK), the user proceeds to step S15. Here, for example, the display screen of the result image on the output unit 30f of the teaching terminal 30 displays the OK button and the NG button to accept the user's judgment result input.

[0121] In step S16, the user adjusts the inspection parameters via the inspection parameter adjustment unit 31. Parameter adjustment can be performed, for example, by changing the value of the inspection parameter column 140 in the basic image setting screen 100 shown in FIG5 via the operation input unit 30e.

[0122] Then, the user performs a simulation test on the result image based on the adjusted inspection parameters (step S17). Here, a new substrate ID is assigned to the image of the substrate (result image) generated by the processing of steps S1 to S13, and substrate ID 9008 is registered at the bottom of the substrate ID list 120 of the basic image setting screen 100 shown in FIG5. Therefore, the user selects substrate ID 9008 and presses the simulation test button 150, thereby enabling the simulation test to be performed based on the adjusted inspection parameters.

[0123] The results of the simulation test are displayed in the View No. List 230 of the basic image setting screen 100 shown in Figure 5. Based on the results of the simulation test, the user determines whether the inspection parameters are optimal (step S18). Here, for example, the display screen of the output unit 30f of the teaching terminal 30 displays the OK button and the NG button to accept the user's judgment result input.

[0124] If the user determines that the inspection parameters are not optimal, the process returns to step S16, and the inspection parameters are adjusted again. If the parameters are determined to be optimal, they are applied to the mass production inspection (step S19). Here, the adjusted inspection parameters are stored in the inspection parameter storage unit 22 of the management server 20 and reflected in the inspection logic, etc., of the corresponding inspection device 10's inspection unit 13.

[0125] Then, mass production inspection is performed based on the optimized inspection parameters (step S20).

[0126] Therefore, it is possible to easily generate foreign object defect images that allow for efficient setting of inspection parameters.

[0127] [Variation Example]

[0128] Figure 7 shows an example of the display of the poor image generation preparation screen 201 of the modified example of Embodiment 1. For structures common to Embodiment 1, common symbols are used and detailed descriptions are omitted.

[0129] In Example 1, as illustrated in the flowchart shown in FIG4, depending on whether the defective transfer area is around the solder pad, automatic, or resist (step S6), the presence or absence of missed detections and the degree of deviation at each coordinate point are calculated (steps S7 to S9), and the transfer position is determined in order of decreasing missed detections or increasing deviation (step S10). The method for determining the transfer position is not limited to this. In this modified example, depending on whether the defective transfer area is around the solder pad, automatic, or resist, the foreign object image can be transferred at random positions in various locations.

[0130] In the image defect generation preparation screen 201 shown in Figure 7, the structures of the foreign object image column 210, the substrate ID list 220, and the field of view No. list 230 are the same as those in the image defect generation preparation screen 200 of Embodiment 1. The foreign object transfer list 240 in the image defect generation preparation screen 201 is different from that in the image defect generation preparation screen 200 of Embodiment 1. In addition to the foreign object ID column 241, the defective transfer area column 242, and the transfer number column 243, it also includes a condition column 244.

[0131] Condition bar 244 indicates the conditions for determining the transfer location of foreign objects displayed in foreign object ID bar 241. Here, for foreign object IDs 1 to 10, as explained in Embodiment 1, the condition for transfer to locations with high detection difficulty, such as those with many missed detections or small deviations, is displayed. For foreign object IDs 11 and 12, the condition for random transfer is displayed. The display of condition bar 244 can also default to "transfer to locations with high detection difficulty," and the user can operate the input section 30e to select "random transfer." In addition, the conditions that can be selected as conditions for determining the transfer location are not limited to these; the user can also select appropriate conditions from the drop-down list and display them in condition bar 244. Furthermore, locations with high detection difficulty are not limited to locations with many missed detections or small deviations as described above.

[0132] Here, the order of missed detections from most to least, or the order of deviation from smallest to largest, corresponds to the rules specified in this invention, as do locations where detection is difficult. Furthermore, randomness also corresponds to the rules specified in this invention.

[0133] It should be noted that, in order to compare the constituent elements and the configuration of the embodiments of the present invention, the symbols of the constituent elements of the present invention are described in conjunction with the accompanying drawings.

