JP4419778B2 - Substrate inspection device, parameter setting method and parameter setting device - Google Patents

Description

  The present invention relates to a technique for automatically generating parameters used in a substrate inspection apparatus.

  Conventionally, there has been proposed a board inspection apparatus for inspecting the mounting quality of a printed circuit board on which a large number of electronic components are mounted. In this type of printed circuit board, “the shape of the solder pile when the electrode part of the electronic component and the land are soldered” is called a solder fillet. However, depending on the wetting of the electrode part of the electronic component, a solder fillet is formed. In some cases, the electronic component and the solder fillet are not in contact with each other. Therefore, in order to inspect the soldering quality, it is necessary to accurately grasp the shape of the solder fillet made of a free curve.

  However, in the conventional board inspection apparatus, since monochrome (single color) single illumination is used as a light source, it is difficult to perform image analysis of the three-dimensional shape of the solder fillet. Therefore, the quality of soldering cannot be determined, and the board inspection apparatus cannot be used practically.

  In order to solve such a problem, the present applicant has proposed a substrate inspection apparatus of the type shown in FIG. 22 (see Patent Document 1). This method is called the three-color light source color highlight method (or simply the color highlight method), and is a technology that obtains the three-dimensional shape of the solder fillet as a pseudo-color image by illuminating the inspection object with light sources of multiple colors. is there.

  It is said that the practical use of automatic inspection of printed circuit boards is practically after the advent of this color highlighting technology. In particular, at the present time when electronic components are miniaturized, it is difficult to visually determine the solder fillet shape, and it can be said that board inspection cannot be realized without a color highlight type substrate inspection apparatus.

  As shown in FIG. 22, the color highlight type substrate inspection apparatus images a light projecting unit 105 that irradiates the inspection target 107 on the substrate 110 with the three primary color lights at different incident angles, and reflected light from the inspection target 107. An imaging unit 106. The light projecting unit 105 includes three annular light sources 111, 112, and 113 that have different diameters and that simultaneously emit red light, green light, and blue light based on a control signal from the control processing unit. Yes. The light sources 111, 112, and 113 are arranged in directions corresponding to different elevation angles when viewed from the inspection target 107 while being centered on the position directly above the inspection target 107.

  When the inspection target (solder fillet) 107 is irradiated by the light projecting unit 105 having such a configuration, light of a color corresponding to the inclination of the surface of the inspection target 107 enters the imaging unit 106. Therefore, as shown in FIG. 23, when the soldering of the electronic component is good / when the component is missing / when the solder is insufficient, the color pattern of the captured image depends on the shape of the solder fillet. A clear difference appears. This facilitates image analysis of the three-dimensional shape of the solder fillet, and makes it possible to accurately determine the presence / absence of electronic components and the quality of soldering.

In the color highlight type substrate inspection system, color parameters (color conditions) representing “the color of a good product that should be” and “the color of a defective product that should be” are set in advance, and the corresponding color parameter is selected from the inspection image. The color area to be extracted is extracted, and the quality is determined based on various feature amounts (for example, area and length) of the extracted area. Therefore, prior to the actual inspection, it is necessary to set the color parameters used for the inspection, the types of feature amounts, the determination conditions (for example, threshold values) for separating good products from defective products, and the like. The color parameters, feature amounts, and determination conditions are collectively referred to as inspection logic or inspection parameters, and setting and adjusting the inspection logic is generally referred to as teaching.

  In order to improve the inspection accuracy, it is important to set the color parameter so that a significant and clear difference appears between the feature quantity indicated by the non-defective product and the feature quantity indicated by the defective product. That is, it can be said that the quality of teaching of color parameters directly affects the inspection accuracy.

  Therefore, the applicant has proposed a tool for supporting the setting of color parameters in the color highlight method as shown in FIG. 24 (see Patent Document 2). In this tool, it is possible to set the upper limit value and the lower limit value of each of the hue ratios ROP, GOP, BOP and lightness data BRT of red, green, and blue as color parameters. The input screen of FIG. 24 is provided with a color parameter setting unit 127 for inputting color parameter setting values, and a setting range display unit 128 for displaying a color range extracted by each set color parameter. ing. The setting range display section 128 displays a hue diagram 134 showing all colors obtained under a predetermined brightness. When the operator sets the upper limit value and lower limit value of each color parameter, Displays a confirmation area 135 surrounding the color extracted by the set color parameter. When the binarization display button 129 is pressed, the extraction result based on the current color parameter is displayed as a binary image. According to this tool, the operator can track the color parameters until an appropriate extraction result is obtained while viewing the confirmation area 135 and the binary image.

By the way, in the double-sided mounting substrate, there is a through hole that plays a role of wiring the front surface and the back surface. The double-sided mounting greatly contributes to the high density of the board, and the through hole plays an important role in connecting both sides of the board. However, in board inspection using a visual sensor based on image processing, through holes tend to cause misrecognition of components and wiring patterns and often have an adverse effect on inspection performance. Therefore, conventionally, in order to prevent deterioration of the inspection performance due to the through hole, it is common to remove the through hole at the pre-processing stage of the substrate inspection. Patent Documents 3 and 4 disclose techniques for erasing a through hole from an image by combining an image before through-hole processing and an image after processing.
Japanese Patent Laid-Open No. 2-78937 JP 9-145633 A Japanese Patent Laid-Open No. 4-120448 Japanese Patent Laid-Open No. 7-20061

  The board inspection apparatus has an advantage that a plurality of inspection items can be inspected at high speed and accurately at once for the mounting quality of the printed board. However, in actual operation of the board inspection device, the over logic that teaches each inspection logic according to the individual inspection object, does not miss the defective product, and determines that the non-defective product is a defective product is an allowable value ( Judgment accuracy must be sufficiently increased until the value can be kept below the value assumed in advance.

  However, in the color highlight type substrate inspection apparatus, although advanced substrate inspection that can withstand practical use is possible, teaching to suppress oversight and overdetection of defective products to a target value is difficult. Even if the color parameter setting support tool described above is used, it is inevitable that the setting of a color parameter will depend on the experience and intuition of the operator. In addition, no matter how good the operator is, the adjustment must be repeated on a trial and error basis, which is inefficient and requires a lot of labor and adjustment time.

In a manufacturing environment where the product life cycle is shortening and the manufacturing environment is changing rapidly, it is strongly desired to reduce teaching work and to further automate teaching.

  In teaching, it is preferable to prepare as many non-defective samples and defective samples as possible. However, in reality, it is difficult to predict defects that may occur in the board mounting line. It is also difficult to manufacture defective products. Therefore, teaching is often carried out only from good samples. In such a case, it is necessary for the operator to imagine the color of the defective product by relying on his own experience and intuition, and there is a problem that the accuracy of the color parameter to be created greatly depends on the skill of the operator.

  The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a technique capable of automatically generating parameters used in a substrate inspection apparatus.

  More specifically, an object of the present invention is to provide a technique capable of automatically generating highly accurate parameters even when there is no defective sample.

