CN110579479A - PCB maintenance system and maintenance method based on false point defect detection - Google Patents

PCB maintenance system and maintenance method based on false point defect detection Download PDF

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Publication number
CN110579479A
CN110579479A CN201910732889.5A CN201910732889A CN110579479A CN 110579479 A CN110579479 A CN 110579479A CN 201910732889 A CN201910732889 A CN 201910732889A CN 110579479 A CN110579479 A CN 110579479A
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China
Prior art keywords
defect
defects
false point
image
pcb
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CN201910732889.5A
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Chinese (zh)
Inventor
诺尼·弗依斯沃瑟
凡·柯布兰
胡冰峰
陈朋飞
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Kangdai Imaging Technology (suzhou) Co Ltd
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Kangdai Imaging Technology (suzhou) Co Ltd
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Priority to CN201910732889.5A priority Critical patent/CN110579479A/en
Priority to KR1020227007669A priority patent/KR20220041212A/en
Priority to PCT/CN2019/121165 priority patent/WO2021027184A1/en
Publication of CN110579479A publication Critical patent/CN110579479A/en
Priority to TW109127011A priority patent/TWI757825B/en
Pending legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/956—Inspecting patterns on the surface of objects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/956—Inspecting patterns on the surface of objects
    • G01N21/95607—Inspecting patterns on the surface of objects using a comparative method
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/0008—Industrial image inspection checking presence/absence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8854—Grading and classifying of flaws
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8854—Grading and classifying of flaws
    • G01N2021/888—Marking defects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8883—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges involving the calculation of gauges, generating models
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/956—Inspecting patterns on the surface of objects
    • G01N21/95607—Inspecting patterns on the surface of objects using a comparative method
    • G01N2021/95615—Inspecting patterns on the surface of objects using a comparative method with stored comparision signal
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/956—Inspecting patterns on the surface of objects
    • G01N2021/95638—Inspecting patterns on the surface of objects for PCB's
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods
    • G01N2201/1296—Using chemometrical methods using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30141—Printed circuit board [PCB]

Abstract

the invention discloses a PCB (printed circuit board) overhauling system and method based on false point defect detection, wherein the system comprises automatic optical detection equipment, a database server and overhauling equipment, wherein the automatic optical detection equipment is used for scanning a printed circuit board to be detected to obtain a scanned image and comparing the scanned image with a corresponding standard image loaded by the database server to construct a defect list containing defect coordinate information of a defect corresponding to the primary judgment of the scanned image; the maintenance equipment can load the scanned image and the corresponding defect list through the database server, perform one-to-one re-inspection on the preliminarily determined defects of the scanned image at each defect coordinate in the defect list, delete the defects from the defect list if the re-inspection defects are false point defects, and maintain the defects at the residual defect coordinates in the corresponding defect list of the printed circuit board. The invention inspects the false point defect detected by AOI and then carries out maintenance, thereby greatly improving the maintenance efficiency.

Description

PCB maintenance system and maintenance method based on false point defect detection
Technical Field
The invention relates to the field of circuit board detection and maintenance, in particular to a PCB maintenance system and a maintenance method based on false point defect detection.
background
in the current highly developed electronic industry era, Printed Circuit boards (PCBs for short) have become one of the essential parts of products such as computers and electronic communication. The printed circuit board has the defect of short circuit or open circuit in the production process, and the quality of the printed circuit board determines whether the corresponding electronic device product is qualified or not, so the quality detection and the maintenance of the printed circuit board are very important.
in the prior art, automatic Optical Inspection equipment (AOI for short) is commonly used in the production process of circuit boards, the AOI can detect defects on PCBs, and then manual Inspection is performed according to the defects detected by the AOI. Present customer not only has the requirement to AOI self work efficiency, and the work efficiency requirement of accomplishing the maintenance after detecting AOI also more and more high, at present, general AOI supplier on the market, only can provide solitary AOI equipment, the PCB that is detected obtains the defect coordinate from AOI equipment after, remove maintenance equipment, according to this defect coordinate, the manual work is overhauld the defect one by one through maintenance equipment, this in-process, at data transmission, PCB panel transport, defect point overhauls etc. can all consume a large amount of time one by one.
