• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.102-103
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• 2008

Magnetron Sputtering, MOCVD, Thermal Evaporation에 의해 성장된 ZnO layer에 대한 Dependency Temperature Photoluminescence (PL)를 이용하여 비교 분석을 통해 Deep level emission에 대해 연구하였다. Sputter에 의해 성장된 ZnO 박막은 Violet, Green, Orange-red 영역의 $Zn_i$, $V_o$, $O_i$의 defect에 의한 Deep level emission을 보였고, MOCVD에 의해 성장된 박막은 비교적 산소양이 낮은 성장 조건에서는 blue-green 영역에서, 산소양이 높은 조건에서의 박막은 Orange-red 영역의 Deep level emission을 보였다. Blue-green 영역에서의 emission은 온도가 증가함에 따라 다른 Barrier를 보였는데, 이는 $V_{Zn}$와 $V_o$에 의한 것임을 알 수 있었다. 한편, ZnO nanorods는 $V_o$에 의한 Green 영역에서의 Deep level emission을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.1-11
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• 2015

A concept of Deep Level Situation Understanding is proposed to realize human-like natural communication (called casual communication) among multi-agent (e.g., humans and robots/machines), where the deep level situation understanding consists of surface level understanding (such as gesture/posture understanding, facial expression understanding, speech/voice understanding), emotion understanding, intention understanding, and atmosphere understanding by applying customized knowledge of each agent and by taking considerations of thoughtfulness. The proposal aims to reduce burden of humans in humans-robots interaction, so as to realize harmonious communication by excluding unnecessary troubles or misunderstandings among agents, and finally helps to create a peaceful, happy, and prosperous humans-robots society. A simulated experiment is carried out to validate the deep level situation understanding system on a scenario where meeting-room reservation is done between a human employee and a secretary-robot. The proposed deep level situation understanding system aims to be applied in service robot systems for smoothing the communication and avoiding misunderstanding among agents.

• Journal of IKEEE
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• v.26 no.2
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• pp.306-312
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• 2022

We investigated electrical properties and deep level traps in 4H-SiC merged PiN Schottky (MPS) diodes with different barrier heights by different PN ratios and metallization annealing temperatures. The barrier heights of MPS diodes were obtained in IV and CV characteristics. The leakage current increased with the lowering barrier height, resulting in 10 times larger current. Additionally, the deep level traps (Z_1/2 and RD_1/2) were revealed by deep level transient spectroscopy (DLTS) measurement in four MPS diodes. Based on DLTS results, the trap energy levels were found to be shallow level by 22~28% with lower barrier height It could confirm the dependence of the defect level and concentration determined by DLTS on the Schottky barrier height and may lead to incorrect results regarding deep level trap parameters with small barrier heights.

• Korean Journal of Materials Research
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• v.9 no.1
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• pp.104-107
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• 1999

We investigated the effects of cooling condition on deep levels and iron precipitate formation in iron-contaminated p-type silicon by DLTS(Deep Level Transient Spectroscopy) and preferential etching technique. Dependency of cooling condition on Bulk Micro-Defect (BMD) and four different iron-related deep traps were observed. For normal cooling condition, T1, T2, T3, T4 traps that related to Fe\ulcorner or Fe-O complex were obtained. However, the trap with activation energy, 0.4 eV was observed for slow cooling condition. The trap caused by the $\textrm{Fe}^{+}\textrm{}^{-}$ pair (H4:0.56eV) were detected only at the case of $\textrm{LN}_{2}$ quenching condition.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.465-468
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• 1987

This paper describes a model to calculate the equilibrium electron density of MODFET at the interface that takes into account the simultaneous shallow and deep level in the Al-GaAs layer. In the present study we have made an investigation of the interface electron density with different values of the AlGaAs doping density and spacer layer thickness, considering simultaneously two doner levels. In this case, the ratio of the shallow to the deep donor concentraction is considered. From the comparison with early experimental results we could find the deep level and that the deep donor concentration is about 50% with the Al mole fraction X ${\sim}0.3$, activation energy Edx=65meV, temperature $77^{\circ}K$ and spacer thickness range $50A{\sim}100A$. Also we have investigated the effect of the temperature. As temperature increase, at critical mole fraction X the nature of the donor concentration changes from $\Gamma$ to L and X.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.79-89
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• 2018

