• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.9
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• pp.701-709
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• 2011

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.

• Journal of Sensor Science and Technology
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• v.17 no.6
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• pp.437-446
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• 2008

Single crystalline ${ZnIn_2}{Se_4}$ layers were grown on thoroughly etched semi-insulating GaAs (100) substrate at $400^{\circ}C$ with hot wall epitaxy (HWE) system by evaporating ${ZnIn_2}{Se_4}$ source at $630^{\circ}C$. The crystalline structure of the single crystalline thick films was investigated by the photoluminescence (PL) and Double crystalline X-ray rocking curve (DCRC). The carrier density and mobility of ${ZnIn_2}{Se_4}$ single crystalline thick films measured from Hall effect by van der Pauw method are $9.41{\times}10^{16}cm^{-3}$ and $292cm^2/V{\cdot}s$ at 293 K, respectively. The temperature dependence of the energy band gap of the ${ZnIn_2}{Se_4}$ obtained from the absorption spectra was well described by the Varshni's relation, $E_g(T)$=1.8622 eV-$(5.23{\times}10^{-4}eV/K)T^2$/(T+775.5 K). After the as-grown ${ZnIn_2}{Se_4}$ single crystalline thick films was annealed in Zn-, Se-, and In-atmospheres, the origin of point defects of ${ZnIn_2}{Se_4}$ single crystalline thick films has been investigated by the photoluminescence (PL) at 10 K. The native defects of $V_{Zn}$, $V_{Se}$, $Zn_{int}$, and $Se_{int}$ obtained by PL measurements were classified as a donors or acceptors type. And we concluded that the heat-treatment in the Se-atmosphere converted ${ZnIn_2}{Se_4}$ single crystalline thick films to an optical p-type. Also, we confirmed that In in ${ZnIn_2}{Se_4}$/GaAs did not form the native defects because In in ${ZnIn_2}{Se_4}$ single crystalline thick films existed in the form of stable bonds.

• Journal of Sensor Science and Technology
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• v.6 no.5
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• pp.392-399
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• 1997

In this study, the effect of the sintering temperature on the dielectric and piezoelectric properties of 0.5 wt% $MnO_{2}$-doped $0.125Pb(Mg_{1/2}Nb_{2/3})O_{3}-0.435PbTiO_{3}-0.44PbZrO_{3}$ ceramics were investigated aiming at ultrasonic motor applications. From experimental result, it was found that the optimal sintering temperature condition was at $1270^{\circ}C$. The sample sintered at $1270^{\circ}C$ had density of $7.72\;g/cm^{3}$, dielectric constant of 570, dielectric loss of 0.82%, remanent polarization of $19.18{\mu}C/cm^{2}$, coercive field of 9.63 kV/cm, electromechanical coupling factor of radial mode of 55.1%, mechanical quality factor of 886. Temperature and frequency dependence of dielectric constant and dielectric loss of the sintered sample at $1270^{\circ}C$ was also investigated.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.426-428
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• 2003

Optical and microwave remote sensing data have been widely used in land cover and land use classification. Thanks to the spectral absorption characteristics of ground object in visible and near infrared region, optical data enables to extract different land cover types according to their material composition like water body, vegetation cover or bare land. On the other hand, microwave sensor receives backscatter radiance which contains information on surface roughness, object density and their 3-D structure that are very important complementary information to interpret land use and land cover. Separate use of these data have brought many successful results in practice. However, the accuracy of the land use / land cover established by this methodology still has some problems. One of the way to improve accuracy of the land use / land cover classification is just combination of both optical and microwave data in analysis. In this paper for the research, the author used LANDSAT TM scene 127/45 acquired on October 21, 1992, JERS-1 SAR scene 119/265 acquired on October 27, 1992 and aerial photographs taken on October 21, 1992. The study area has been selected in Hanoi City and surrounding area, Vietnam. This is a flat agricultural area with various land use types as water rice, secondary crops like maize, cassava, vegetables cultivation as cucumber, tomato etc. mixed with human settlement and some manufacture facilities as brick and ceramic factories. The use of only optical or microwave data could result in misclassification among some land use features as settlement and vegetables cultivation using frame stages. By combination of multitemporal JERS-1 SAR and TM data these errors have been eliminated so that accuracy of the final land use / land cover map has been improved. The paper describes a methodology for data combination and presents results achieved by the proposed approach.

