• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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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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• 2000.10a
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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.

• Journal of the Semiconductor & Display Technology
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• v.12 no.1
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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.

• Proceedings of the IEEK Conference
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• 2003.07e
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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.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.260-265
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• 2015

This paper proposes a alignment system using edge feature. An alignment system obtains the position and orientation of printed circuit board(PCB) or liquid crystal display(LCD) panel through the fiducial marks. Thus, it is the indicator of the system performance how accurate we detect the positions of the fiducial marks in the target image. Edges have the geometrical characteristics such as positions, lengths, and shapes. These features are suitable for finding the marks and have the advantages of lighting variations, model occlusion, as well as variations in scale and angle. The performance of the proposed system is validated through the alignment experiment using an display panel alignment system included X, Y axis, and rotatable stage.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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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.

• Journal of Radiation Industry
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• v.8 no.3
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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.