• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.353-359
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• 1988

In this paper, the design and implementation of a multi-processor based die bonder machine for the semiconductor will be described. This is a final research results carried out for two years from June, 1986 to July, 1988. The mechanical system consists of three subsystems such as bonding head module, wafer feeding module, and lead frame feeding module. The overall control system consists of the following three subsystems each of which employs a 16 bit microprocessor MC 68000 : (i) supervisory control system, (ii) visual recognition / inspection system and (iii) the display system. Specifically, the supervisory control system supervises the whole sequence of die bonder machine, performs a self-diagnostics while it controls the bonding head module according to the prespecified bonding cycle. The vision system recognizes the die to inspect the die quality and deviation / orientation of a die with respect to a reference position, while it controls the wafer feeding module. Finally, the display system performs a character display, image display ans various error messages to communicate with operator. Lead frame feeding module is controlled by this subsystem. It is reported that the proposed control system were applied to an engineering sample and tested in real-time, and the results are sucessful as an engineering sample phase.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.709-716
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• 2004

This paper describes an intelligent real-time monitoring system of a semiconductor processing equipment, which determines normal or not for a wafer in processing, using multiple time-series pattern recognition. The proposed system consists of three phases, initialization, learning and real-time prediction. The initialization phase sets the weights and tile effective steps for all parameters of a monitoring equipment. The learning phase clusters time series patterns, which are producted and fathered for processing wafers by the equipment, using LBG algorithm. Each pattern has an ACI which is measured by a tester at the end of a process The real-time prediction phase corresponds a time series entered by real-time with the clustered patterns using Dynamic Time Warping, and finds the best matched pattern. Then it calculates a predicted ACI from a combination of the ACI, the difference and the weights. Finally it determines Spec in or out for the wafer. The proposed system is tested on the data acquired from etching device. The results show that the error between the estimated ACI and the actual measurement ACI is remarkably reduced according to the number of learning increases.

• Journal of fish pathology
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• v.27 no.2
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• pp.99-105
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• 2014

Biosensors consist of biochemical recognition agents like antibodies immobilized on the surfaces of transducers that change the recognition into a measurable electronic signal. Here we report a piezoelectric immunosensor made to detect Vibrio vulnificus. A 9MHz AT-cut piezoelectric wafer attached with two gold electrodes of 5mm diameter was used as the transducer of the QCM biosensor with a reproducibility of ${\pm}0.1Hz$ in frequency response. We have tried different approaches to immobilize antibody on the sensor chip. Concerning the orientation of antibody for the best antigen binding capacity, the antibody was immobilized by specific binding to protein G or by cross-linking through hydrazine. In addition, protein G was cross-linked on glutaraldehyde activated immine layer (PEI) or EDC/NHS activated sulfide monolayer (MPA). PEI was found to be more effective to immobilize protein G following glutaraldehyde activation than MPA. However, hydrazine chip showed a better capability to immobilize more IgG than protein G chip and a higher sensitivity. The sensor system was able to detect V. vulnificus in dose dependent manner and was able to detect bacterial cells within 5 minutes by monitoring frequency shifts in real time. The detection limit can be improved by preincubation to enrich the bacterial cell number.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.

• Journal of Conservation Science
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• v.37 no.3
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• pp.255-269
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• 2021

A vast amount of materials-such as prints, woodblock prints, manuscripts, old novels, and letters-written in old Korean and using old grammar and/or obsolete characters, are collected in many institutions, including the Jangseogak at the Academy of Korean Studies. Digitization of these texts has required a prolonged manual inputting process. Individual researchers, who majored in old Korean, have read and typed the characters into electronic documents, which depends upon individual skill, effort, and approach, and is particularly limiting because none can be significantly increased. To date, only a small proportion of the old Korean document collections, currently kept in storage, have been digitized and made available to the public. Even the electronic formats of the texts prove difficult to displaying correctly, due to the incompatibility between the old Korean characters and the character set on today's electronic devices. To improve the techniques and efficiency of digitizing old Korean texts, it is necessary to develop optical character recognition (OCR), which will analyze images of old Korean documents, as well as input, display, and storage methods.

• Vacuum Magazine
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• v.2 no.1
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• pp.10-16
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• 2015

With the recognition of nanotechnology as one of the future strategic technologies, the R&D efforts have been performed under exclusive supports of governments and private sectors. At present, nanotechnology is at the focus of research and public attention in almost every advanced country including USA, Japan, and many others in EU. Keeping tracks of such technical trends, center for nanoscale mechatronics and manufacturing (CNMM) was established in 2002 as a part of national nanotechnology promotion policy led by ministry of science and technology (MOST) in Korea. It will hold widespread potential applications in electronics, optical electronics, biotechnology, micro systems, etc, with the promises of commercial visibility and competitiveness. In this paper, wafer scale multilayer nanoimprint lithography technology which is well-known the next generation lithography, roll-typed nanoimprint lithography (R-NIL), roll-typed liquid transfer imprint lithography (R-LTIL), the key technology for nanomanufacturing and nanoscale measurement technology will be introduced. Additionally, its applications and some achievements such as solar cell, biosensor, hard disk drive, and MOSFET, etc by means of the developed multilayer nanoimprint lithography system are introduced.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.51-57
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• 1999

In this paper, an adaptive thresholding of the nonuniformly contrasted images is proposed through using the contrast pre-enhancement of the local regions and the bilinear interpolation between the local threshold values. The nonuniformly contrasted image is decomposed into 9${\times}$9 sized local regions, and the contrast is enhanced by intensifying the gray level difference of each low contrasted or blurred region. Optimal threshold values are obtained by iterative method from the gray level distribution of each contrast-enhanced local region. Discontinuities are reduced at the region of interest or at the characters by using bilinear interpolation between the neighboring threshold surfaces. Character recognition experiments are conducted using backpropagation neural network on the characters extracted from the nonuniformly contrasted document, PCB, and wafer images binarized through using the proposed thresholding and the conventional thresholding methods, and the results prove the relative effectiveness of the proposed scheme.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.55-67
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• 2022

Sensor data can provide fault diagnosis for equipment. However, the cause analysis for fault results of equipment is not often provided. In this study, we propose an explainable convolutional neural network framework for the sensor-based time series classification model. We used sensor-based time series dataset, acquired from vehicles equipped with sensors, and the Wafer dataset, acquired from manufacturing process. Moreover, we used Cycle Signal dataset, acquired from real world mechanical equipment, and for Data augmentation methods, scaling and jittering were used to train our deep learning models. In addition, our proposed classification models are convolutional neural network based models, FCN, 1D-CNN, and ResNet, to compare evaluations for each model. Our experimental results show that the ResNet provides promising results in the context of time series classification with accuracy and F1 Score reaching 95%, improved by 3% compared to the previous study. Furthermore, we propose XAI methods, Class Activation Map and Layer Visualization, to interpret the experiment result. XAI methods can visualize the time series interval that shows important factors for sensor data classification.