  The board inspection apparatus according to the present invention irradiates a mounting component on a board with light of a plurality of colors at different incident angles, and extracts a region that satisfies a predetermined color condition from an image obtained by imaging the reflected light. The component mounting state is inspected based on whether or not the feature value of the extracted area satisfies a predetermined determination condition. When light of a plurality of colors is irradiated at different incident angles, a color pattern corresponding to the angle of the surface of the inspection object is captured. For example, since good solder is inclined, the hue of light having the largest incident angle (hereinafter referred to as the first hue) appears in the image, whereas the solder at the time of a component missing defect is flat on the land. Therefore, there is a tendency that many hues of light having the smallest incident angle (hereinafter referred to as second hue) appear in the image. Therefore, it is possible to determine whether the soldered state is good or not by examining how much pixels of the first hue or the second hue are included in the solder area in the image.

  When creating (teaching) parameters such as color conditions and determination conditions, prepare as many non-defective images that contain many pixels of the first hue and defective images that contain many pixels of the second hue as teacher images. Is preferred. However, as described above, it is difficult to sufficiently prepare defective product images. However, since the color tone of the image changes depending on the imaging environment and device variations, the second hue cannot be accurately predicted without the teacher image.

  Therefore, the present inventors examined whether a color corresponding to the second hue appearing in the defective product image can be extracted from the image obtained by imaging the non-defective substrate. As a result, it has been found that even in the image of the non-defective substrate, the terminal portion of the solder, the electrode of the component, the metal portion around the through hole, etc. are imaged with a color close to the second hue. This is because the surface of these portions is flat (parallel to the substrate surface), like the solder at the time of component missing failure. In addition, it has been found that components, substrates, and the like that exhibit a second hue under visible light using a material that diffusely reflects are imaged with colors close to the second hue.

  Among these, the present inventors have focused particularly on the color of the metal portion around the through hole. This is because the through hole always exists if it is a double-sided mounting board, and because the through hole has a known shape and size, it is easy to discriminate by image recognition and extract colors.

  That is, the gist of the present invention is to generate a parameter only from a non-defective sample by using a color appearing around a through hole as a substitute for a defective solder color.

Specifically, the parameter setting device of the present invention includes a through-hole image acquisition unit, an image acquisition unit, a defective color range estimation unit, a mapping unit, a color range search unit, and a color condition setting unit. These functions are realized by a program of the information processing apparatus.

  In the parameter setting device, the through-hole image acquisition unit acquires a plurality of through-hole images obtained by imaging the through-holes on the substrate, and the image acquisition unit acquires the parts with good soldering. A plurality of non-defective images are acquired. The defective color range estimation means estimates a defective color range that can be taken by a pixel in a defective solder region from the color distribution of the through hole peripheral pixels in the through hole image. The mapping means maps the color of each pixel in the solder area in the non-defective image to a color space as a good point, maps a predetermined number of defective points in the defective color range of the color space, and searches for a color range. Means obtains a color range for separating the good point color from the bad point color based on the frequency distribution of the good points and bad points in the color space, and the color condition setting means obtains the obtained color range Is set as a color condition (color parameter) used in substrate inspection.

  Thereby, a color condition which is one of inspection parameters is automatically generated. In addition, since the defective color range is estimated from the color distribution of the peripheral pixels around the through hole, it is possible to generate a highly accurate parameter even if there is no defective product image.

  The color space used in the present invention may be a multi-dimensional color space consisting of at least three axes of brightness, hue, and saturation. For example, the color space is mostly contained in defective solder areas and almost in good solder areas. It is also preferable to use a two-dimensional color space composed of a saturation axis and a lightness axis for hues that tend not to be included (that is, the second hue). Employing the two-dimensional color space simplifies the color range search process. In the two-dimensional color space, if the color condition is composed of a lower limit and an upper limit of saturation and a lower limit and an upper limit of lightness, the color range becomes a rectangular area, and the color range search process is further simplified.

  Strictly speaking, the color appearing around the through hole is slightly different from the color appearing in the defective solder area. Specifically, the hues of the two are substantially the same, but the brightness tends to be different.

  Therefore, the defective color range estimation means may determine the defective color range by obtaining a color distribution range of the pixels around the through-hole and correcting the value range in the brightness direction of the color distribution range. By performing brightness correction in this way, a defective color range closer to the real thing (defective solder area) can be obtained, and the teaching accuracy of the color conditions is improved.

  Here, it is preferable that the defective color range estimation means expands the upper limit of the brightness of the color distribution range of the through-hole peripheral pixels to the maximum brightness value as the defective color range. The color appearing in the defective solder area is based on the knowledge that the brightness direction spreads larger than the color appearing around the through-hole, and the upper limit tends to reach the vicinity of the maximum value of brightness.

  The shape of the defective color range may be set freely. For example, a rectangular shape, a polygonal shape, a shape that is symmetrical to the distribution range of good points, or a shape that expands the color distribution range of the through-hole peripheral pixels may be used.

  Further, the number of defective points distributed in the defective color range (frequency), the distribution method, and the like may be freely set. For example, the number of defective points may be the same as the number of through-hole peripheral pixels, a sufficient number necessary for teaching may be set in advance, or a number approximately equal to the frequency of good points. As for the distribution method, it may be distributed uniformly within the defective color range, a normal distribution may be employed, or a weighted distribution may be employed based on an empirical or statistical tendency. It is also preferable to grasp the distribution range of good points and give a weight near the boundary between the distribution range and the defective color range.

  The color range search means may determine the color range so that the difference between the number of defective points and the number of good points included in the color range is maximized. With this algorithm, it is possible to automatically search for a color range that suitably separates good points and bad points.

  After the color condition is set as described above, a determination condition setting process may be executed. In that case, the parameter setting device of the present invention may include a feature amount histogram generation unit, a threshold value determination unit, and a determination condition setting unit. These functions are realized by a program of the information processing apparatus.

  In the parameter setting device, the feature amount histogram generating means extracts a pixel region that satisfies the color condition from the solder region in the non-defective image, and generates a feature amount histogram for the feature amount of the pixel region, The threshold value determining means calculates a threshold value for determining the feature value of the solder area in the good product image based on the frequency distribution of the feature value histogram, and the determination condition setting means is the calculated threshold value Is set as a determination condition used in substrate inspection. Thereby, a determination condition (threshold value), which is one of inspection parameters, is also automatically generated.

  Here, various features such as the area, area ratio, length, maximum width, center of gravity, and shape of the pixel region are assumed as the feature amount. What is necessary is just to employ | adopt one or two or more preferable feature-values according to the object which should be detected by test | inspection.

  The automatically generated parameters (color conditions and determination conditions) are stored in the storage unit of the substrate inspection apparatus and used for substrate inspection processing.

  Note that the present invention can be understood as a parameter setting method for a substrate inspection apparatus including at least a part of the above processing, or a program for realizing the method. The present invention can also be understood as a parameter setting device of a substrate inspection apparatus having at least a part of the means for executing the above processing, or a substrate inspection apparatus equipped with such a device. Each of the above means and processes can be combined with each other as much as possible to constitute the present invention.

