• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.302-302
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• 2000

This paper presents the measurement and correction of PCB alignment errors for PCB-manufacturing machines. The conventional PCB-manufacturing machine doesn't have enough accuracy to accommodate the demand for high-resolution circuit pattern and high-density mounting capacity of electronic chips. It is because of alignment errors of PCB loaded to the PCB-manufacturing machine. Therefore, this study focuses on the development of the system which is able to measure and correct alignment errors whit high-accuracy. An automatic optical inspection part measures the PCB alignment error using two cameras, and the high-accuracy 3-axis stage makes correct of these error. The operating system is run in the environment of Window 98 (or NT). Finally we implemented this system to PCB screen printer and PCB exposure system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.295-295
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• 2000

In this paper, the measurement algorithm of PCB alignment error is developed using image processing. The X-Y-$\theta$ table correcting PCB alignment error is driven by drive module based on microprocessor according to calculation results obtained through image processing procedure. In order to recognize the X-Y-$\theta$ position errors, two fiducial points are marked on PCB and two cameras of ultraviolet rays types are used for detection of the points to capture exactly fiducial points under disturbance of illumination change. Through application for a practical screen printer, the precision control using the developed position control system can be realized about 2.5${\mu}{\textrm}{m}$ in table moving range and 8${\mu}{\textrm}{m}$ in camera processing precision.

• 반도체디스플레이기술학회지
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• 제12권1호
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2335-2338
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• 2003

This paper presents a real-time PCB (Printed Circuit Board) vision inspection system. This system can detect the OPEN and SHORT of the PCB which of the line width is 150$\mu\textrm{m}$. Our PCB inspection system is based on the referential method. Since the size of the captured PCB image is very large, the image is divided into 512${\times}$512 images to apply the accurate alignment efficiently. To correct the misalignment between the reference image and the inspection image, pattern matching is performed. In order to implement the proposed algorithm in real-time, we use the SIMD instruction and the double buffering structures. Our experiential results show the effectiveness of the developed inspection algorithm.

• 제어로봇시스템학회논문지
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• 제16권7호
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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.

• 한국지능시스템학회논문지
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• 제25권3호
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• pp.260-265
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• 2015

본 논문은 에지 특징을 이용한 얼라인 시스템을 제안한다. 얼라인 시스템은 PCB나 LCD 패널 등에 새겨진 기준 마크(fiducial mark)를 통하여 위치 정보를 획득한다. 그러므로 획득한 영상에서 정확하게 기준 마크를 찾는 것이 얼라인 시스템 성능의 지표가 된다. 에지 특성은 위치, 길이, 형태 등의 기하학적 특징을 지니며 이런 특징은 기준 마크를 찾기 위해 적절하고, 밝기의 변화, 크기 및 각도의 변화에 강한 특성을 지닌다. X, Y 축 이동 및 회전 할 수 있는 장비와 소프트웨어을 이용한 얼라인먼트 실험을 통하여 제안한 시스템의 성능을 검증하였다.

• 한국정밀공학회지
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• 제27권2호
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• pp.70-77
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• 2010

This study proposed an auto focusing method for a multi-focus image in assembling lens modules in digital camera phones. A camera module in a camera phone is composed of a lens barrel, an IR glass, a lens mount, a PCB board and aspheric lenses. Alignment among the components is one of the important factors in product quality. Auto-focus is essential to adjust image quality of an IR glass in a lens holder, but there are two focal points in the captured image due to thickness of IR glass. So, sharpness, probability and a scale factor are defined to find desired focus from a multi-focus image. The sharpness is defined as clarity of an image. Probability and a scale factors are calculated using pattern matching with a registered image. The presented algorithm was applied to a lens assembly machine which has 5 axes, two vacuum chucks and an inspection system. The desired focus can be determined on the local maximum of the sharpness, the probability and the scale factor in the experiment.

• 방사선산업학회지
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• 제8권3호
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• pp.147-153
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• 2014

This paper describes a large area CMOS image sensor module Implementation using the precision align inspection program. This work is needed because wafer cutting system does not always have high precision. The program check more than 8 point of sensor edges and align sensors with moving table. The size of a $2{\times}1$ butted CMOS image sensor module which except for the size of PCB is $170mm{\times}170mm$. And the pixel size is $55{\mu}m{\times}55{\mu}m$ and the number of pixels is $3,072{\times}3,072$. The gap between the two CMOS image sensor module was arranged in less than one pixel size.