the prior art lacks a solution for improving the detection and maintenance of the defects of the PCB.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a PCB (printed circuit board) overhauling system and method based on false point defect detection, which greatly improve the PCB defect detection and overhauling efficiency, and the technical scheme is as follows:
On one hand, the invention provides a PCB maintenance system based on false point defect detection, which comprises automatic optical detection equipment, a database server and maintenance equipment, wherein a defect virtual detection module for verifying false point defects is configured on the maintenance equipment;
the automatic optical detection equipment is used for scanning a printed circuit board to be detected to obtain a scanned image, and comparing the scanned image with a corresponding standard image loaded through a database server to construct a defect list, wherein the defect list comprises defect coordinate information of a preliminarily determined defect corresponding to the scanned image;
The database server is used for storing the scanning image output by the automatic optical detection equipment and a corresponding defect list;
the virtual defect detection module of the overhaul equipment can load a scanned image and a corresponding defect list through the database server, re-inspect the preliminarily judged defects of the scanned image at each defect coordinate in the defect list one by one, delete the defect from the defect list if the re-inspected defect is a false point defect, and overhaul the defects at the residual defect coordinates in the corresponding defect list of the printed circuit board by the overhaul equipment.
as a first optional technical solution, the rechecking the preliminarily determined defects includes: extracting a local image at a defect coordinate corresponding to the preliminarily determined defect, and determining whether the local image meets a short-circuit characteristic or a broken-circuit characteristic, wherein the short-circuit characteristic comprises a straight line connecting two flat cables, the broken-circuit characteristic comprises a gap on the flat cables, if any one of the characteristics is met, the defect is determined to be a real defect, otherwise, the defect is determined to be a false point defect.
as a second optional technical solution, the rechecking the preliminarily determined defects includes: extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, and judging whether the local image simultaneously meets the following conditions: if the non-linear, irregular and isolated graphs simultaneously meet the characteristics, the defects are judged to be false point defects.
As a third optional technical solution, the rechecking the preliminarily determined defects includes:
Loading a plurality of preset defect template images through a database server, wherein the defect template images are calibrated to be real defects or false point defects;
extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, and comparing the local image with the defect template image to find a defect template image with the highest similarity;
if the defect template image with the highest similarity is calibrated to be a real defect, judging the preliminarily judged defect to be a real defect; and if the defect template image with the highest similarity is calibrated to be a false point defect, judging the defect of the preliminary judgment to be the false point defect.
As a fourth optional technical solution, the rechecking the preliminarily determined defects includes: and extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, inputting the local image into a trained neural network model, and judging whether the defect is a real defect or a false point defect according to a result output by the neural network model.
Further, scanning the printed circuit board to be detected comprises scanning the PCB by adopting different viewing angle angles to obtain different viewing angle views, wherein the viewing angle views comprise a two-dimensional viewing angle view and a three-dimensional viewing angle view.
Furthermore, the overhaul equipment also comprises a movable camera device, wherein the camera device can be moved to the position of the residual defect coordinates in the defect list corresponding to the printed circuit board, and the defects at the position of the defect coordinates are amplified and displayed for manual overhaul.
furthermore, the number of the database servers is one, the number of the automatic optical detection devices and the number of the overhaul devices are multiple, and the numbers of the automatic optical detection devices and the overhaul devices are the same or different.
On the other hand, the invention provides a PCB overhauling method based on false point defect detection, which comprises the following steps:
scanning a printed circuit board to be detected to obtain a scanned image;
Comparing the defect coordinate information with a standard image of the printed circuit board, taking the difference as a preliminarily judged defect, and constructing a defect list, wherein the defect list comprises defect coordinate information of the preliminarily judged defect corresponding to the scanned image;
performing one-to-one rechecking on the preliminarily determined defects of the scanned image at each defect coordinate in a defect list, and deleting the defects from the defect list if the rechecked defects are false point defects;
and repairing the defects at the residual defect coordinates in the defect list corresponding to the printed circuit board.
