• Proceedings of the Optical Society of Korea Conference
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• 2006.07a
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• pp.469-471
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• 2006

This paper describes a efficient insepction method of PCB PADs for AFVI. The methods for PCB inspection have been tried to detect the defects in PCB PADs, but their low detection rate results from pattern variations that are originating from etching, printing and handling processes. The adaptive inspection method has been newly proposed to extract minute defects based on dynamic segments and filters. The vertexes are extracted from CAM master images of PCB and then a lot of segments are constructed in master data. The proposed method moves these segments to optimal directions of a PAD contour and so adaptively matches segments to PAD contours of inspected images, irrespectively of various pattern variations. It makes a fast, accurate and reliable inspection of PCB patterns. Experimental results show that proposed methods are found to be effective for flexible defects detection.

• Journal of the Semiconductor & Display Technology
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• v.10 no.1
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• pp.113-118
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• 2011

This paper proposes computer visual inspection algorithms for PCB defects which are found in a manufacturing process. The proposed method can detect open circuit and short circuit on bare PCB without using any reference images. It performs adaptive threshold processing for the ROI (Region of Interest) of a target image, median filtering to remove noises, and then analyzes connected components of the binary image. In this paper, the connected components of circuit pattern are defined as 6 types. The proposed method classifies the connected components of the target image into 6 types, and determines an unclassified component as a defect of the circuit. The analysis of the original target image detects open circuits, while the analysis of the complement image finds short circuits. The machine vision inspection system is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiment results show that the proposed algorithms are quite successful.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.273-275
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• 2005

Active thermography is being used since several years for remote non-destructive testing. It provides thermal images for remote detection and imaging of damages. Also, it is based on propagation and reflection of thermal waves which are launched from the surface into the inspected component by absorption of modulated radiation. For energy deposition, it use external heat sources (e.g., halogen lamp or convective heating) or internal heat generation (e.g., microwaves, eddy current, or elastic wave). Among the external heat sources, the ultrasound is generally used for energy deposition because of defect selective heating up. The heat source generating a thermal wave is provided by the defect itself due to the attenuation of amplitude modulated ultrasound. A defect causes locally enhanced losses and consequently selective heating up. Therefore amplitude modulation of the injected ultrasonic wave turns a defect into a thermal wave transmitter whose signal is detected at the surface by thermal infrared camera. This way ultrasound thermography(UT) allows for selective defect detection which enhances the probability of defect detection in the presence of complicated intact structures. In this paper the applicability of UT for fast defect detection is described. Examples are presented showing the detection of defects in PCB material. Measurements were performed on various kinds of typical defects in PCB materials (both Cu metal and non-metal epoxy). The obtained thermal image reveals area of defect in row of thick epoxy material and PCB.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.988-999
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• 2021

AOI (Automatic Optical Inspection) of PCB (Printed Circuit Board) is a very important step to guarantee the product performance. The process of registering components called teaching mode is first perform, and AOI is then carried out in a testing mode that checks defects, such as recognizing and comparing the component mounted on the PCB to the stored components. Since most of registration of the components on the PCB is done manually, it takes a lot of time and there are many problems caused by mistakes or misjudgement. In this paper, A components classifier is proposed using YOLO (You Only Look Once) v2's object detection model that can automatically register components in teaching modes to reduce dramatically time and mistakes. The network of YOLO is modified to classify small objects, and the number of anchor boxes was increased from 9 to 15 to classify various types and sizes. Experimental results show that the proposed method has a good performance with 99.86% accuracy.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.397-408
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• 2009

Image training is a very important yet difficult state for automated visual inspection using computers. Because the size of parts for the recently produced PCB (Printed Circuit Board) becomes smaller and circuit patterns gradually become more complex, a difficult and complex training process is becoming a big problem within an industry where development cycle for new products is short and various products must be inspected. This research produced a reference image by using CAD (Gerber) file which becomes a standard for PCB automatic visual inspection. Reference image from a Gerber file guarantees PCB patterns with no defects. Through system implementation and experimentation, Gerber file is used in order to propose a plan which allows an easy training process for PCB automatic visual inspection system.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.3 s.180
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• pp.102-109
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• 2006