[0134] <Postscript 1>

[0135] A foreign object composite image generation apparatus (30) generates a foreign object composite image, which is obtained by compositing an image of a foreign object on an image of a substrate to adjust the inspection parameters of an inspection procedure applied in the inspection of whether there is a foreign object on the substrate. The foreign object composite image generation apparatus (30) is characterized in that it includes: a basic information acquisition unit (33) that acquires an image of the substrate, i.e., a substrate image, an image of the foreign object, i.e., a foreign object image, and information associated with the foreign object image, i.e., foreign object association information; and a synthesis unit (32) that synthesizes the foreign object image at a synthesis position determined according to a predetermined rule on the substrate image to generate the foreign object composite image.

[0136] <Appendix 2>

[0137] According to the foreign object synthesis image generation apparatus (30) described in Appendix 1, the rule is based on an index value that includes whether or not the inspection has any omissions.

[0138] <Appendix 3>

[0139] According to Appendix 1 or 2, the foreign object synthesis image generation apparatus (30) is characterized in that the rule is based on an index value including the difference between the measured value of the foreign object and the inspection parameter.

[0140] <Appendix 4>

[0141] According to any one of Appendix 1 to 3, the foreign object composite image generation apparatus (30) is characterized in that the rule is based on an index value including the difference between the average color of the foreign object and the color of the position of the substrate image where the foreign object image should be composited, and the compositing unit determines the compositing position in ascending order of the difference.

[0142] <Appendix 5>

[0143] The foreign object composite image generation apparatus (30) according to any one of appendices 1 to 4 is characterized in that the composite position can be selected from the periphery of the substrate pads, the resist, and any of the automatic settings.

[0144] <Appendix 6>

[0145] The foreign object composite image generation apparatus (30) according to any one of Appendices 1 to 5 is characterized in that it comprises a display unit (30f) for displaying a confirmation screen (200), the confirmation screen (200) comprising: a foreign object image display area (210) for displaying the foreign object image in association with the foreign object association information; and a composite content display area (240) for displaying the composite position of each of the foreign object images displayed in the foreign object image display area and the number of the foreign object images being composited.

[0146] <Appendix 7>

[0147] The foreign object composite image generation apparatus (30) according to any one of Appendix 1 to 6 is characterized in that the basic information acquisition unit (33) acquires the foreign object image and the foreign object association information from the foreign object library (27), and the foreign object library (27) stores the foreign object image and the foreign object association information together.

[0148] <Appendix 8>

[0149] The foreign object synthesis image generation apparatus (30) according to Appendix 7 is characterized in that it includes the foreign object library (27).

[0150] <Appendix 9>

[0151] A method for generating a composite image of foreign matter, wherein the composite image of foreign matter is obtained by synthesizing an image of foreign matter on an image of a substrate to adjust the inspection parameters of an inspection procedure applied in the inspection of whether there is foreign matter on the substrate. The method for generating the composite image of foreign matter includes the following steps: obtaining an image of the substrate, i.e., a substrate image; obtaining an image of the foreign matter, i.e., a foreign matter image; obtaining information associated with the foreign matter image, i.e., foreign matter association information; and synthesizing the foreign matter image at a synthesis position on the substrate image determined according to a predetermined rule to generate the composite image of foreign matter.

[0152] <Postscript 10>

[0153] According to the method for generating foreign object composite images as described in Appendix 9, the rule is based on an index value that includes whether or not there are any missed detections during the inspection.

[0154] <Postscript 11>

[0155] According to the foreign object synthesis image generation method described in Appendix 9 or 10, the rule is based on an index value that includes the difference between the measured value of the foreign object and the inspection parameter.

[0156] <Appendix 12>

[0157] The method for generating a composite image of a foreign object according to any one of Appendices 9 to 11 is characterized in that the rule is based on an index value including the difference between the average color of the foreign object and the color of the position of the substrate image where the foreign object image should be composited, and the composite position is determined in ascending order of the difference.

[0158] <Appendix 13>

[0159] The method for generating a composite image of foreign objects according to any one of Appendices 9 to 12 is characterized in that the composite position can be selected from any one of the following: the periphery of the pads of the substrate, positioning, and automatic setting.

[0160] <Appendix 14>

[0161] The method for generating a composite image of a foreign object according to any one of Appendices 9 to 13 is characterized in that the image of the foreign object and the associated information of the foreign object are obtained from a foreign object library (27), which stores the image of the foreign object and the associated information of the foreign object together.

[0162] <Postscript 15>

[0163] A data structure (27) is characterized in that the data structure is obtained by associating a foreign object image with foreign object association information, wherein the foreign object image is an image of a foreign object existing on a substrate, the foreign object association information includes information associated with the foreign object image, the foreign object association information is used to determine the synthesis position of the synthesized foreign object image when synthesizing the foreign object image on the image of the substrate to generate a synthesized foreign object image, and the synthesized foreign object image is used to adjust the inspection parameters of the inspection procedure applied in the inspection of whether there is a foreign object on the substrate based on the image of the substrate.

