KR20190046225A - Method and apparatus for inspecting PCB pannel based on big data and artificial intelligence - Google Patents
Method and apparatus for inspecting PCB pannel based on big data and artificial intelligence Download PDFInfo
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- KR20190046225A KR20190046225A KR1020170139557A KR20170139557A KR20190046225A KR 20190046225 A KR20190046225 A KR 20190046225A KR 1020170139557 A KR1020170139557 A KR 1020170139557A KR 20170139557 A KR20170139557 A KR 20170139557A KR 20190046225 A KR20190046225 A KR 20190046225A
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
- H05K13/00—Apparatus or processes specially adapted for manufacturing or adjusting assemblages of electric components
- H05K13/08—Monitoring manufacture of assemblages
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Abstract
Description
본 발명은 인공지능 AOI(Automatic Optical Inspection)에 관한 것으로, 보다 상세하게는 빅 데이터와 인공지능(AI)을 이용하여 PCB 패널을 검사하는 방법 및 그 장치에 관한 것이다.BACKGROUND OF THE
일반적으로, 전자회로용 PCB 기판은, 절연 기판상에 박막, 후막 등에 의해 복수개의 회로소자를 형성하고, 소자 상호간을 막에 의해 접속하여 회로화한 후, 칩이나 소자 등 각종 탑재부품을 실장하여 완성되게 된다. Generally, a PCB substrate for an electronic circuit is formed by forming a plurality of circuit elements on an insulating substrate by a thin film, a thick film, etc., connecting the elements to each other through a film to form a circuit, mounting various mounted components such as chips and elements It is completed.
한편, 이러한 PCB 기판은 자동화 생산라인에 의해 대량으로 제작되어지기 때문에 생산설비에서의 이물질 발생으로 인한 회로패턴의 단선 또는 패턴폭 불량 등이 발생될 수 있기 때문에 검사과정에서 작업자가 이를 식별하여 스크레치를 표시함과 함께 식별이 용이한 마킹용 잉크를 이용하여 불량 PCB를 표시하게 된다.On the other hand, since such a PCB substrate is manufactured in a large quantity by an automated production line, a circuit pattern breakage or a pattern width defect may occur due to the generation of foreign substances in the production equipment. Therefore, The marking ink which is easy to be identified along with the marking is used to display the defective PCB.
본 발명이 이루고자 하는 기술적 과제는, 빅 데이터와 인공지능를 이용한 PCB 패널 자동 검사 방법 및 그 장치를 제공하는 데 있다.SUMMARY OF THE INVENTION It is an object of the present invention to provide a method and an apparatus for automatically inspecting a PCB panel using big data and artificial intelligence.
도 1은 본 발명과 종래 기술을 비교 도시한 도면이고,FIG. 1 is a view showing a comparison between the present invention and a conventional technique,
도 2는 본 발명의 실시 예에 따른 시스템 구성의 일 예를 도시한 도면이다. 서 실행한다.2 is a diagram showing an example of a system configuration according to an embodiment of the present invention. .
본 발명에 따르면, 하나의 장치에서 결함 검출 및 판정이 실행되므로 생산성이 높다. 또한, 빅 데이터와 인공지능을 이용하여 최상의 판정 신뢰도를 확보할 수 있다. According to the present invention, since defect detection and determination are performed in one apparatus, productivity is high. In addition, the best judgment reliability can be ensured by using big data and artificial intelligence.
도 1은 본 발명과 종래 기술을 비교 도시한 도면이고,
도 2는 본 발명의 실시 예에 따른 시스템 구성의 일 예를 도시한 도면이다. FIG. 1 is a view showing a comparison between the present invention and a conventional technique,
2 is a diagram showing an example of a system configuration according to an embodiment of the present invention.
도 1은 본 발명과 종래 기술을 비교 도시한 도면이고, 도 2는 본 발명의 실시 예에 따른 시스템 구성의 일 예를 도시한 도면이다. FIG. 1 is a diagram showing a comparison between the present invention and a conventional art, and FIG. 2 is a diagram showing an example of a system configuration according to an embodiment of the present invention.