  According to the present invention, even when there is no defective sample, parameters used in the board inspection apparatus can be automatically generated with high accuracy, and teaching work can be reduced and teaching can be automated.

  Exemplary embodiments of the present invention will be described in detail below with reference to the drawings.

<First Embodiment>
(Configuration of board inspection system)
FIG. 1 shows a hardware configuration of a board inspection system according to an embodiment of the present invention.

  The substrate inspection system includes a substrate inspection device 1 that executes substrate inspection processing and a parameter setting device 2 that automatically generates parameters used in the substrate inspection processing of the substrate inspection device 1. The board inspection apparatus 1 and the parameter setting apparatus 2 can exchange electronic data such as images and parameters via a wired or wireless network, or a recording medium such as an MO or a DVD. In the present embodiment, the board inspection apparatus 1 and the parameter setting apparatus 2 are configured separately, but it is also possible to integrate the function of the parameter setting apparatus into the board inspection apparatus main body.

(Configuration of board inspection equipment)
The board inspection apparatus 1 is an apparatus for automatically inspecting the mounting quality (soldering state, etc.) of the mounting component 21 on the board 20 by a color highlight method. The substrate inspection apparatus 1 generally includes an X stage 22, a Y stage 23, a light projecting unit 24, an imaging unit 25, and a control processing unit 26.

  Each of the X stage 22 and the Y stage 23 includes a motor (not shown) that operates based on a control signal from the control processing unit 26. By driving these motors, the X stage 22 moves the light projecting unit 24 and the imaging unit 25 in the X axis direction, and the Y stage 23 moves the conveyor 27 that supports the substrate 20 in the Y axis direction.

  The light projecting unit 24 includes three annular light sources 28, 29, and 30 that have different diameters and that simultaneously emit red light, green light, and blue light based on a control signal from the control processing unit 26. . The light sources 28, 29, and 30 are arranged in directions corresponding to different elevation angles when centered on the position directly above the observation position and viewed from the observation position. With this arrangement, the light projecting unit 24 irradiates the mounting component 21 on the substrate 20 with light of a plurality of colors (in this embodiment, three colors of R, G, and B) at different incident angles.

  The imaging unit 25 is a color camera, and is positioned downward at a position directly above the observation position. As a result, the reflected light on the substrate surface is imaged by the imaging unit 25, converted into color signals R, G, and B of the three primary colors and supplied to the control processing unit 26.

  The control processing unit 26 includes an A / D conversion unit 33, an image processing unit 34, an inspection logic storage unit 35, a determination unit 36, an imaging controller 31, an XY stage controller 37, a memory 38, a control unit (CPU) 39, and a storage unit 32. , An input unit 40, a display unit 41, a printer 42, a communication I / F 43, and the like.

  The A / D conversion unit 33 is a circuit that receives the color signals R, G, and B from the imaging unit 25 and converts them into digital signals. The digital grayscale image data for each hue is transferred to the image data storage area in the memory 38.

  The imaging controller 31 is a circuit including an interface for connecting the control unit 39 to the light projecting unit 24 and the image capturing unit 25, and the light amount of each light source 28, 29, 30 of the light projecting unit 24 based on the output of the control unit 39. And control such as maintaining the mutual balance of the hue light outputs of the imaging unit 25 is performed.

  The XY stage controller 37 is a circuit including an interface for connecting the control unit 39 to the X stage 22 and the Y stage 23, and controls driving of the X stage 22 and the Y stage 23 based on the output of the control unit 39.

  The inspection logic storage unit 35 is a storage unit that stores inspection logic used for substrate inspection processing. The board inspection apparatus 1 can perform a plurality of types of inspection processes such as a fillet inspection for inspecting a solder shape and a lack inspection for inspecting a missing part. The inspection logic is prepared for each type of inspection, and includes color parameters (color conditions) for extracting a predetermined color pattern (pixel area) from the image, types of feature values extracted from the color pattern, Consists of pass / fail judgment conditions related to the feature amount.

The image processing unit 34 performs a process of extracting a region satisfying the color parameter from an image obtained by imaging the component 21 on the substrate 20 and a process of calculating a predetermined feature amount from the extracted region. It is. The determination unit 36 is a circuit that receives the feature amount calculated by the image processing unit 34 and executes processing for determining whether the component mounting state is good or not based on whether or not the feature amount satisfies a predetermined determination condition.

  The input unit 40 includes a keyboard, a mouse, and the like necessary for inputting operation information and data related to the substrate 20. The input data is supplied to the control unit 39. The communication I / F 43 is for transmitting and receiving data to and from the parameter setting device 2 and other external devices.

  The control unit (CPU) 39 is a circuit that executes various arithmetic processes and control processes. The storage unit 32 is a storage device composed of a hard disk and a memory, and stores the CAD information of the substrate, the determination result of the substrate inspection process, and the like in addition to the program executed by the control unit 39.

  FIG. 2 shows a functional configuration of the substrate inspection apparatus 1. The board inspection apparatus 1 includes an instruction information reception function 10, a board carry-in function 11, a CAD information reading function 12, a stage operation function 13, an imaging function 14, an inspection logic reading function 15, an inspection function 16, a determination result writing function 17, and a board. An unloading function 18 is provided. These functions are realized by the control unit 39 controlling the hardware according to a program stored in the storage unit 32. In addition, a CAD information storage unit 32 a that stores CAD information and a determination result storage unit 32 b that stores determination results are provided inside the storage unit 32.

(Board inspection processing)
Next, substrate inspection processing in the substrate inspection apparatus 1 will be described. Here, component missing inspection will be described as an example of board inspection processing. The component missing inspection is a process for determining whether or not the mounting position of a component is normal, and is performed in order to find missing defects such as component misalignment and missing.

  As shown in the upper part of FIG. 3, when the component is mounted at a normal position (design position), the solder fillet shape has a wide inclined surface like a mountain skirt from the component 21 to the land on the substrate 20. It becomes. On the other hand, in the defective defective product, the solder spreads over the entire land, and the solder fillet shape at the center of the land becomes flat.

  When these solder fillets are imaged by the substrate inspection apparatus 1, images as shown in the middle of FIG. 3 are obtained. Since the red, green, and blue irradiation lights are incident on the solder fillet at different angles, the hue of the reflected light that is incident on the imaging unit 25 changes according to the inclination of the solder fillet. That is, the reflected light of the blue light having the largest incident angle is dominant in the steep part, whereas the reflected light of the red light having the smallest incident angle is dominant in the part having little inclination. Therefore, a blue hue region is large in a non-defective solder fillet, and a red hue region is large in a defective solder fillet.

  In the missing inspection according to the present embodiment, such a tendency of the color pattern is used to determine whether or not the component is mounted based on the size (area) of the red region. Hereinafter, the flow of the missing inspection process will be described in detail with reference to the flowchart of FIG.