Further, the rechecking of each preliminarily determined defect includes the steps of:
Extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, and judging the local image according to any one of the following modes:
The first way is to judge whether the local image meets the short-circuit characteristic or the open-circuit characteristic, wherein the short-circuit characteristic comprises a straight line connecting two flat cables, the open-circuit characteristic comprises that a gap exists on the flat cables, if any one characteristic is met, the defect is judged to be a real defect, otherwise, the defect is judged to be a false point defect;
the second way is to determine whether the local image simultaneously satisfies the following conditions: if the non-linear, irregular and isolated graphs simultaneously meet the characteristics, the defects are judged to be false point defects;
the third mode is that the similarity comparison is carried out between the local image and a plurality of preset defect template images which are calibrated as real defects or false point defects, and whether the defects are the real defects or the false point defects is judged according to the calibration of the defect template image with the highest similarity;
And the fourth mode is that the local image is input into a neural network model which finishes training, and whether the defect is a real defect or a false point defect is judged according to the result output by the neural network model.
the invention has the following beneficial effects:
a. False point defects detected by AOI are checked, and the false point defects which do not need to be overhauled are eliminated and then overhauled, so that the overhauling efficiency is greatly improved;
b. The AOI is connected with the maintenance equipment through the database server, so that efficient data transmission is realized;
c. The maintenance equipment is provided with a movable camera device, so that the real defects after the false point defects are eliminated are positioned, amplified and displayed, and the manual maintenance efficiency is improved;
d. A plurality of AOI equipment are provided with a set of database server and are connected with a plurality of maintenance equipment, so that the space and the cost are saved.
drawings
the subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description when read with the accompanying drawings in which:
FIG. 1 is a schematic structural diagram of a PCB repairing system based on false point defect detection according to an embodiment of the present invention;
FIG. 2 is a characteristic diagram of a real short defect scan image according to an embodiment of the present invention;
FIG. 3 is a characteristic diagram of a scanned image of a false point defect according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a single database server corresponding to multiple AOIs and multiple VVRs according to an embodiment of the present invention;
FIG. 5 is a two-dimensional view of a PCB scanned with a two-dimensional view according to an embodiment of the present invention;
FIG. 6 is a three-dimensional view of a PCB scanned using three-dimensional views according to an embodiment of the present invention;
fig. 7 is a flowchart of a PCB repairing method based on false point defect detection according to an embodiment of the present invention.
Detailed Description
In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present invention.
the subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description when read with the accompanying drawings.
it will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity.
because the illustrative embodiments of the present invention may be implemented, to a great extent, using electronic components and circuits known to those skilled in the art, as described above, no greater detail is necessary to understand or appreciate the underlying concepts of the invention so as not to obscure or distract from the teachings of the present invention.
the PCB maintenance system based on the false point defect detection comprises an automatic optical detection device (hereinafter referred to as AOI), a database server (database Server) and a maintenance device, wherein a defect virtual detection module (also called as a virtual verification detection module, referred to as VVR) for verifying the false point defect is configured on the maintenance device, and the automatic optical detection device and the defect virtual detection module are both in communication connection with the database server;
the automatic optical detection equipment is used for scanning a printed circuit board to be detected to obtain a scanned image, comparing the scanned image with a corresponding standard image loaded through a database server, taking a difference point obtained by comparison as a preliminarily judged defect, and constructing a defect list, wherein the defect list comprises defect coordinate information of the preliminarily judged defect corresponding to the scanned image;
The database server is used for storing the scanning image output by the automatic optical detection equipment and a corresponding defect list;
The virtual defect detection module of the overhaul equipment can load a scanned image and a corresponding defect list through the database server, re-inspect the preliminarily judged defects of the scanned image at each defect coordinate in the defect list one by one, delete the defect from the defect list if the re-inspected defect is a false point defect, and overhaul the defects at the residual defect coordinates in the corresponding defect list of the printed circuit board by the overhaul equipment.