도 1 및 도 2를 참조하면, 운영체계는 다음과 같다.1 and 2, the operating system is as follows.
1) Scan Camera를 이용하여, 검사 대상물로부터 영상을 취득한다.1) Obtain an image from the object to be inspected using a Scan Camera.
2) 취득된 영상을 빅데이터 Data Base의 이미지와 비교한다.2) Compare the acquired image with the image of Big Data Data Base.
3) 영상 정보의 이미지와 빅데이터의 이미지를 비교하여 결함 여부를 확정한다. 3) Confirm the defect by comparing the image of the image information with the image of the big data.
* 결합 여부를 확정하기 위하여, 기존에는 검사자가 영상을 확인하여 양품과 불량을 판정하였다. 인공지능 AOI에서는, 영상 정보를 통하여 검사 장치가 양품과 불량을 판정한다.* In order to confirm whether or not to join, the inspector checked the image and judged the good product and the defect. In artificial intelligence AOI, inspection device judges good product and defective through image information.
고도화Advancement
1단계: Big data의 정보량을 확보하는 단계. 결함 판정의 신뢰성 확보 및 정보 고도화 단계Step 1: Securing information of Big data. Securing reliability of defect judgment and information advancement step
2단계: 방대한 정보가 생성 되어지면, 머신 러닝을 통하여 결함 판정이 이루어지도록 고도화한다. Step 2: If vast amount of information is generated, advance the machine learning to make the defect determination.
본 발명은 또한 컴퓨터로 읽을 수 있는 기록매체에 컴퓨터가 읽을 수 있는 코드로서 구현하는 것이 가능하다. 컴퓨터가 읽을 수 있는 기록매체는 컴퓨터 시스템에 의하여 읽혀질 수 있는 데이터가 저장되는 모든 종류의 기록장치를 포함한다. 컴퓨터가 읽을 수 있는 기록매체의 예로는 ROM, RAM, CD-ROM, 자기 테이프, 플로피디스크, 광데이터 저장장치 등이 있다. 또한 컴퓨터가 읽을 수 있는 기록매체는 네트워크로 연결된 컴퓨터 시스템에 분산되어 분산방식으로 컴퓨터가 읽을 수 있는 코드가 저장되고 실행될 수 있다.The present invention can also be embodied as computer-readable codes on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like. The computer-readable recording medium may also be distributed over a networked computer system so that computer readable code can be stored and executed in a distributed manner.
이제까지 본 발명에 대하여 그 바람직한 실시예들을 중심으로 살펴보았다. 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자는 본 발명이 본 발명의 본질적인 특성에서 벗어나지 않는 범위에서 변형된 형태로 구현될 수 있음을 이해할 수 있을 것이다. 그러므로 개시된 실시예들은 한정적인 관점이 아니라 설명적인 관점에서 고려되어야 한다. 본 발명의 범위는 전술한 설명이 아니라 특허청구범위에 나타나 있으며, 그와 동등한 범위 내에 있는 모든 차이점은 본 발명에 포함된 것으로 해석되어야 할 것이다.The present invention has been described with reference to the preferred embodiments. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is defined by the appended claims rather than by the foregoing description, and all differences within the scope of equivalents thereof should be construed as being included in the present invention.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI712991B (en) * | 2019-10-08 | 2020-12-11 | 智泰科技股份有限公司 | Defect detection method and device with both automatic optical detection and artificial intelligence detection functions |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR200375821Y1 (en) | 2004-11-18 | 2005-03-10 | 쎄미모토 주식회사 | Automatic marking apparatus for print circuit board |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR200375821Y1 (en) | 2004-11-18 | 2005-03-10 | 쎄미모토 주식회사 | Automatic marking apparatus for print circuit board |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI712991B (en) * | 2019-10-08 | 2020-12-11 | 智泰科技股份有限公司 | Defect detection method and device with both automatic optical detection and artificial intelligence detection functions |
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