  The instruction information receiving function 10 is in a waiting state until instruction information for instructing execution of substrate inspection is input (step S100; NO, step S101). When instruction information is input from an external device by operating the input unit 40 or via the communication I / F 43, the instruction information receiving function 10 receives the instruction information, the board carry-in function 11, the CAD information reading function 12, and the inspection logic. The data is sent to the reading function 15 (step S100; YES). The instruction information includes information (model number, etc.) of the board to be inspected.

  The inspection logic reading function 15 reads the inspection logic corresponding to the model number of the board from the inspection logic storage unit 35 (step S102). Here, inspection logic for missing inspection is read. The inspection logic includes a color parameter (color condition) and a threshold value (determination condition).

  The board carry-in function 11 carries the board 20 to be inspected from the printed board carry-in section onto the conveyor 27 based on the instruction information (step S103), and the CAD information reading function 12 loads the CAD corresponding to the board model number. Information is read from the CAD information storage unit 32a (step S104).

  Next, the stage operation function 13 obtains information such as the dimensions, shape, and component arrangement of the substrate 20 from the read CAD information, and the plurality of components 21 mounted on the substrate 20 are sequentially observed positions (imaging positions). The X stage 22 and the Y stage 23 are operated via the XY stage controller 37 (step S105).

  On the other hand, the imaging function 14 causes the three light sources 28, 29, and 30 of the light projecting unit 24 to emit light via the imaging controller 31, and simultaneously irradiates red, green, and blue light onto the substrate 20. In addition, the imaging function 14 controls the imaging unit 25 via the imaging controller 31, and images the component 21 on the substrate 20 in synchronization with the operation of the stages 22 and 23 (step S106). The captured image is taken into the memory 38.

  Next, the inspection function 16 extracts a solder region from the captured image by the image processing unit 34 (step S107). The extraction of the solder region can be automatically performed by template matching, for example.

  Subsequently, the inspection function 16 binarizes the extracted solder region using the color parameter (step S108). The color parameter used here is composed of four values, a lower limit and an upper limit of red saturation and a lower limit and an upper limit of lightness. In the binarization process, pixels included in the color range defined by the color parameter are converted to white pixels, and other pixels are converted to black pixels.

  The lower part of FIG. 3 shows the solder area after binarization. By binarizing with the color parameter, it can be seen that only the red color region in the solder region is extracted as a white pixel, and the difference (feature) between the non-defective image and the defective product image is clarified.

  Subsequently, the inspection function 16 uses the image processing unit 34 to extract the feature amount of the white pixel region. Here, the area (number of pixels) of the white pixel region is calculated as the feature amount. Then, the inspection function 16 passes the area value of the white pixel region to the determination unit 36, and the determination unit 36 compares the area value of the white pixel region with a threshold value (step S109). If the area value exceeds the threshold value (step S109; YES), it is determined that the mounting quality of the component 21 is defective (there is a missing defect) (step S110), and the area value is equal to or less than the threshold value. (Step S109; NO), it is determined that the mounting quality of the component 21 is good (no missing defects) (step S111).

  The determination result writing function 17 writes the determination result together with the location ID (information for specifying a part) in the determination result storage unit 32b (step S112).

  When all the components on the board 20 have been inspected, the board carry-out function 18 carries out the board 20 by the printed board carrying section, and the board inspection process is finished (step S113).

According to the board inspection process described above, it is possible to accurately grasp the three-dimensional shape of the solder fillet from the color pattern appearing in the two-dimensional image, and to accurately determine the presence or absence of a defect.

  By the way, in order to achieve high determination accuracy so that defective products are not overlooked and overdetection is less than an allowable value, the color parameters (color conditions) and threshold values (determination conditions) of the inspection logic are set in advance. It is necessary to set the optimum value according to the inspection object. Conventionally, in order to accurately set parameters used for missing inspection, it has been essential to have a large number of missing defective samples (teacher images) and experienced experts. On the other hand, in the present embodiment, the parameter setting device 2 realizes automation of parameter generation, and even when there is no missing defective teacher image, the color appearing in the defective defective solder region only from the non-defective sample image. By estimating, it is possible to generate a highly accurate parameter.

  Hereinafter, the configuration and processing of the parameter setting device 2 will be described in detail separately for the case where there is a defective product image and the case where there is no defective product image.

[When there is a defective product image]
(Configuration of parameter setting device)
As shown in FIG. 1, the parameter setting device 2 is a general-purpose device including a CPU, a memory, a hard disk, an I / O control unit, a communication I / F, a display unit, an information input unit (keyboard and mouse), etc. as basic hardware. It is composed of a computer (information processing device).

  FIG. 5 shows a functional part for generating parameters from non-defective and missing defective teacher images in the functional configuration of the parameter setting device 2. The function part includes an instruction information reception function 50, a teacher image information reading function 51, an image acquisition function 52, a distribution function 53, a mapping function 54, a color range search function 55, a binarization function 56, and a feature amount histogram generation function 57. , A threshold value determination function 58, an inspection logic generation function 59, and an inspection logic writing function 60. These functions are realized by a program stored in a memory or a hard disk being read and executed by a CPU.

  In addition, a teacher image information DB 61 for storing teacher image information used for teaching is provided in the hard disk. The teacher image information includes an image of a mounted component imaged by the board inspection apparatus 1 and teacher information (teaching data) indicating whether the image is a non-defective product or a defective product. In order to increase the reliability of teaching, it is preferable to prepare dozens to thousands of teacher image information for each of the non-defective product and the defective product.

(Parameter setting process)
The flow of parameter setting processing will be described with reference to the flowchart of FIG. In the present embodiment, an example in which inspection parameters used in the above-described missing inspection are generated will be described.

  The instruction information receiving function 50 is in a waiting state until instruction information for instructing automatic generation of inspection logic is input (step S200; NO, step S201). When the instruction information is input from the information input unit, the instruction information receiving function 50 transmits the instruction information to the teacher image information reading function 51 (step S200; YES). This instruction information includes information for specifying teacher image information to be subjected to inspection logic generation, the type of inspection logic, and the like.

  The teacher image information reading function 51 reads teacher image information corresponding to the examination logic to be created from the teacher image information DB 61 in accordance with the instruction information (step S202). The teacher image information includes a non-defective image (an image in which the component mounting position is normal) and a defective product image (an image in which the component mounting position is not normal). Teacher information is given to these images.

FIG. 7 shows an example of a good product image and a defective product image. In the non-defective image, the component 62 is mounted at the designed position, and good solder fillets are formed in the land regions 63 and 63 at both ends of the component 62. The teacher information “good” is given to this image. On the other hand, in the defective product image, parts are missing, and flat solder fillets are formed in the land regions 65 and 65. The teacher information “bad” is given to this image.