As shown in fig. 1, after the AOI device scans the PCB, it can obtain an overall layout picture of the defect, and can accurately calibrate the coordinates of the corresponding defect point in the picture, and in the AOI device system, it also has a function of determining the type of the defect, such as missing soldering, multi-soldering, and soldering error of the circuit board. The system comprises a database server, a maintenance equipment, a VVR system, an intelligent judgment system and a visual inspection system, wherein the database server is connected with the AOI and has a data storage function, the database server can accurately store information input after AOI scanning, the VVR system is connected with the database server and is used for maintaining equipment, the VVR acquires defect information of corresponding plates in the database server, the 'false point' information and the 'false point' coordinate information in the defect information can be accurately judged through an intelligent judgment system or a manual picture inspection, the judged 'false point' information can be deleted through operation, and after the 'false point' is deleted, the 'false point' information is moved to a position corresponding to a 'true point' defect coordinate through a Video on the VVR equipment to be manually maintained.
All defect points scanned by AOI before comparing all need by solitary maintenance equipment, through the artifical method of overhauing defect point one by one, reduced a large amount of work and wasted on the processing of "false point", not only improved work efficiency, avoided artifical "false point" erroneous judgement of overhauing in addition.
in a preferred embodiment of the present invention, different viewing angle angles may be used to scan the PCB to obtain different viewing angle views, such as a two-dimensional view (for example, fig. 5) of a certain contrast, saturation, and hue or a 3D visual image (for example, fig. 6), and especially, as shown in fig. 6, the 3D scanning visual method may accurately determine "false points" and "true points", improve the accuracy of the determination, avoid the false point being mistakenly deleted, and provide image reference for subsequent manual repair, thereby facilitating manual maintenance.
as a first optional technical solution, the rechecking the preliminarily determined defects by using an elimination method includes: extracting a local image at a defect coordinate corresponding to the preliminarily determined defect, and determining whether the local image meets a short-circuit feature or a disconnection feature, wherein the short-circuit feature comprises a straight line (shown in fig. 2) connecting two flat cables, the disconnection feature comprises a gap (not shown) existing on the flat cables, if any one of the features is met, the defect is determined to be a real defect, otherwise, the defect is determined to be a false point defect. The "real defect" is that the manual repair is needed point by point, such as the multi-soldered narrow slit in fig. 2, which causes short circuit of the PCB, and then the narrow slit needs to be manually removed.
As a second optional technical solution, the rechecking the preliminarily determined defects by using the feature correspondence method includes: extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, and judging whether the local image simultaneously meets the following conditions: if the non-linear, irregular and isolated pattern (as shown in fig. 3) satisfies the above characteristics, the defect is determined to be a false point defect. The 'false point defects' can be dust, stain, fingerprints or the like, a large number of false point defects exist in the PCB, the defects can be judged during AOI scanning, if the defects are not intelligently eliminated, a large number of workers are spent on the 'false point defects' during subsequent maintenance, and the VVR system is introduced in the embodiment of the invention, so that the time cost in the aspect can be greatly reduced.
As a third optional technical solution, the rechecking the preliminarily determined defects by using the similarity matching method includes:
Loading a plurality of preset defect template images through a database server, wherein the defect template images are calibrated to be real defects or false point defects;
Extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, and comparing the local image with the defect template image to find a defect template image with the highest similarity;
If the defect template image with the highest similarity is calibrated to be a real defect, judging the preliminarily judged defect to be a real defect; and if the defect template image with the highest similarity is calibrated to be a false point defect, judging the defect of the preliminary judgment to be the false point defect.
as a fourth optional technical solution, the rechecking the preliminarily determined defects includes: and extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, inputting the local image into a trained neural network model, and judging whether the defect is a real defect or a false point defect according to a result output by the neural network model. The neural network model can adopt a deep neural network in the prior art and train the neural network by combining a back propagation algorithm and a random gradient descent method.
in a preferred embodiment of the present invention, the inspection and repair equipment further includes a movable camera device, and the camera device can move to the defect coordinates remaining in the defect list corresponding to the printed circuit board, and magnify and display the defects at the defect coordinates for manual inspection and repair. In this embodiment, the camera device has two functions, wherein the first function is to move the camera device to the relative position of the defect to be overhauled so as to prompt an overhaul person to overhaul the PCB at the current relative position of the camera device; secondly, camera device can carry out high magnification to current defect area and show to make the maintainer clearly, confirm the defect that needs the maintenance at present fast, avoid the mistake to overhaul.