[0164] Label Explanation

[0165] 30: Teaching terminal; 32: Transfer unit; 33: Image reading unit.

Claims

1. A foreign object composite image generation apparatus that generates a foreign object composite image, wherein the foreign object composite image is obtained by compositing an image of a foreign object onto an image of a substrate based on an image of a substrate to adjust the inspection parameters of an inspection procedure applied in inspecting whether there is a foreign object on the substrate, the foreign object composite image generation apparatus being characterized in that it comprises: a basic information acquisition unit that acquires an image of the substrate, i.e., a substrate image, an image of the foreign object, i.e., a foreign object image, and information associated with the foreign object image, i.e., foreign object association information; and a compositing unit that composites the foreign object image at a compositing position of the substrate image determined according to a predetermined rule to generate the foreign object composite image.

2. The foreign object composite image generation device according to claim 1, characterized in that, The rules are based on indicator values ​​that include whether any checks have been missed.

3. The foreign object composite image generation device according to claim 1, characterized in that, The rule is based on an index value that includes the difference between the measured value of the foreign object and the inspection parameter.

4. The foreign object composite image generation device according to claim 1, characterized in that, The rule is based on an index value that includes the difference between the average color of the foreign object and the color of the position in the substrate image where the foreign object image should be synthesized. The synthesis unit determines the synthesis position in ascending order of the difference.

5. The foreign object composite image generation device according to claim 1, characterized in that, The synthesis location can be selected from the periphery of the substrate pads, the resist, or any of the automatic settings.

6. The foreign object composite image generation apparatus according to claim 1, characterized in that, The foreign object composite image generation device includes a display unit that displays a confirmation screen. The confirmation screen includes: a foreign object image display area that displays the foreign object image in association with the foreign object association information; and a composite content display area that displays the composite position of each foreign object image displayed in the foreign object image display area and the number of foreign object images being composited.

7. The foreign object composite image generation device according to claim 1, characterized in that, The basic information acquisition unit obtains the foreign object image and the foreign object association information from the foreign object database, and the foreign object database stores the foreign object image and the foreign object association information together.

8. The foreign object composite image generation apparatus according to claim 7, characterized in that, The foreign object synthesis image generation device includes the foreign object library.

9. A method for generating a composite image of foreign matter, wherein the composite image of foreign matter is generated by synthesizing an image of foreign matter onto an image of a substrate, wherein the inspection parameters of an inspection procedure applied in inspecting the presence or absence of foreign matter on the substrate are adjusted based on an image of the substrate, and the method for generating the composite image of foreign matter includes the following steps: The process involves obtaining an image of the substrate (i.e., a substrate image), obtaining an image of the foreign object (i.e., a foreign object image), obtaining information associated with the foreign object image (i.e., foreign object association information), and synthesizing the foreign object image at a synthesis position determined according to a predetermined rule in the substrate image to generate the foreign object composite image.

10. The method for generating a composite image of a foreign object according to claim 9, characterized in that, The rules are based on indicator values ​​that include whether any checks have been missed.

11. The method for generating a composite image of a foreign object according to claim 9, characterized in that, The rule is based on an index value that includes the difference between the measured value of the foreign object and the inspection parameter.

12. The method for generating a composite image of a foreign object according to claim 9, characterized in that, The rule is based on an index value that includes the difference between the average color of the foreign object and the color of the position in the substrate image where the foreign object image should be synthesized, and the synthesis position is determined in ascending order of the difference.

13. The method for generating a composite image of a foreign object according to claim 9, characterized in that, The synthesis location can be selected from the periphery of the substrate pads, the resist, or any of the automatic settings.

14. The method for generating a composite image of a foreign object according to claim 9, characterized in that, The foreign object image and the foreign object association information are obtained from a foreign object database, which stores the foreign object image and the foreign object association information together.

15. A data structure obtained by associating a foreign object image with foreign object association information, the foreign object image being an image of a foreign object existing on a substrate, the foreign object association information including information associated with the foreign object image, the foreign object association information being used to determine the synthesis position of the synthesized foreign object image when synthesizing the foreign object image on an image of the substrate to generate a synthesized foreign object image, the synthesized foreign object image being used to adjust the inspection parameters of an inspection procedure applied in the inspection of whether or not there is a foreign object on the substrate based on the image of the substrate.

Images