  When the teacher image information is read, the image acquisition function 52 extracts a solder area from the image to which the teacher information is added (step S203). As shown in FIG. 8, the image acquisition function 52 has a template composed of a land window 70 and a component main body window 71, and enlarges / reduces the template, or relative to the land window 70 and the component main body window 71. The windows 70 and 71 are aligned with the land areas 63 and 65 and the part 62 in the image while shifting the position. If a part is missing as in the defective product image of FIG. 8, a part main body window 71 is temporarily placed in the middle of the land windows 70 at both ends. For example, a method such as template matching may be used for matching the windows. Thereby, the land areas 63 and 65 are specified for the non-defective product image and the defective product image, respectively. And the area | region except the overlap part with the component main body window 71 from the land window 70 is extracted as a solder area | region (refer the shaded part of FIG. 8). Note that the land area itself (the entire land window 70) may be handled as a solder area.

  Next, the distribution function 53 distributes the extracted solder area into the target image and the excluded image based on the teacher information (step S204). In this example, since the purpose is to detect defective products, the solder area to which the teacher information “defective” is assigned is the target image, and the solder area to which the teacher information “good” is assigned is the excluded image. The solder area to which the teacher information “ignore” is given is ignored.

  The target image extracted here represents a defective solder fillet when a missing defect occurs, and the excluded image represents a good solder fillet when the component mounting position is normal. Therefore, creating an optimal color parameter for missing inspection includes a color range (red color) of the pixel of the target image as much as possible, and has a color range that can almost eliminate the color of the pixel of the excluded image. Equivalent to finding the optimal solution.

  Therefore, first, the mapping function 54 maps the colors of all pixels of the target image and the excluded image to the color histogram (step S205). At this time, the pixel of the target image is “target point”, and the pixel of the excluded image is “excluded point”, and the mapping is performed in a mutually distinguishable format. The color histogram is obtained by recording the frequency (number) of pixels at each point in the color space. The color histogram can grasp the color distribution of the pixels constituting the solder area. Note that the pixel referred to here is the minimum resolution of an image. When the mapping process is executed collectively for a plurality of pixels, color mixing occurs, and therefore the process for each pixel is preferable.

  In general, the color space is composed of a multidimensional space of at least hue, saturation, and brightness. Therefore, in order to accurately grasp the color distribution of the pixel, it is preferable to use a multidimensional color histogram in which the color of the pixel is mapped in the multidimensional color space.

  However, in the color highlight method, since red and blue are used for the light source, a red or blue color tends to appear strongly in the solder area (this causes reflection close to specular reflection on the solder surface). Because.) Further, as described above, it is also known that most pixels have a blue color in a good solder region, and most pixels have a red color in a defective solder region.

Therefore, one color (for example, red) or two colors (for example, red) is used for the purpose of determining a color parameter for separating a color appearing in a defective solder area (target point) and a color appearing in a good solder area (exclusion point). For example, blue and red) are sufficient. Therefore, in the present embodiment, red is selected as a hue that is included in a large amount in the target image (defective solder region) and hardly included in the excluded image (good solder region), and the red saturation axis is selected. And a two-dimensional color histogram in which pixel colors are mapped in a two-dimensional color space consisting of brightness axes. This greatly simplifies the algorithm for finding the optimal color parameter solution.

  FIG. 9 shows an example of a two-dimensional color histogram. The horizontal axis represents the saturation of red. The larger the positive value, the stronger the red component, and the larger the negative value, the stronger the cyan component that is the complementary color of red. The vertical axis represents the brightness, and the brightness increases as the value increases. White circles (◯) in the histogram represent target points, and black triangles (▲) represent exclusion points. It can be seen that there is a difference in color distribution between the target point and the excluded point.

  Next, the color range search function 55 searches for a color range that optimally divides the color distribution of the target point and the color distribution of the exclusion point based on the two-dimensional color histogram (step S206). In the present embodiment, for simplification of the algorithm, as shown in FIG. 10A, the lower limit (RInf) and upper limit (RSup) of saturation and the lower limit (LInf) and upper limit (LSup) of lightness are included. Consider a rectangular color range. The optimum solution to be obtained here is a color range that includes as many target points (◯) as possible and hardly includes exclusion points (（).

Specifically, the color range search function 55 calculates the frequency total value E for each color range while changing the values of RInf, RSup, LInf, and LSup (see Equation 1), and the frequency total value E is maximized. Find the color range. The frequency total value E is an index representing the difference between the number of target points (frequency) included in the color range and the number of excluded points (frequency). FIG. 10B shows a color range in which the frequency total value E is maximum.

  Then, the color range search function 55 sets a color range in which the frequency total value E is maximized as an inspection color parameter (color condition). As described above, according to the present embodiment, it is possible to automatically generate color parameters for appropriately separating the target image (target point) and the excluded image (excluded point).

  Next, processing for automatically generating an inspection threshold value (determination condition) is executed using the color parameter.

  First, the binarization function 56 binarizes all the solder areas of the non-defective product image and the defective product image using the color parameter (step S207). In this binarization processing, pixels included in the color range defined by the color parameter are converted to white pixels, and other pixels are converted to black pixels.

As shown in FIG. 11, the area of white pixels is very large in a defective product image, and the area of white pixels is extremely small in a good product image. Therefore, when such a binarized image is used, it becomes easy to quantitatively calculate a feature amount for identifying a non-defective product or a defective product. Examples of the feature amount include the area, area ratio, center of gravity, length, maximum width, and shape of the white pixel region. Here, the area is selected as the feature amount.

  The feature amount histogram generation function 57 grasps the difference between the distribution trend of the feature amount of the non-defective image and the distribution trend of the feature amount of the non-defective image, and thus the area value of the white pixel region for each of the non-defective image and the defective product image. An area histogram is created for step S208. FIG. 12 shows an example of an area histogram of a non-defective product image and a defective product image (hereinafter simply referred to as “good product histogram” or “defective product histogram”). It can be seen that a clear difference appears between the feature quantity distribution of the non-defective image and the feature quantity distribution of the defective product image.

  Next, the threshold value determination function 58 calculates a threshold value for optimally separating the feature quantity of the non-defective product image and the feature quantity of the defective product image based on the frequency distribution of the non-defective product histogram and the defective product histogram ( Step S209). Various methods for optimally separating the two peaks appearing in the feature amount histogram have been proposed, and any method may be adopted here. For example, Otsu's discriminant analysis method may be used, or a point separated by 3σ from the edge of a non-defective image peak may be determined as a threshold based on experience. In this way, a threshold value for discriminating between non-defective products and defective products is generated.

  Then, the inspection logic generation function 59 generates inspection logic from the color parameter, the type of feature amount (area in this example), and the threshold value (step S210), and the inspection logic writing function 60 generates the inspection logic on the substrate. The data is written in the inspection logic storage unit 35 of the inspection device 1 (step S211).

  According to the processing described above, since inspection logic (parameters) used in component missing inspection is automatically generated, the time and load required for teaching can be greatly reduced.

  In addition, since the optimum color parameter and threshold value are calculated by the above-described algorithm, it is possible to perform pass / fail judgment by the color highlight method with high accuracy. Note that the reliability of the color parameter and the threshold value is improved as the number of teacher image information to be given first increases.

[When there is no defective product image]
In the case where there is no defective product image, in this embodiment, a color appearing around the through hole on the substrate is used as a substitute for the color of the defective solder area.