In a preferred embodiment of the present invention, as shown in fig. 4, the number of the database servers is one, and the number of the automatic optical inspection devices and the repair devices is plural, and the number of the automatic optical inspection devices and the number of the repair devices are the same or different. The system is characterized in that one set of database server is configured for a plurality of AOI devices and connected with a plurality of VVR systems, only one set of database server can be used at a client, the database server can work together with the plurality of AOIs and the plurality of VVRs, the database server can collect defect information of single or multiple PCBs and can collect defect information of a plurality of AOIs scanned PCBs, and therefore space and cost of the client can be saved through the system which can work through one set of database server.
in an embodiment of the present invention, there is provided a PCB repairing method based on false point defect detection, as shown in fig. 7, the repairing method includes the following steps:
S1, scanning the printed circuit board to be detected to obtain a scanned image;
S2, comparing the defect coordinate information with the standard image of the printed circuit board, taking the difference as the preliminarily judged defect and constructing a defect list, wherein the defect list comprises the defect coordinate information of the preliminarily judged defect corresponding to the scanned image;
S3, starting traversing the defect list, such as rechecking the preliminarily determined defects at the first defect coordinate in sequence;
s4, if the result of the retest is that the defect is a false point defect, executing S5, otherwise executing S6;
S5, deleting the false point defects obtained by the rechecking from the defect list;
S6, judging whether the traversal of the defects in the defect list is finished or not, if so, executing S7, otherwise, traversing the defects at the next defect coordinate in the defect list and continuing executing S4;
and S7, repairing the defects at the positions of the residual defect coordinates in the defect list corresponding to the printed circuit board.
as described in the above embodiment, the rechecking of each preliminarily determined defect includes the steps of:
extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, and judging the local image according to any one of the following modes:
the first way is to judge whether the local image meets the short-circuit characteristic or the open-circuit characteristic, wherein the short-circuit characteristic comprises a straight line connecting two flat cables, the open-circuit characteristic comprises that a gap exists on the flat cables, if any one characteristic is met, the defect is judged to be a real defect, otherwise, the defect is judged to be a false point defect;
The second way is to determine whether the local image simultaneously satisfies the following conditions: if the non-linear, irregular and isolated graphs simultaneously meet the characteristics, the defects are judged to be false point defects;
the third mode is that the similarity comparison is carried out between the local image and a plurality of preset defect template images which are calibrated as real defects or false point defects, and whether the defects are the real defects or the false point defects is judged according to the calibration of the defect template image with the highest similarity;
and the fourth mode is that the local image is input into a neural network model which finishes training, and whether the defect is a real defect or a false point defect is judged according to the result output by the neural network model.
The above four ways are described in detail in the above embodiments, and are not described herein again. The invention connects the defect coordinate and the scanned image (preferably, the predicted type of the defect) after AOI detection to the maintenance equipment through the database server, the maintenance equipment is simultaneously provided with a high-end defect virtual detection module, the defect point is automatically screened through the defect virtual detection module, the system classifies all gray scale defect images (the scanned image with the defect) as 'true' or 'false' and 'false' defects are deleted, the 'false' defects may be dust, dirt and the like, and thus can be deleted from the maintenance defect list; or the false defects are deleted from the repair defect list by manually inspecting the gray scale defect map provided by the AOI. The "real" defect will then be magnified by the camera and accepted by hand, thus saving time for inspection of the "false" defect point by the inspection equipment. The conversion of industry 4.0 is realized, a large amount of manual overhaul time is saved, the labor cost is reduced, and the probability of manual misjudgment is reduced.
