Tape substrate pattern of ultra-fine pitch circuit less than 10 micrometers in pattern width, is required to be inspected
through high resolution optics. In the process of picking out defects at the level of the critical dimension through image
processing, however, trivial blemishes formed by dust or micro particles may be detected simultaneously. This leads to
unnecessary work on the part of operators reviewing and verifying the additional detected points. To maximize the
efficiency of the inspection process, we need to identify and classify the defect candidates whether it is a real pattern
defect or simply a trivial blemish by dust. Since a real defect arising from under or over etching bears inherent features in
shape and brightness, it can thus be discriminated from other trivial blemishes. In this article, we propose an image
feature based defect classification method, where proper measures were obtained from a series of image analysis with
FFT. Based on the data collected from experiments, we devised a statistic model for classification.
KEYWORDS: Object recognition, 3D image processing, Simulation of CCA and DLA aggregates, Optical engineering, X-ray imaging, Wavelets, 3D modeling, X-rays, Feature extraction, Distortion
This paper presents a distortion-tolerant 3-D volume object recognition technique. Volumetric information on 3-D objects is reconstructed by x-ray imaging. We introduce 3-D feature extraction, volume matching, and statistical significance testing for the 3-D object recognition. The 3D Gabor-based wavelets extract salient features from 3-D volume objects and represent them in the 3-D spatial-frequency domain. Gabor coefficients constitute feature vectors that are invariant to translation, rotation, and distortion. Distortion-tolerant volume matching is performed by a modified 3-D dynamic link association (DLA). The DLA is composed of two stages: rigid motion of a 3-D graph, and elastic deformation of the graph. Our 3-D DLA presents a simple and straightforward solution for a 3-D volume matching task. Finally, significance testing decides the class of input objects in a statistical manner. Experiment and simulation results are presented for five classes of volume objects. We test three classes of synthetic data (pyramid, hemisphere, and cone) and two classes of experimental data (short screw and long screw). The recognition performance is analyzed in terms of the mean absolute error between references and input volume objects. We also confirm the robustness of the recognition algorithm by varying system parameters.
Most engineered products/processes/systems have continually evolved to enhance their performance. However, they still need to further evolve towards having such characteristics as high precision, intelligence and autonomy. In this paper, an opto-mechatronic systems technology is introduced as one of the enabling techniques, for such evolution. It is an integrated multidisciplinary technology combining optical, electrical, mechanical and computer engineering fields, thus creating new value and functions as a result of integration. Such integrated technology is shown to be derivable from the key technologies of the optical and mechatronic engineering fields and to a variety of fundamental functionalities required to produce the systems. The obtainable synergy and the future perspectives of the technology are discussed in detail.
The visual information obtained from CCD camera is vulnerable to external illumination and the surface reflection properties of object images. Thus, the success of extracting aimed features from images depends mostly on the appropriate design of illumination. This paper presents a visual inspection system that is equipped with a flexible illumination and an auto-focusing unit. The proposed illumination system consists of a three-layered LED illumination device and the
controllable diffusers. Each layer is composed of LEDs arranged in a ring type, and a controllable diffuser unit is located in front of each layer. The diffuser plays a role of diffusing lights emitted from the LEDs so that the characteristics of illumination is made varied. This combined configuration of the LED light sources and the adjustable diffuser creates the various lighting conditions. In addition to this flexible illumination function, the vision system is equipped with an auto-focusing unit composed of a pattern projector and a working distance adjustable zoom camera. For the auto-focusing, hill climbing algorithm is used here based on a reliable focus measure that is defined as the variance of high frequency terms in an image. Through a series of experiments, the influence of the illumination system on image quality is analyzed for various objects that have different reflective properties and shapes. As an example study, the electrical parts inspection is investigated. In this study, several types of chips with different sizes and heights are segmented and focused automatically, and then analyzed for part inspection. The results obtained from a series of experiments confirm the usefulness of the proposed system and the effectiveness of the illumination and focusing method.
In this research, we propose a 3D volume reconstruction method using x-ray images and present a series of calibration methods to implement it in an x-ray imaging system. In our previous work, we have proposed an advanced 3D reconstruction algorithm based on algebraic reconstruction technique(ART), called a uniform and simultaneous ART(USART). In practice, however, there are two main issues to implement it in a realized x-ray imaging system. The first one is huge computation time and memory required in achieving 3D volume, which is a common limitation in ART methods. The second issue is the problem on system calibration for determining the geometry of the x-ray imaging conditions which are necessary information in ART method. This work addresses solving out these problems : We propose a fast computing model of USART, where spherical voxel elements are employed in computation to reduce computation time and memory. And a calibration method is proposed here to identify the x-ray imaging geometry based on a cone beam projection model. For this purpose, a reference grid pattern is locally displaced to predetermined positions, and then their relative coordinates are determined by analyzing the image variations according to the displacements of the grid pattern. The validity of the proposed 3D reconstruction method is investigated from a series of experiments.