  A large number of through holes are provided in the double-sided mounting substrate. As shown in FIG. 13, around the through hole 80, a metal ring 81 for electrically connecting the wiring pattern on the front surface and the wiring pattern on the back surface is attached. Since the surface of the metal ring 81 is flat (substantially parallel to the substrate surface), blue and green reflected light hardly enters the imaging unit 25, and only red reflected light enters the imaging unit 25. This is the same as in the case of a defective solder fillet. Therefore, a color close to the missing defective solder area appears around the through hole.

  However, according to experiments by the present inventors, it has been found that the color appearing around the through hole is slightly different from the red color appearing in the defective solder area. Specifically, the hues of the two are substantially the same, but the red color that appears around the through-hole tends to have a lower brightness. The reason why the hues are common is considered to be that only light from the same red light source is incident on both. On the other hand, the lightness is different because the amount of reflected light incident on the imaging unit 25 is smaller than that of the solder fillet because the width of the surface of the metal ring 81 is narrow.

Therefore, in the present embodiment, red that appears around the through hole is extracted from an image obtained by imaging a non-defective substrate, and the lightness is corrected to approximate the color that appears in the missing defective solder region.

(Configuration of parameter setting device)
14 and 15 show functional parts for generating parameters from the through-hole image and the non-defective teacher image in the functional configuration of the parameter setting device 2.

  The parameter setting device 2 includes, as new functions, a substrate image reading function 90, a CAD information reading function 91, a through hole image acquisition function 92, a through hole peripheral pixel mapping function 93, an outlier deletion function 94, and a color distribution range setting function 95. A color distribution range correction function 96, a defective color information writing function 97, and a defective color information reading function 99. Among these, the through-hole peripheral pixel mapping function 93, the outlier deletion function 94, the color distribution range setting function 95, and the color distribution range correction function 96 constitute a defective color range estimation function. These functions are also realized by a program stored in a memory or a hard disk being read and executed by a CPU. In addition, a defective color information storage unit 98 for storing the defective color range estimated by the defective color range estimation function is provided in the hard disk.

(Bad color range estimation process)
A process of estimating a color range (defective color range) that can be taken by a pixel in a defective solder region from the color distribution of through-hole peripheral pixels in the through-hole image will be described with reference to the flowchart of FIG.

  First, the board image reading unit 90 reads a captured image (board image) of a printed board from the imaging unit 25 or the image information DB of the board inspection apparatus 1 (step S300). As a printed board to be imaged here, a good sample board in a good solder state may be used, or a bare board before component mounting may be used.

  Next, the CAD information reading function 91 reads the CAD information of the printed board corresponding to the read board image from the CAD information storage unit 32a of the board inspection apparatus 1 (step S301). This CAD information includes information such as the position and size of the through hole on the printed circuit board.

  The through-hole image acquisition function 92 acquires a plurality of through-hole images from the board image (step S302). Specifically, as shown in FIG. 17, the presence range 82 of the through hole 80 in the board image is roughly specified with reference to the CAD information, and then the through hole 80 is detected from the existence range 82 by image recognition processing. The detailed position 83 is specified. Since light is not reflected in the through hole 80, the through hole 80 appears black in the image captured by the color highlight type substrate inspection apparatus 1. Therefore, the detailed position 83 of the through hole 80 can be easily specified by applying a general circular search algorithm for black color (color with low brightness and saturation). When the detailed position 83 is specified, the through-hole image acquisition function 92 extracts a partial image including the periphery of the through-hole 80 as a through-hole image. As shown in FIG. 17, the color of the red light source appears around the through hole 80 (the portion of the metal ring 81).

  Subsequently, the through-hole peripheral pixel mapping function 93 samples a plurality of peripheral pixels of the through-hole 80 from the through-hole image, and maps the color of these peripheral pixels to the color histogram (step S303). At this time, it is preferable to sample ten to several hundred peripheral pixels from a plurality of through-hole images.

FIG. 18 shows an example of a color histogram. Here, 2 consisting of a red saturation axis and a brightness axis.
A dimensional color histogram is used. It can be seen that the red color of the pixels around the through-hole is distributed to a certain extent around the medium brightness. A point (outlier) that clearly deviates from the color distribution is noise.

  Since the outlier is an obstacle in defining the color distribution of the through-hole peripheral pixels, the outlier is deleted in advance by the outlier deletion function 94 (step S304). Specifically, the outlier removal function 94 grasps the main mass of the color distribution by hierarchical clustering or defines a range of several σ from the color centroid as the main mass of the color distribution, and deviates from the point that deviates from the mass. Consider it as a value and delete it from the color histogram. Then, the color distribution range setting function 95 calculates a rectangular range surrounding all the points remaining after the outlier deletion (step S305). This rectangular range represents the color distribution range 84 of the through-hole peripheral pixels.

  As described above, the color of the pixel around the through hole is lower in brightness than the color of the defective solder region. Therefore, the color distribution range correction function 96 calculates the defective color range 85 by correcting the value range in the lightness direction of the color distribution range 84 of the through-hole peripheral pixels (step S306). In this embodiment, the defective color range 85 is obtained by expanding the upper limit of the lightness of the color distribution range 84 to the lightness maximum value LMax. This is because the color appearing in the defective solder area has a larger spread in the brightness direction than the color appearing around the through hole, and the upper limit tends to reach the vicinity of the maximum value of brightness. Another reason is that the red color that appears in the good solder area gathers in the vicinity of low to medium brightness, so that even if the defective color range is expanded to the maximum brightness, the accuracy of the color parameters described later is hardly affected. It is.

  Through the above processing, the defective color range is accurately estimated from the color distribution of the through-hole peripheral pixels. The defective color range is stored in the defective color information storage unit 98 by the defective color information writing function 97 (step S307) and used for the following parameter setting process.

(Parameter setting process)
A flow of parameter setting processing using the estimated defective color range will be described with reference to the flowchart of FIG. Detailed description of the same processing as in the flowchart of FIG. 6 will be omitted.

  When instruction information for instructing automatic generation of inspection logic is input, teacher image information corresponding to the inspection logic is read from the teacher image information DB 61 (steps S200 to S202). The teacher image information includes only non-defective images.

  Then, the image acquisition function 52 extracts the solder area from the non-defective image by template matching or the like (step S400), and the mapping function 54 maps the color of all the pixels in the solder area as “good points” to the color histogram (step S400). S401). At this time, the mapping function 54 stores the frequency of mapped good points. FIG. 20 shows an example of a color histogram, which represents a good point mapped with a black triangle (▲).

  On the other hand, the defective color information reading function 99 reads the defective color range from the defective color information storage unit 98 (step S402). The mapping function 54 refers to the defective color range and maps a virtual “defective point” to the color histogram (step S403). At this time, the mapping function 54 uniformly distributes the number of defective points equal to the frequency of the good points stored previously in the defective color range. This is because, from experience and statistically, it is convenient to search for a color range if the defective points are plotted at the same rate as the good points.