the invention inspects the false point defects detected by AOI, and inspects the false point defects which do not need to be inspected after removing the false point defects, thereby greatly improving the inspection efficiency; the AOI is connected with the maintenance equipment through the database server, so that efficient data transmission is realized; the maintenance equipment is provided with a movable camera device, so that the real defects after the false point defects are eliminated are positioned, amplified and displayed, and the manual maintenance efficiency is improved; a plurality of AOI equipment are provided with a set of database server and are connected with a plurality of maintenance equipment, so that the space and the cost are saved.
further, those skilled in the art will appreciate that the boundaries between the above described operations are merely illustrative. Multiple operations may be combined into a single operation, a single operation may be distributed in additional operations, and operations may be performed at least partially overlapping times. Further, alternative embodiments may include multiple illustrations of specific operations, and the order of operations may be varied in various other embodiments.
however, other modifications, variations, and alternatives are also possible. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.
in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of other elements or steps than those listed in a claim. Furthermore, the terms "a" or "an," as used herein, are defined as one or more than one. Moreover, the use of the introductory phrases such as "at least one" and "one or more" in the claim recitations should not be interpreted to imply that the introduction of an indefinite articles "a" or "an" into another claim element limits any particular claim containing such introduced claim element to inventions containing only one such element, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an". The same holds true for the use of definite articles. Unless otherwise specified, terms such as "first" and "second" are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
while certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.

Claims (10)

1. a PCB maintenance system based on false point defect detection is characterized by comprising automatic optical detection equipment, a database server and maintenance equipment, wherein a defect virtual detection module used for verifying false point defects is configured on the maintenance equipment;
the automatic optical detection equipment is used for scanning a printed circuit board to be detected to obtain a scanned image, and comparing the scanned image with a corresponding standard image loaded through a database server to construct a defect list, wherein the defect list comprises defect coordinate information of a preliminarily determined defect corresponding to the scanned image;
The database server is used for storing the scanning image output by the automatic optical detection equipment and a corresponding defect list;
The virtual defect detection module of the overhaul equipment can load a scanned image and a corresponding defect list through the database server, re-inspect the preliminarily judged defects of the scanned image at each defect coordinate in the defect list one by one, delete the defect from the defect list if the re-inspected defect is a false point defect, and overhaul the defects at the residual defect coordinates in the corresponding defect list of the printed circuit board by the overhaul equipment.
2. The PCB overhaul system based on false point defect detection of claim 1, wherein the retesting of the preliminarily determined defects comprises: extracting a local image at a defect coordinate corresponding to the preliminarily determined defect, and determining whether the local image meets a short-circuit characteristic or a broken-circuit characteristic, wherein the short-circuit characteristic comprises a straight line connecting two flat cables, the broken-circuit characteristic comprises a gap on the flat cables, if any one of the characteristics is met, the defect is determined to be a real defect, otherwise, the defect is determined to be a false point defect.
3. The PCB overhaul system based on false point defect detection of claim 1, wherein the retesting of the preliminarily determined defects comprises: extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, and judging whether the local image simultaneously meets the following conditions: if the non-linear, irregular and isolated graphs simultaneously meet the characteristics, the defects are judged to be false point defects.
4. The PCB overhaul system based on false point defect detection of claim 1, wherein the retesting of the preliminarily determined defects comprises:
Loading a plurality of preset defect template images through a database server, wherein the defect template images are calibrated to be real defects or false point defects;
Extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, and comparing the local image with the defect template image to find a defect template image with the highest similarity;
If the defect template image with the highest similarity is calibrated to be a real defect, judging the preliminarily judged defect to be a real defect; and if the defect template image with the highest similarity is calibrated to be a false point defect, judging the defect of the preliminary judgment to be the false point defect.
5. The PCB overhaul system based on false point defect detection of claim 1, wherein the retesting of the preliminarily determined defects comprises: and extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, inputting the local image into a trained neural network model, and judging whether the defect is a real defect or a false point defect according to a result output by the neural network model.