• Nuclear Engineering and Technology
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• v.39 no.3
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• pp.193-206
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• 2007

The Korea Atomic Energy Research Institute has developed an advanced fast reactor concept, KALIMER-600, which satisfies the Generation IV reactor design goals of sustainability, economics, safety, and proliferation resistance. The concept enables an efficient utilization of uranium resources and a reduction of the radioactive waste. The core design has been developed with a strong emphasis on proliferation resistance by adopting a single enrichment fuel without blanket assemblies. In addition, a passive residual heat removal system, shortened intermediate heat-transport system piping and seismic isolation have been realized in the reactor system design as enhancements to its safety and economics. The inherent safety characteristics of the KALIMER-600 design have been confirmed by a safety analysis of its bounding events. Research on important thermal-hydraulic phenomena and sensing technologies were performed to support the design study. The integrity of the reactor head against creep fatigue was confirmed using a CFD method, and a model for density-wave instability in a helical-coiled steam generator was developed. Gas entrainment on an agitating pool surface was investigated and an experimental correlation on a critical entrainment condition was obtained. An experimental study on sodium-water reactions was also performed to validate the developed SELPSTA code, which predicts the data accurately. An acoustic leak detection method utilizing a neural network and signal processing units were developed and applied successfully for the detection of a signal up to a noise level of -20 dB. Waveguide sensor visualization technology is being developed to inspect the reactor internals and fuel subassemblies. These research and developmental efforts contribute significantly to enhance the safety, economics, and efficiency of the KALIMER-600 design concept.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.175-184
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• 2009

This study attempts to analyze the difference among the variations of ecological distribution in Gobi desert and Manchuria through satellite based land cover classification. This was motivated by two well-known facts: 1) Gobi desert, which is an old source region, had been gradually expanded eastward; 2) Manchuria, which is located in east of Gobi desert, was observed as a new source region of yellow dust. An unsupervised classification called ISODATA clustering method was employed to detect the land cover change and to characterize the status of desertification and its expanding trends using NDVI (Normalized Difference Vegetation Index) derived from VEGETATION sensor onboard the SPOT satellite for 1999 and 2007. We analyzed NDVI annual variation pattern for every classes and divide into 5 level according to their vegetation's density level based on NDVI. As results, Gobi desert is showed positive variation: a decrease $78,066km^2$ in central Gobi desert and out skirts of Gobi desert (level-0) but Manchuria area is worse than previous time: an increase $25,744km^2$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.935-941
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• 2020

People estimation is important to provide IoT services. Most people counting technologies use camera or sensor data. However, the conventional technologies have the disadvantages of invasion of privacy and the need to install extra infrastructure. This paper proposes a method for estimating the number of people using a Wi-Fi AP. We use channel state information of Wi-Fi and analyze that using deep learning technology. It can be achieved by pre-installed Wi-Fi infrastructure that reduce cost for people estimation and privacy infringement. The proposed algorithm uses a k-binding data for pre-processing process and a 1D-CNN learning model. Two APs were installed to analyze the estimation results of six people. The result of the accurate number estimation was 64.8%, but the result of classifying the number of people into classes showed a high result of 84.5%. This algorithm is expected to be applicable to estimate the density of people in a small space.

• Current Applied Physics
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• v.18 no.12
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• pp.1473-1479
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• 2018

Recently multiferroic materials have attract great interest for the applications on memorial, spintronic and magneto-electric sensor devices for their spontaneous magneto-electric coupling properties. Research and development of the various kinds of multiferroics are indispensable factor for a new generation multifunctional materials. In this research, mechanical, electronic, magnetic and nonlinear optical properties of La modified $BiLaFe_2O_6$ (BLFO) and Mn modified $Bi_2FeMnO_6$ (BFMO) were studied as new members of multiferroic $BiFeO_3$ (BFO) series by first-principles calculations, and compared with the pure BFO to discover the optimized properties. Our results show that BLFO and BFMO have good mechanical stability as revealed by elastic constants that satisfy the stability criteria. All these compounds exhibit anisotropic and ductile nature. The enhanced properties by La and Mn substitution, such as increased hardness, improved magnetism, decreased band gap and comparable second harmonic generation responses reveal that the new multiferroic members of BLFO and BFMO would get wider application than their BFO counterpart. Our study is expected to providing an appropriate mechanical reference data as guidance for engineering of high efficiency multifunctional devices with the BFO series.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.481-487
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• 2020

Radiation measurement technology has steadily improved and its usage is expanding in various industries such as nuclear medicine, security search, satellite, nondestructive testing, environmental industries and the domain of nuclear power plants (NPPs). Especially, the simultaneous measurements of gamma rays and neutrons can be even more critical for nuclear safety management of spent nuclear fuel and monitoring of the nuclear material. A semiconductor detector comprising cadmium, zinc, and tellurium (CZT) enables to detect gamma-rays due to the significant atomic weight of the elements via immediate neutron and gamma-ray detection. Semiconductor sensors might be used for nuclear safety management by monitoring nuclear materials and spent nuclear fuel with high spatial resolution as well as providing real-time measurements. We aim to introduce a portable nuclide-analysis device that enables the simultaneous measurements of neutrons and gamma rays using a CZT sensor. The detector has a high density and wide energy band gap, and thus exhibits highly sensitive physical characteristics and characteristics are required for performing neutron and gamma-ray detection. Portable nuclide-analysis device is used on NPP-decommissioning sites or the purpose of nuclear nonproliferation, it will rapidly detect the nuclear material and provide radioactive-material information. Eventually, portable nuclide-analysis device can reduce measurement time and economic costs by providing a basis for rational decision making.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.