Next, the color range search function 55 searches for a color range that optimally separates the good point color distribution and the bad point color distribution based on the color histogram (step S404). The color range search algorithm is almost the same as that described above (see FIG. 10), and the difference between the number of defective points and the number of good points included in the color range (frequency total value E) is maximized according to the following equation 2. A color range is required. This color range is set as a color parameter (color condition) for missing inspection.

  Subsequently, a process of automatically generating a threshold value (determination condition) for inspection using the color parameter is executed.

  The binarization function 56 binarizes all the solder areas of the non-defective image using the color parameters (step S405), and the feature amount histogram generation function 57 uses the area histogram regarding the area value of the white pixel area of the non-defective image. Is created (step S406). FIG. 21 shows an example of an area histogram.

  The threshold value determination function 58 calculates a threshold value for determining the feature amount (area value) of the solder region in the non-defective product image based on the frequency distribution of the area histogram (step S407). However, in the case of this processing, since only the non-defective image mountain is included in the area histogram, a method of separating two peaks as in the Otsu discriminant analysis method cannot be used. Therefore, in this case, it is only necessary to determine a threshold value by securing a sufficient margin so that a non-defective product is erroneously judged as a defective product (overdetection). Specifically, an empirical margin may be taken from the end point (upper limit) of the good product histogram, or a statistical method such as setting a threshold at a distance of 6σ from the center of the good product histogram may be used. In this way, a threshold value for discriminating between non-defective products and defective products is generated.

  Then, the inspection logic generation function 59 generates inspection logic from the color parameter, the type of feature amount (area in this example), and the threshold value (step S210), and the inspection logic writing function 60 generates the inspection logic on the substrate. The data is written in the inspection logic storage unit 35 of the inspection device 1 (step S211).

  According to the processing described above, even when there is no defective sample, inspection logic (parameters) used for missing part inspection can be automatically generated.

  Moreover, since the through-hole image obtained by imaging the board is used, the actual distribution range of the defective color can be accurately estimated, and highly reliable color parameters and threshold values can be generated. It is.

<Modification>
The above-described embodiment is merely an example of the present invention. The scope of the present invention is not limited to the above embodiment, and various modifications can be made within the scope of the technical idea.

For example, in the above embodiment, a two-dimensional color histogram (color space) is used, but a multi-dimensional (hue, saturation, brightness) color histogram may be used. Also for the two-dimensional color histogram, the saturation axis of other hues such as blue, green, and yellow may be used instead of the saturation axis of red, or the hue axis may be used instead of the saturation axis. The selection of the axis of the color histogram may be determined according to the tendency of the color pattern of the component image captured by the board inspection apparatus.

  Further, the color range is not limited to a rectangle, and a circle, a polygon, a free curve figure, or the like may be used. Furthermore, when the color histogram is multidimensional, the color range may be multidimensional.

  In the above embodiment, the processing for generating the parameters for missing inspection is given as an example. However, the present invention can be applied to other inspections as long as the parameters (color conditions) are generated based on the distribution range of defective colors. It can also be applied to parameter generation processing.

  In the above embodiment, the area is used as the feature amount. However, as the feature amount used for the pass / fail judgment, an area ratio, a length, a maximum width, a center of gravity, and the like can be preferably adopted. The area ratio is the occupation ratio of the binarized area in the land window. For example, if a component is soldered with a deviation from the land area, the area of the solder area increases and the area ratio changes. If this is regarded as a feature amount, it is effective for inspection of component displacement. The length is the length in the vertical direction or the horizontal direction of the white pixel region, and the maximum length is the maximum value among the lengths of the white pixel region. The center of gravity is the relative position of the center of gravity of the white pixel region with respect to the land window.

  Any feature amount may be used as long as it can accurately determine pass / fail, and it is also preferable to combine a plurality of types of feature amounts to improve accuracy. It is also possible to extract a plurality of types of feature amounts in the parameter setting process, and to adopt a feature amount that best separates non-defective products and defective products among them. In the above embodiment, an area histogram (area value histogram) is used to determine the determination condition (threshold value). However, if the type of feature amount is different, a feature amount histogram (area ratio histogram, length histogram, Maximum width histogram, centroid histogram, etc.). For example, if an area ratio histogram is used instead of the area value histogram, the pass / fail judgment is executed based on the pixel occupation rate binarized by the color parameter in the land window. Even when the size becomes smaller or larger, a determination process that is not affected by the size of the land window is possible.

  Further, the parameter setting device refers to the instruction information or the teacher information to determine the presence / absence of a defective product image. When there is a defective product image, the parameter setting process of FIG. 6 is executed. The processing may be automatically switched, such as executing the parameter setting processing of FIG. 16 and FIG.

  In the above embodiment, the same number of defective points as the good points are mapped, but the frequency of defective points may be set in advance. In that case, the process of counting the number of good points in the parameter setting process becomes unnecessary. In addition, since the frequency of defective points is known, the frequency of defective points to be distributed in the defective color range together with the defective color range in the defective color range estimation process and how to distribute them are also obtained, and the defective color is used as defective color information. You may make it store in information DB.

  In the above embodiment, the defect color information is calculated prior to the parameter setting process. For example, a through-hole image is also prepared in the teacher image information DB, and a series of parameter setting processes is performed. The defective color range may be calculated.

The figure which shows the hardware constitutions of the board | substrate inspection system which concerns on embodiment of this invention. The figure which shows the function structure of a board | substrate inspection apparatus. The figure which shows the relationship between the shape of a solder fillet, an imaging pattern, and a binarized image. The flowchart which shows the flow of a board | substrate inspection process. The figure which shows the function structure of the parameter setting apparatus in case a defective product image exists. The flowchart which shows the flow of the parameter setting process in case there exists a inferior goods image. The figure which shows an example of a non-defective product image and a defective product image. The figure which shows the extraction process of a solder area | region. The figure which shows an example of a two-dimensional color histogram. The figure which shows the search process of a color range. The figure which shows an example of the binarization result of a non-defective product image and a defective product image. The figure which shows the area histogram and threshold value determination process of a good article and inferior goods. The figure explaining the difference of the color which appears in the through hole periphery, and the color which appears in the soldering area | region of a defect defect. The figure which shows the function structure of the parameter setting apparatus in the case of no defective product image. The figure which shows the function structure of the parameter setting apparatus in the case of no defective product image. 6 is a flowchart showing a flow of defective color range estimation processing when there is no defective product image. The figure which shows the extraction process of a through-hole image. The figure which shows a defective color range estimation process. The flowchart which shows the flow of the parameter setting process in the case of no defective product image. The figure which shows the search process of the color range in the case of no defective product image. The figure which shows the threshold value determination process in the case of no defective product image. The figure which shows the structure of the board | substrate inspection apparatus of a color highlight system. The figure which shows an example of the color pattern which appears in a captured image. The figure which shows the setting support tool of a color parameter.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Board | substrate inspection apparatus 2 Parameter setting apparatus 10 Instruction information reception function 11 Board | substrate carrying-in function 12 CAD information reading function 13 Stage operation function 14 Imaging function 15 Inspection logic reading function 16 Inspection function 17 Judgment result writing function 18 Board | substrate unloading function 20 Board | substrate 21 Mounted parts 22 X stage 23 Y stage 24 Projection unit 25 Imaging unit 26 Control processing unit 27 Conveyor 28 Red light source 29 Green light source 30 Blue light source 31 Imaging controller 32 Storage unit 32a CAD information storage unit 32b Judgment result storage unit 33 A / D Conversion unit 34 Image processing unit 35 Inspection logic storage unit 36 Determination unit 37 XY stage controller 38 Memory 39 Control unit 40 Input unit 41 Display unit 42 Printer 50 Instruction information reception function 51 Teacher image information reading function 52 Image acquisition function 53 Distribution function 4 Mapping Function 55 Color Range Search Function 56 Binarization Function 57 Feature Quantity Histogram Generation Function 58 Threshold Determination Function 59 Inspection Logic Generation Function 60 Inspection Logic Write Function 62 Parts 63, 65 Land Area 70 Land Window 71 Parts Body Window 80 Through hole 81 Metal ring 82 Existing range of through hole 83 Detailed position of through hole 84 Color distribution range of pixels around through hole 85 Defect color range 90 Substrate image reading function 91 CAD information reading function 92 Through hole image acquisition function 93 Through hole Peripheral pixel mapping function 94 Outlier removal function 95 Color distribution range setting function 96 Color distribution range correction function 97 Defective color information writing function 98 Defective color information storage unit 99 Defective color information reading function