6. The PCB inspection system based on false point defect detection of claim 1, wherein scanning the PCB to be inspected comprises scanning the PCB with different view angles to obtain different view angles, wherein the view angles comprise a two-dimensional view and a three-dimensional view.
7. the PCB inspection system based on false point defect detection of claim 1, wherein the inspection equipment further comprises a movable camera device, the camera device can move to the position of the defect coordinate left in the defect list corresponding to the PCB, and the defect at the position of the defect coordinate is displayed in an enlarged mode for manual inspection.
8. the PCB inspection system based on the detection of the false point defect as claimed in any one of the claims 1 to 7, wherein the number of the database server is one, the number of the automatic optical inspection equipment and the inspection equipment is plural, and the number of the automatic optical inspection equipment and the inspection equipment is the same or different.
9. a PCB maintenance method based on false point defect detection is characterized by comprising the following steps:
Scanning a printed circuit board to be detected to obtain a scanned image;
Comparing the defect coordinate information with a standard image of the printed circuit board, taking the difference as a preliminarily judged defect, and constructing a defect list, wherein the defect list comprises defect coordinate information of the preliminarily judged defect corresponding to the scanned image;
performing one-to-one rechecking on the preliminarily determined defects of the scanned image at each defect coordinate in a defect list, and deleting the defects from the defect list if the rechecked defects are false point defects;
and repairing the defects at the residual defect coordinates in the defect list corresponding to the printed circuit board.
10. The PCB overhauling method based on the false point defect detection as recited in claim 9, wherein the rechecking of each preliminarily judged defect comprises the following steps:
extracting a local image at a defect coordinate corresponding to the preliminarily judged defect, and judging the local image according to any one of the following modes:
the first way is to judge whether the local image meets the short-circuit characteristic or the open-circuit characteristic, wherein the short-circuit characteristic comprises a straight line connecting two flat cables, the open-circuit characteristic comprises that a gap exists on the flat cables, if any one characteristic is met, the defect is judged to be a real defect, otherwise, the defect is judged to be a false point defect;
The second way is to determine whether the local image simultaneously satisfies the following conditions: if the non-linear, irregular and isolated graphs simultaneously meet the characteristics, the defects are judged to be false point defects;
The third mode is that the similarity comparison is carried out between the local image and a plurality of preset defect template images which are calibrated as real defects or false point defects, and whether the defects are the real defects or the false point defects is judged according to the calibration of the defect template image with the highest similarity;
and the fourth mode is that the local image is input into a neural network model which finishes training, and whether the defect is a real defect or a false point defect is judged according to the result output by the neural network model.
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Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1955717A (en) * 2000-09-10 2007-05-02 奥博泰克有限公司 Reducing of error alarm in PCB detection
US20080002874A1 (en) * 2006-06-29 2008-01-03 Peter Fiekowsky Distinguishing reference image errors in optical inspections
CN106897994A (en) * 2017-01-20 2017-06-27 北京京仪仪器仪表研究总院有限公司 A kind of pcb board defect detecting system and method based on layered image
CN107093174A (en) * 2017-04-05 2017-08-25 湖北工业大学 A kind of PCB design defect inspection method
CN109752392A (en) * 2018-12-24 2019-05-14 苏州江奥光电科技有限公司 A kind of pcb board defect type detection system and method
