• Journal of Korea Society of Industrial Information Systems
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• v.5 no.4
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• pp.55-59
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• 2000

In start up process control, it may be necessary to use appropriate scheme in monitoring processes with individual observations. In these situation individual observations are periodically drawn from the process. In this paper, using modifying statistics with individual measurement, we suggest a simple technique which operating control chart for monitoring the process. And compare individual observation control procedures that are X, an exponentially weighted moving(EWM), adjusted EWM and adjusted MCEWM charts. And estimate the ARL to detection of shifts in the process mean and standard deviation using simulation.

• IE interfaces
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• v.16 no.spc
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• pp.45-48
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• 2003

Entity-relationship modeling for designing relational database is a very complicated thinking process that requires extensive knowledge and experiences. It is very likely that designers make mistakes in this process. In order to minimize the mistakes, a systematic method to guide the thinking process is needed. In this research, an entity-relationship modeling system is developed, which resolves the whole process of information modeling, data modeling, and functional dependency relationship analysis into small and simple decision-making steps. Therefore, it can reduce the possibility of making decision errors and improve the efficiency of the modeling process. It's functionality and efficiency is verified through some modeling examples. It is expected that the modeling system can be commercialized, if some functions are added, such as detection, warning, and correction of decision errors, and educational help.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.142.2-142.2
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• 2015

Plasma etch endpoint detection (EPD) of SiO2 and PR layer is demonstrated by plasma impedance monitoring in this work. Plasma etching process is the core process for making fine pattern devices in semiconductor fabrication, and the etching endpoint detection is one of the essential FDC (Fault Detection and Classification) for yield management and mass production. In general, Optical emission spectrocopy (OES) has been used to detect endpoint because OES can be a simple, non-invasive and real-time plasma monitoring tool. In OES, the trend of a few sensitive wavelengths is traced. However, in case of small-open area etch endpoint detection (ex. contact etch), it is at the boundary of the detection limit because of weak signal intensities of reaction reactants and products. Furthemore, the various materials covering the wafer such as photoresist (PR), dielectric materials, and metals make the analysis of OES signals complicated. In this study, full spectra of optical emission signals were collected and the data were analyzed by a data-mining approach, modified K-means cluster analysis. The K-means cluster analysis is modified suitably to analyze a thousand of wavelength variables from OES. This technique can improve the sensitivity of EPD for small area oxide layer etching processes: about 1.0 % oxide area. This technique is expected to be applied to various plasma monitoring applications including fault detections as well as EPD.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.919-921
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• 2015

The edge is widely utilized as a preconditioning process in order to simplify the images in several fields such as the object recognition and detection. The edge detection methods which are generally known include the methods of Sobel, Roberts and Laplacian. However, such current methods have an advantage that the implementation is simple but bring more less an insufficient result since they use the fixed weighting mask. Therefore, an algorithm using the modified morphology is proposed in order to supplement such problems of the current edge detection methods and obtain the excellent edge detection, and also a simulation using this algorithm is conducted to compare with such current methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.1-12
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• 2023

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1401-1406
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• 2014

Knowledge of the manufacturing process of semiconductor devices in order to obtain a copper pattern using chemical mechanical polishing (CMP) planarization using a Wafer polishing process is applied with a thickness of the copper measured in real time, which need to be precisely controlled by, where the acquisition the actual thickness of the sensor value with the calculated value in terms of error can occur in the process. Approximated the actual measurement values so as to obtain a method using a simple average, moving average, compared to the results using filters onggo Strom real-time measurements of the thickness of the units of the control system to reduce the variation in the implementation of the method described for the.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.126-126
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• 2014

Label-free biomolecular assay based localized surface plasmon resonance (LSPR) of noble metal nanoparticles enables simple and rapid detection with the use of simple equipment. Nanosized metal nanoparticles exhibit a strong absorption band when the incident light frequency is resonant with the collective oscillation of the electrons, which is known as the LSPR. Here we demonstrate localized surface plasmon resonance (LSPR) substrates such as plasmonic Au nanodisks fabricated by a nanoimprinting process and gold nanorod-immobilized surfaces and their applications to highly sensitive and/or label-free biosensing. To increase detection sensitivity various bioreceptors weree designed. A single chain variable fragment (scFv) was used as a receptor to bind C-reactive protein (CRP). The results of this effort showed that CRP in human serum could be quantitatively detected lower than 1 ng/ml. Aptamers, which were immobilized on gold nanorods, were used to detect mycotoxins. The specific binding of ochratoxin A (OTA) to the aptamer was monitored by the longitudinal wavelength shift of LSPR peak in the UV-Vis spectra resulting from the changes of local refractive index near the GNR surface induced by accumulation of OTA and G-quadruplex structure formation of the aptamer. According to our results, OTA could be quantitatively detected lower than 1 nM level. Additionally, aptamer-functionalized GNR substrate was quite robust and can be regenerated many times by rinsing at 70 OC to remove bound target. During seven times of washing steps, the developed OTA sensing system could be reusable. Moreover, the proposed biosensor exhibited selectivity over other mycotoxins with an excellent recovery for detection in grinded corn samples, suggesting that the proposed LSPR based aptasensor plays an important role in label-free detection of mycotoxins.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.6 s.360
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• pp.24-30
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• 2007

In this paper, an energy detection-based low power, low complexity IR-UWB receiver in multipath impulse radio channel is presented. The proposed receiver has a simple 1-bit sampler for energy detection. Also, multipath signal received from multipath impulse radio channel is amplified and envelope of the signal is detected. Then, energy detection technique using integrator by summing multipath signals in certain period is adopted to minimize the BER loss by simple energy detection. In particular, in acquisition of a sample signal, SNR is additionally improved using a digital sampler. Symbol decision using several sampled signals is performed and thus the process of symbol synchronization is significantly simplified. Also, it is effectively designed to be compatible with influences of multipath and timing error. In addition, the proposed receiver complexity is reduced using pulse decision window. The performance of the proposed receiver is simulated based on IEEE 802.15.4a channel model and the algorithms are implemented on FPGA.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.4 no.4
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• pp.34-38
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• 2005

This is a widespread requirement for low cost lightweight thermal imaging sensors for both military and civilian applications. Today, a large number of uncooled infrared detector developments are under progress due to the availability of silicon technology that enables realization of low cost IR sensor. System prices are continuing to drop, and swelling production volume will soon drive process substantially lower. The feasibility of micromechanical optical and infrared (IR) detection using microcantilevers is demonstrated. Microcantilevers provide a simple Structurefor developing single- and multi-element sensors for visible and infrared radiation that are smaller, more sensitive and lower in cost than quantum or thermal detectors. Microcantilevers coated with a heat absorbing layer undergo bending due to the differential stress originating from the bimetallic effect. This paper reports a micromachined silicon uncooled thermal imager intended for applications in automated process control. This paper presents the design, fabrication, and the behavior of cantilever for thermomechanical sensing.

• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.129-134
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• 1990

Exponential smoothing methods have enjoyed a long history of successful applications and have been used in forecasting for many years. However, it has been long known that one of the deficiencies of the method is an inability to respond quickly to interventions to interruptions, or to large changes in level of the underlying process. An exponential smoothing method adaptive to repeated random level changes is proposed using a change-detection statistic derived from a simple dynamic linear model. The results are compared with Trigg and Leach's and the exponential smoothing methods.