Claims (15)

A region satisfying a predetermined color condition is extracted from an image obtained by irradiating a component mounted on the board with light of multiple colors at different angles of incidence and imaging the reflected light, and the features of the extracted region Is a method of automatically generating parameters used in a board inspection apparatus that inspects the mounting state of the component depending on whether or not a predetermined determination condition is satisfied,
Information processing device
Obtain multiple through-hole images obtained by imaging through-holes on the board and multiple good-quality images obtained by imaging components with good soldering,
From the color distribution of pixels around the through hole in the through hole image, the defective color range that can be taken by the pixel in the defective solder area is estimated,
Mapping the color of each pixel of the solder area in the good product image as a good point to a color space, and mapping a predetermined number of defective points in the defective color range of the color space;
Obtaining a color range for separating the good point color from the bad point color based on the frequency distribution of good points and bad points in the color space;
A parameter setting method for a substrate inspection apparatus, wherein the obtained color range is set as a color condition used in substrate inspection. The information processing apparatus includes:
2. The parameter setting method for a substrate inspection apparatus according to claim 1, wherein the defective color range is determined by obtaining a color distribution range of the through-hole peripheral pixels and correcting a value range in the brightness direction of the color distribution range. The information processing apparatus includes:
3. The parameter setting method for a substrate inspection apparatus according to claim 2, wherein an upper limit of the brightness of the color distribution range of the through-hole peripheral pixels is expanded to the maximum brightness value as the defective color range. The information processing apparatus includes:
The parameter setting method of the board | substrate inspection apparatus of any one of Claims 1-3 which distributes the number of defective points substantially equal to the frequency of the said good point in the said defective color range. The information processing apparatus includes:
The parameter setting method for a substrate inspection apparatus according to claim 1, wherein the defective points are distributed substantially uniformly in the defective color range. The information processing apparatus includes:
6. The parameter setting method for a substrate inspection apparatus according to claim 1, wherein the color range is determined so that a difference between the number of defective points and the number of good points included in the color range is maximized. The information processing apparatus includes:
Extracting a pixel region that satisfies the color condition from the solder region in the non-defective image, and generating a feature amount histogram for the feature amount of the pixel region,
Based on the frequency distribution of the feature amount histogram, a threshold value for determining the feature amount of the solder area in the good product image is calculated,
The parameter setting method for a substrate inspection apparatus according to claim 1, wherein the calculated threshold value is set as a determination condition used in substrate inspection. A region satisfying a predetermined color condition is extracted from an image obtained by irradiating a component mounted on the board with light of multiple colors at different angles of incidence and imaging the reflected light, and the features of the extracted region Is an apparatus for automatically generating parameters used in a board inspection apparatus that inspects the mounting state of the component depending on whether or not a predetermined determination condition is satisfied,
Through-hole image acquisition means for acquiring a plurality of through-hole images obtained by imaging through-holes on a substrate;
A defective color range estimating means for estimating a defective color range that can be taken by a pixel in a defective solder region from a color distribution of pixels around the through hole in the through hole image;
Image acquisition means for acquiring a plurality of non-defective images obtained by imaging components with good soldering;
Mapping means for mapping the color of each pixel in the solder area in the non-defective image to a color space as a good point, and mapping a predetermined number of defective points in the defective color range of the color space;
Color range search means for obtaining a color range for separating the good point color and the bad point color based on the frequency distribution of the good point and the bad point in the color space;
Color condition setting means for setting the obtained color range as a color condition used in substrate inspection;
A parameter setting device for a substrate inspection apparatus comprising: The defective color range estimation means includes
9. The parameter setting device for a substrate inspection apparatus according to claim 8, wherein the defective color range is determined by obtaining a color distribution range of the through-hole peripheral pixels and correcting a value range in the brightness direction of the color distribution range. The defective color range estimation means includes
10. The parameter setting device for a substrate inspection apparatus according to claim 9, wherein an upper limit of the brightness of the color distribution range of the through-hole peripheral pixels is expanded to the maximum brightness value as the defective color range. The mapping means includes
The parameter setting device for a substrate inspection apparatus according to any one of claims 8 to 10, wherein a number of defective points substantially equal to the frequency of the good points are distributed in the defective color range. The mapping means includes
12. The parameter setting device for a substrate inspection apparatus according to claim 8, wherein the defective points are distributed substantially uniformly within the defective color range. The color range search means includes
The parameter setting device for a substrate inspection apparatus according to any one of claims 8 to 12, wherein the color range is determined so that a difference between the number of defective points and the number of good points included in the color range is maximized. A feature amount histogram generating means for extracting a pixel region satisfying the color condition from a solder region in the good product image and generating a feature amount histogram for a feature amount of the pixel region;
Threshold value determining means for calculating a threshold value for determining the feature amount of the solder region in the non-defective product image based on the frequency distribution of the feature amount histogram;
Determination condition setting means for setting the calculated threshold as a determination condition used in substrate inspection;
The parameter setting device for a substrate inspection apparatus according to any one of claims 8 to 13, further comprising: A storage unit that stores color conditions and determination conditions set by the parameter setting device according to claim 14;
A light projecting means for irradiating light of a plurality of colors at different incident angles on a mounting component on the substrate;
A region extracting means for extracting a region that satisfies the color condition from an image obtained by imaging the reflected light;
An inspection means for inspecting a soldering state of the component based on whether or not a feature value of the extracted region satisfies the determination condition;
A board inspection apparatus comprising:

Images