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This study aims to present an optical alignment and compensation control of die bonder for chips containing through-silicon vias and develop three-dimensional integrated circuit stacked packaging for compact size and multifunction.

The machine vision, optical alignment method and sub-pixel technology in dynamic imaging condition are used. Through a comparison of reference image, the chip alignment calibration can improve machine accuracy and stability.

According to the experimental data and preliminary results of the analysis, accuracy can be achieved within the desired range, and the accuracy is much better than traditional die bonder equipment. The results help further research in die bonder for chips containing through-silicon vias.

In subsequent testing of the chip, the machine can simultaneously test multiple chips to save test time and increase productivity.

The purpose of this paper is to propose an automatic pattern matching template generating method for the automatic optical inspection system in TFT LCD assembly and positioning process, to improve the conventional image technology. Besides, focusing on integrating the image system with the existing control system, the double aligner mark searching time is decreased to reduce the working time of the integrated system.

The improved pattern matching method of genetic algorithm was adopted, including setting for template image selecting, encoding, calculating fitness function, pattern matching, template generating and genetic algorithm steps. The predetermined pixels were selected from the target template based on the minimum difference to the block image to be tested by utilizing the genetic algorithm, and the other pixels which have not been selected were neglected.

The selected pixels were encoded for recording by sequence mode, and then the target template and the image to be tested were compared based on the calculated fitness function. This method has the advantages of using the fitness function to reduce the searching time, with the help of genetic algorithm to find the optimal target template, and saving memory space by recording target template based on the sequence mode.

The genetic algorithm used in this study is a kind of optimal tool free from gradient data. As long as the fitness function and after continuous iteration are determined, the optimal solution can be found out, and then the optimal target template can be generated.

This system uses fitness function to reduce the pattern matching time. Plural pixels are preset inside the target template, and its fitness function value is calculated. When the target template is compared with the image to be tested, only the fitness function value (also the difference of the plural pixels) is calculated and compared.

The remaining pixels are neglected, so that the searching time can be reduced greatly. The sequence mode is used to save the required memory space for recording target template. Since sequence mode is adopted to record the information of selected pixels, lots of required memory space for recording target template information will be saved.

The purpose of this paper is to apply an on‐line automatic inspection and measurement of surface defect of thin‐film transistor liquid‐crystal display (TFT‐LCD) panels in the polyimide coating process with a modified template matching method and back propagation neural network classification method.

By using the technique of searching, analyzing, and recognizing image processing methods, the target pattern image of TFT‐LCD cell defects can be obtained.

With template match and neural network classification in the database of the system, the program judges the kinds of the target defects characteristics, finds out the central position of cell defect, and analyzes cell defects.

The recognition speed becomes faster and the system becomes more flexible in comparison to the previous system. The proposed method and strategy, using unsophisticated and economical equipment, is also verified. The proposed method provides highly accurate results with a low‐error rate.

In terms of sample training, the principles of artificial neural network were used to train the sample detection rate. In sample analysis, character weight was implemented to filter the noise so as to enhance discrimination and reduce detection.

The paper describes how pre‐inspection image processing was utilized in collaboration with the system to excel the inspection efficiency of present machines as well as for reducing system misjudgment. In addition, the measure for improving cell defect inspection can be applied to production line with multi‐defects to inspect and improve six defects simultaneously, which improves the system stability greatly.

The purpose of this paper is to present an automatic inspection and control method for a reagent rapid test strip production system, with image processing techniques.

Fluorescence, color arrangement and combination matching with the database were used to identify the responses of biochemicals. The position accuracy and insufflation consistency between the control line and test line on a reagent rapid test strip will be analyzed from the image after series processing.

The system can identify failed products and regulate production conditions to insure that the quality standard is maintained. The idea edges of the control line and test line are the boundary at which a significant change occurs in the surface reflectance and illumination of the viewer. But the change of the real boundary of the test line may be insufficient for identification.

As the illumination of biological reagent images cannot be measured precisely in the production process, and the intensity of the background light source is difficult to control, there are always significant errors in the production process. If the environment at sampling could be precisely controlled, the accuracy of the system could be enhanced.

This study developed software architecture for a biological reagent production and inspection system. Future studies will focus on the implementation of testing, and improvement of the system, so that it can be applied to medical systems for the benefit of all patients.

The purpose of this paper is to propose an automatic optical inspection system for measuring the surface profile of a microlens array.

The system set‐up was constructed according to the principle of the Fizeau interferometer. After capturing the ring interference fringe images of the microlens with a camera, the diameter, profile information and optical properties were analyzed through a microlens surface profile algorithm using innovative image pre‐processing with a precision of less than 0.09 micron.

By integrating with the genetic algorithm, the XY‐Table shortest moving path of the system is calculated to achieve the purpose of high‐speed inspection and automatic microlens array surface profile measurement.

The measurement results of this system were also compared with other systems, including the atomic force microscope and stylus profiler, to verify the measurement precision and accuracy of this system.

This study aimed to developed a defect detection system for a segment-type display module panel.

The system included a data acquisition card, a video camera, a computer and a display module on a testing table. The video camera captured the display pattern of the display module and transferred it to the computer through the data acquisition card. The dynamic multi-thresholding method and analysis as well as back propagation neural network classification was used to classify the detected defects.

The threshold values for the brightness at different positions in the display module image were obtained using the neural network and then stored in the look-up table, using two to six matrixes.

The recognition speed was faster and the system was more flexible in comparison to the previous system. The proposed method, using unsophisticated and economical equipment, was also verified as providing highly accurate results with a low error rate.

The purpose of this research is to develop an automatic optical inspection system for thin film transistor (TFT) liquid crystal display (LCD).

A new algorithm that accounts for the closing, opening, etching, dilating, and genetic method is used. It helps to calculate the location and rotation angle for transistor patterns precisely and quickly. The system can adjust inspection platform parameters according to viewed performance. The parameter adaptation occurs in parallel with running the genetic algorithm and imaging processing methods. The proposed method is compared with the algorithms that use artificial parameter sets.

This system ensures high quality in an LCD production line. This multipurpose image‐based measurement method uses unsophisticated and economical equipment, and it also detects defects in the micro‐fabrication process.

The experiment's results show that the proposed method offers advantages over other competing methods.

The purpose of this paper is to present an automatic inspection method of colors and textures classification of paper and cloth objects.

In this system, the color image is transformed from RGB model to other suitable color model with one of the components being chosen as the gray‐level image for extracting textures. The gray‐level image is decomposed into four child images using wavelet transformation. Two child images capable of detecting variations along columns and rows are used to generate 0° and 90° co‐occurrence matrices, respectively. Some of the distinguishable texture features are derived from the two co‐occurrence matrixes. Finally, the test image is classified using neural networks. Nine color papers and eight color cloths are used to test the developed classification method.

The results show that recognition rate higher than 97.86 percent can be achieved if color and texture features are both used as the inputs to the networks.

The paper presents a new approach for testing materials. The multipurpose measurement application with unsophisticated and economical equipment can be confirmed in online inspection of papers and cloth manufacturing.