Inspection and shape measurement of three-dimensional objects are widely needed in the fields of quality monitoring and reverse engineering. X-ray computed tomography could be a good solution since the method can acquire three dimensional volume information of a product from a series of acquired cross-sectional images. To reconstruct a cross-section in computed tomography, a number of data are required, projected from all but uniformly spaced view angles. In many applications of industrial field, however, it may not be possible to acquire such projection data obtained at all angles due to the size of objects or obstructed situation by other structures at some angles. In such a limited condition, analytical solution to reconstruct a cross-section is not available in general, and an iterative numerical method such as algebraic reconstruct technique (ART) and its modified algorithms, such as uniform and simultaneous ART methods, are used. In those iterative methods, the intensities of the image pixels in the reconstructed image are estimated and updated independently, thus the reconstructed image looks like a mosaic, of which the resolution is restricted to the number of image elements, pixels. In this paper, a new image reconstruction method is proposed based on a radial basis f function (RBF) neural network. In this method, a cross-section image is represented by RBF network, and is reconstructed through the learning process of the network. To achieve this, a learning method of the network is proposed here based on the projection of the image instead of the reference image itself. The algorithm is tested by a series of simulation studies on some of modeled images, and the performance of the proposed method is evaluated in terms of convergence and accuracy.
An x-ray vision can be a unique method to monitor in real time and analyze the motion of mechanical parts which are invisible from outside. Our problem is to identify the pose, i.e. the position and orientation of an object from x-ray projection images. It is assumed here that the x-ray imaging conditions that include the relative coordinates of the x-ray source and the image plane are predetermined and the object geometry is known. In this situation, an x-ray image of an object at a given pose can be estimated computationally by using a priori known x-ray projection image model. It is based on the assumption that a pose of an object can be determined uniquely to a given x-ray projection image. Thus, once we have the numerical model of x-ray imaging process, x-ray image of the known object at any pose could be estimated. Then, among these estimated images, the best matched image could be searched and found. When adequate features in the images are available instead of the image itself, the problem becomes easier and simpler. In this work, for simplicity, only polyhedral objects are considered whose image features consist of corner points and edge lines in their projection images. Based on the corner points and lines found in the images, the best-matched pose of a polyhedral object can be determined. To achieve this, we propose an adequate and efficient image processing algorithm to extract the features of objects in x-ray images. The performance of the algorithms is discussed in detail including the limitations of the method. To evaluate the performance of the proposed method a series of simulation studies is carried out for various imaging conditions.
12 Inspection and shape measurement of 3D objects are widely needed in industries for quality monitoring and control. A number of visual or optical technologies have been successfully applied to measure 3D surfaces. However, those conventional visual or optical methods have inherent shortcomings, which are occlusion problem and variant surface reflection problem. X-ray vision system can be a good solution to these conventional problems, since we can extract the volume information including both the surface geometry and the inner structure of the object. In the x-ray system, the surface condition of an object, whether it is lambertian or specular, does not affect the inherent characteristics of its x-ray images. In this paper, we propose a 3D x-ray imaging method to reconstruct a 3D structure of an object out of 2D x-ray image sets.
The ball grid array (BGA) chip is widely used in high density printed circuit board (PCB). However, inspection of defects in the solder joints is difficult by visual or a normal x-ray imaging method, because unlike conventional packages, solder joints of the BGA are located underneath its own package and ball type leads. Therefore, x-ray digital tomosynthesis (DT), which form a cross-sectional image of 3D objects, is needed to image and inspect the solder joints of BGA. In this paper, we propose a series of algorithms for inspecting the solder joints of BGA by using x-ray cross-sectional images that are acquired from the developed DT system. BGA solder joints are examined to check the alignment between the chip and pad on a PCB, bridge, adequate solder volume. The volume of the solder joint is represented by a gray level in the x-ray images: thus solder joints can be examined by use of the gray-level profiles of each joint. To inspect and classify various defects, pattern classification method using a learning vector quantization neural network and a look up table is proposed. The clusters into which a gray-level profile is classified are generated by the learning process of the network by using a number of sampled gray-level profiles. A series of these developed algorithms for inspecting and classifying defects were tested on a number of BGA solder joints. The experimental results show that the proposed method yields satisfactory solutions for inspection based on x-ray cross-sectional images.
X-ray laminography and DT (digital tomosynthesis) are promising technologies to form a cross-section image of 3D objects and can be a good solution for inspection interior defects of industrial products. It has been known that digital tomosynthesis method has several advantages over laminography method in that it can overcome the problems such as blurring effect or artifact. The DT system consists of a scanning x-ray tube, an image intensifier as an x-ray image detector, and a CCD camera. To acquire an x-ray image of an arbitrary plane of objects, a set of images (8 images or more) should be synthesized by averaging or minimally calculating point by point. The images, however are distorted according to the configurations of the image intensifier and the x-ray source position. To get a clear and accurate synthesized image, the corresponding points in the distorted images should be accurately determined, and therefore, precise calibration of the DT system is needed to map the corresponding points correctly. In this work, a series of calibration methods for the DT system are presented including the correction of the center offset between the x-ray and the image intensifer, the x-ray steering calibration, and the correction of the distortion of the image. The calibration models are implemented to the DT system and the experiment results are presented and discussed in detail.
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