CN109936927A (en) * 2019-04-10 2019-06-25 奥蒂玛光学科技（深圳）有限公司 Circuit board renovation method and system
CN110006903A (en) * 2018-01-05 2019-07-12 皓琪科技股份有限公司 Printed circuit board rechecks system, marker method and reinspection method

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003344309A (en) * 2002-05-30 2003-12-03 Olympus Optical Co Ltd Method of setting parameter for inspection equipment
US8290239B2 (en) * 2005-10-21 2012-10-16 Orbotech Ltd. Automatic repair of electric circuits
CN201440128U (en) * 2009-07-13 2010-04-21 北京航星科技有限公司 Automatic optical detection system for PCB defect detection
CN103926254A (en) * 2014-05-08 2014-07-16 康代影像科技(苏州)有限公司 Statistical system and method used for PCB defect detection
CN207908385U (en) * 2017-12-13 2018-09-25 深圳市光速达机器人科技有限公司 A kind of pcb board weld defect self-checking system that view-based access control model is differentiated
TWI703514B (en) * 2018-05-24 2020-09-01 牧德科技股份有限公司 Artificial intelligence recheck system and method thereof

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1955717A (en) * 2000-09-10 2007-05-02 奥博泰克有限公司 Reducing of error alarm in PCB detection
US20080002874A1 (en) * 2006-06-29 2008-01-03 Peter Fiekowsky Distinguishing reference image errors in optical inspections
CN106897994A (en) * 2017-01-20 2017-06-27 北京京仪仪器仪表研究总院有限公司 A kind of pcb board defect detecting system and method based on layered image
CN107093174A (en) * 2017-04-05 2017-08-25 湖北工业大学 A kind of PCB design defect inspection method
CN110006903A (en) * 2018-01-05 2019-07-12 皓琪科技股份有限公司 Printed circuit board rechecks system, marker method and reinspection method
CN109752392A (en) * 2018-12-24 2019-05-14 苏州江奥光电科技有限公司 A kind of pcb board defect type detection system and method
CN109936927A (en) * 2019-04-10 2019-06-25 奥蒂玛光学科技（深圳）有限公司 Circuit board renovation method and system

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111353983A (en) * 2020-02-28 2020-06-30 腾讯科技（深圳）有限公司 Defect detection and identification method and device, computer readable medium and electronic equipment
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CN111707677A (en) * 2020-06-24 2020-09-25 江西景旺精密电路有限公司 PCB appearance detection method and device
CN111812118A (en) * 2020-06-24 2020-10-23 阿丘机器人科技(苏州)有限公司 PCB detection method, device, equipment and computer readable storage medium
CN111808367A (en) * 2020-07-09 2020-10-23 浙江七色鹿色母粒有限公司 Improvement method for plastic PPR silver grain whitening defect
CN111808367B (en) * 2020-07-09 2023-05-30 浙江七色鹿色母粒有限公司 Improvement method for plastic PPR silver grain whitening defect
WO2022104585A1 (en) * 2020-11-18 2022-05-27 苏州康代智能科技股份有限公司 Remote optical inspection and maintenance method and system for printed circuit board
CN112415973A (en) * 2020-11-24 2021-02-26 广州广合科技股份有限公司 Intelligent control method and system for inner layer AOI (automated optical inspection) process
CN112415973B (en) * 2020-11-24 2022-05-17 广州广合科技股份有限公司 Intelligent control method and system for inner layer AOI (automated optical inspection) process
CN113012097B (en) * 2021-01-19 2023-12-29 富泰华工业（深圳）有限公司 Image rechecking method, computer device and storage medium
CN113012097A (en) * 2021-01-19 2021-06-22 富泰华工业（深圳）有限公司 Image rechecking method, computer device, and storage medium
CN112964737A (en) * 2021-02-04 2021-06-15 鼎勤科技(深圳)有限公司 Double-sided appearance detection method of circuit board
CN113138529A (en) * 2021-04-23 2021-07-20 成都路维光电有限公司 Mask defect detection method and system based on AOI system
CN113138529B (en) * 2021-04-23 2024-04-09 成都路维光电有限公司 Mask defect detection method and system based on AOI system
CN113030123B (en) * 2021-05-27 2021-08-24 安福得鑫智能设备有限公司 AOI detection feedback system based on Internet of things
CN113030123A (en) * 2021-05-27 2021-06-25 南昌华梦达航空科技发展有限公司 AOI detection feedback system based on Internet of things
CN113554626A (en) * 2021-07-26 2021-10-26 朗华全能自控设备（上海）股份有限公司 Method and device for detecting defects of flexible circuit board
CN114782431A (en) * 2022-06-20 2022-07-22 苏州康代智能科技股份有限公司 Printed circuit board defect detection model training method and defect detection method
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