• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.444-452
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• 2008

This paper presents an overall image processing approach of defect inspection of patterned TFT-LCD panels for the real manufacturing process. A prototype of AOI(Automatic Optical Inspection) system which is composed of air floating stage and multi line scan cameras is developed. Adjacent pattern comparison algorithm is enhanced and used for pattern elimination to extract defects in the patterned image of TFT-LCD panels. New region merging algorithm which is based on border expansion is proposed to identify defects from the pattern eliminated defect image. Experimental results show that a developed AOI system has acceptable performance and the proposed algorithm reduces environmental effects and processing time effectively for applying to the real manufacturing process.

• IE interfaces
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• v.21 no.2
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• pp.151-160
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• 2008

Defect size distribution is a probability density function for the defects that occur on wafers or glasses during semiconductor/LCD fabrication. It is one of the most important information to estimate manufacturing yield using well-known statistical estimation methods. The defects are detected by automatic optical inspection (AOI) facilities. However, the data that is provided from AOI is not accurate due to resolution of AOI and its defect detection mechanism. It causes distortion of defect size distribution and results in wrong estimation of the manufacturing yield. In this paper, I suggest a size conversion method and a maximum likelihood estimator to overcome the vague defect size information of AOI. The methods are verified by the Monte Carlo simulation that is constructed as similar as real situation.

• The Optical Journal
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• s.154
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• pp.46-56
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• 2014

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.480-485
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• 2003

We Propose a Path planning method to reduce the Inspection time of AOI (automatic optical inspection) machines in SMT (surface mount technology) in-line system. Inspection windows of board should be clustered to consider the FOV (field-of-view) of camera. The number of clusters is desirable to be minimized in order to reduce the overall inspection time. We newly propose a genetic algorithm to minimize the number of clusters for a given board. Comparative simulation results are presented to verify the usefulness of proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.788-793
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• 2015

This paper proposes a path planning algorithm to reduce the inspection time of AOI (Automatic Optical Inspection) machines for SMT (Surface Mount Technology) in-line system. Since the field-of-view of the camera attached at the machine is much less than the entire inspection region of board, the inspection region should be clustered to many groups. The image acquisition time depends on the number of groups, and camera moving time depends on the sequence of visiting the groups. The acquired image is processed while the camera moves to the next position, but it may be delayed if the group includes many components to be inspected. The inspection delay has influence on the overall job time of the machine. In this paper, we newly considers the inspection delay time for path planning of the inspection machine. The unified approach using genetic algorithm is applied to generates the groups and visiting sequence simultaneously. The chromosome, crossover operator, and mutation operator is proposed to develop the genetic algorithm. The experimental results are presented to verify the usefulness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.109.2-109
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• 2001

In the PCB assembly lines, as components become more complex and smaller, the conventional inspection method using traditional ICT and function test show their limitations in application. The automatic optical inspection(AOI) gradually becomes the alternative in the PCB assembly line. In Particular, the PCB inspection machines need more reliable and flexible object recognition algorithms for high inspection accuracy. The conventional AOI machines use the algorithmic approaches such as template matching, Fourier analysis, edge analysis, geometric feature recognition or optical character recognition (OCR), which mostly require much of teaching time and expertise of human operators. To solve this problem, in this paper, a statistical learning based part recognition method is proposed. The performance of the ...

• 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.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2453-2455
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• 2003

We propose a path planning method to reduce the inspection time of AOI (automatic optical inspection) machines in SMT (surface mount technology) in-line system. Inspection windows of board should be clustered to consider the FOV (field-of-view) of camera. The number of clusters is desirable to be minimized in order to reduce the overall inspection time. We newly propose a genetic algorithm to minimize the number of clusters for a given board. Comparative simulation results are presented to verify the usefulness of proposed algorithm.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.269-274
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• 2015

According to the development of IT industry, prevalence of AOI equipment is spreading, and also requiring the high resolution of the camera used in the equipment. The weight of the camera is increased to obtain a high resolution, and thus increases the vibration displacement is a problem occurring in the picturing, camera motion control also becomes difficult. In this study, using a finite element analysis program NX/NASTRAN, the transient response of the camera was analysed which is subjected to an impact force due to inertia. The finite element analysis result is correlated with laser interferometer measurement. When AOI equipment is restructuring, the correlated finite element analysis model can be used to verify the authenticity of the new design.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.695-702
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• 2010

This paper presents an image alignment algorithm for application of AOI (Automatic Optical Inspection) based on SIFT. Since the correspondences result using SIFT descriptor have many wrong points for aligning, this paper modified and classified those points by five measures called the CCFMR (Cascade Classifier for False Matching Reduction) After reduced the false matching, rotation and translation are estimated by point selection method. Experimental results show that the proposed method has fewer fail matching in comparison to commercial software MIL 8.0, and specially, less than twice with the well-controlled environment’s data sets (such as AOI system). The rotation and translation accuracy is robust than MIL in the noise data sets, but the errors are higher than in a rotation variation data sets although that also meaningful result in the practical system. In addition to, the computational time consumed by the proposed method is four times shorter than that by MIL which increases linearly according to noise.