Sentiment analysis is a technique of text mining that extracts feelings of the person who wrote the sentence like movie review. The preliminary researches of sentiment analysis identify sentiments by using the dictionary which contains negative and positive words collected in advance. As researches on deep learning are actively carried out, sentiment analysis using deep learning model with morpheme or word unit has been done. However, this model has disadvantages in that the word dictionary varies according to the domain and the number of morphemes or words gets relatively larger than that of phonemes. Therefore, the size of the dictionary becomes large and the complexity of the model increases accordingly. We construct a sentiment analysis model using recurrent neural network by dividing input data into phoneme-level which is smaller than morpheme-level. To verify the performance, we use 30,000 movie reviews from the Korean biggest portal, Naver. Morpheme-level sentiment analysis model is also implemented and compared. As a result, the phoneme-level sentiment analysis model is superior to that of the morpheme-level, and in particular, the phoneme-level model using LSTM performs better than that of using GRU model. It is expected that Korean text processing based on a phoneme-level model can be applied to various text mining and language models.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.19-28
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• 2020

Vehicle license plate recognition system is not generalized in Malaysia due to the loose character layout rule and the varying number of characters as well as the mixed capital English characters and italic English words. Because the italic English word is hard to segmentation, a separate method is required to recognize in Malaysian license plate. In this paper, we propose a mixed character level and word level English license plate recognition algorithm using deep learning neural networks. The difference of Gaussian method is used to segment character and word by generating a black and white image with emphasized character strokes and separated touching characters. The proposed deep learning neural networks are implemented on the LPR system at the gate of a building in Kuala-Lumpur for the collection of database and the evaluation of algorithm performance. The evaluation results show that the proposed Malaysian English LPR can be used in commercial market with 98.01% accuracy.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.587-598
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• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.123-126
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• 2022

We investigated deep levels in n-type 4H-SiC epitaxy layer of the Positive-Intrinsic-Negative diode and Schottky barrier diodes by using deep level transient spectroscopy. Despite the excellent performance of 4H-SiC, research on various deep level defects still requires a lot of research to improve device performance. In Positive-Intrinsic-Negative diode, two defects of 196K and 628K are observed more than Schottky barrier diode. This is related to the action of impurity atoms infiltrating or occupying the 4H-SiC lattice in the ion implantation process. The I-V characteristics of the Positive-Intrinsic-Negative diode shows about ~100 times lower the leakage current level than Schottky barrier diode due to the grid structures in Positive-Intrinsic-Negative. As a result of comparing the capacitance of devices diode and Schottky barrier diode devices, it can be seen that the capacitance value lowered if it exists the P implantation regions from C-V characteristics.

• Journal of IKEEE
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• v.28 no.1
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• pp.104-109
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• 2024

In this research, we explored the influence of post-annealing atmospheres on the electrical properties of Ga₂O₃/SiC heterojunction diodes. We fabricated Ga₂O₃/SiC heterojunction diodes by RF sputtering and after the fabrication the post-annealing in various gas atmospheres was performed. We measured the changes in deep-level defects using Deep Level Transient Spectroscopy (DLTS) and we conducted an electrical characteristic of J-V measurement and Hall measurement to analyzed the effects of annealing atmosphere on Ga₂O₃/SiC heterojunction diode. In the N₂ annealed devices, the highest on-state current was measured as 3.06 × 10^-2 A/cm^2, and an increase in carrier concentration of 3.8 × 10¹⁴ cm^-3 was observed. This confirms that the variations in deep level defects due to the post-annealing atmosphere can influence the electrical properties.