Several methods for PCB pattern inspection have been tried to detect fine detects in pad contours, but their low detection accuracy results from pattern variations originating from etching, printing and handling processes. The adaptive inspection algorithm has been newly proposed to extract minute defects based on movable segments. With gerber master images of PCB, vertex extractions of a pad boundary are made and then a lot of segments are constructed in master data. The pad boundary is composed of segment units. The proposed method moves these segments to optimal directions of a pad boundary and so adaptively matches segments to pad contours of inspected images, irrespectively of various pattern variations. It makes a fast, accurate and reliable inspection of PCB patterns. Its performances are also evaluated with several images.

• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.182-192
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• 2017

Recently, there is an increasing interest in analysis of big data that is coming from manufacturing industry. In this paper, we use PCB (Printed Circuit Board) manufacturing data to provide manufacturers with information on areas with high PCB defect rates, and to visualize them to facilitate production and quality control. We use the K-means and DBSCAN clustering algorithms to derive the high fraction of PCB defects, and compare which of the two algorithms provides more accurate results. Finally, we develop a system of MVC structure to visualize the information about bad clusters obtained through clustering, and visualize the defected areas on actual PCB images.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.68-71
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• 2006

Active thermography has been used for several years in the field of remote non-destructive testing. It provides thermal images for remote detection and imaging of damages. Also, it is based on propagation and reflection of thermal waves which are launched from the surface into the inspected component by absorption of modulated radiation. For energy deposition, it use external heat sources (e.g., halogen lamp or convective heating) or internal heat generation (e.g., microwaves, eddy current, or elastic wave). Among the external heat sources, the ultrasound is generally used for energy deposition because of defect selective heating up. The heat source generating a thermal wave is provided by the defect itself due to the attenuation of amplitude modulated ultrasound. A defect causes locally enhanced losses and consequently selective heating up. Therefore amplitude modulation of the injected ultrasonic wave turns a defect into a thermal wave transmitter whose signal is detected at the surface by thermal infrared camera. This way ultrasound thermography(UT) allows for selective defect detection which enhances the probability of defect detection in the presence of complicated intact structures. In this paper the applicability of UT for fast defect detection is described. Examples are presented showing the detection of defects in PCB material. Measurements are performed on various kinds of typical defects in PCB materials (both Cu metal and non-metal epoxy). The obtained thermal image reveals area of defect in row of thick epoxy material and PCB.

• The Journal of Information Technology
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• v.3 no.4
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• pp.13-21
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• 2000

The thesis is for the analysis of the current trend adapted for the method of the test information exchange in the environment of the internal circuit in case of PCB design, The design process is most powerful for improving test characteristics among the entire process of PCB. The PCB is more and more difficult to test as the processes proceed because the nodes become more and more complicated. The data exchange for improving PCB test performance should be easy in order to provide more reliable products in the shorter period. The design technology oriented for the test makes the PCB and its components tested reliably and quickly so that it can effectively improve the quality of the PCB and largely reduce the time and cost of the test development, In addition it can make the substantial standardization In improve the speed of the repeated test and treatment so that the Period of development can be shorter. Also, it helps to effectively detect the potential defects of the products, so that highly reliable PCB can be produced.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.5
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• pp.465-472
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• 2016

This study materialized an FPGA-based system to extract PCB patterns. The Printed Circuit Boards that are produced these days are becoming more detailed and complex. Therefore, the importance of a vision system to extract defects of detailed patterns is increasing. This study produced an FPGA-based system that has high speed handling for vision automation of the PCB production process. A vision library that is used to extract defect patterns was also materialized in IPs to optimize the system. The IPs materialized are Camera Link IP, pattern matching IP, VGA IP, edge extraction IP, and memory IP.