• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2008년도 하계학술대회 논문집 Vol.9
• /
• pp.102-103
• /
• 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
• /
• 제15권1호
• /
• pp.1-11
• /
• 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.

• 전기전자학회논문지
• /
• 제26권2호
• /
• pp.306-312
• /
• 2022

서로 다른 PN 비율과 금속화 어닐링 온도에 의해 장벽 높이가 다른 4H-SiC 병합 PiN Schottky(MPS) 다이오드의 전기적 특성과 심층 트랩을 조사했다. MPS 다이오드의 장벽 높이는 IV 및 CV 특성에서 얻었다. 전위장벽 높이가 낮아짐에 따라 누설 전류가 증가하여 10배의 전류가 발생하였다. 또한, 심층 트랩(Z_1/2 및 RD_1/2)은 4개의 MPS 다이오드에서 DLTS 측정을 통해 밝혀졌다. DLTS 결과를 기반으로, 트랩 에너지 준위는 낮은 장벽 높이와 함께 22~28%의 얕은 수준으로 확인되었다. 이는 쇼트키 장벽 높이에 대해 DLTS에 의해 결정된 결함 수준 및 농도의 의존성을 확인할 수 있다.

• 한국재료학회지
• /
• 제9권1호
• /
• pp.104-107
• /
• 1999

Fe 강제오염된 p-Si에서 여러 가지 quenching 조건에 기인한 에너지 준위들을 deep level transient spectroscopy(DLTS)를 이용하여 측정하였으며, 또한 선택 에칭방법/Optical microscope을 이용한 BMD(bulk micro-defeat)측정을 통하여 Fe 침전물 형서에, Fe 확산을 위한 어닐링 후 Cooling 조건이 미치는 영향을 분석하였다. Cooling 조건들이 여러 종류의 hole trap과 bulk micro-defeat(BMD)형성에 영햐을 주는 것으로 나타났으며, normal cooling의 경우 $\textrm{Fe}_{i}$, 또는 Fe-O complex 와 관계있는 $\textrm{T}_{1},\;\textrm{T}_{2},\;\textrm{T}_{3},\;\textrm{T}_{4}$ trap이 나타났으며, Slow Cooling 의 영향으로 인하여 활성화 에너지가 0.4eV에 해당하는 trap들이 관찰되었다. 또한 $\textrm{Fe}^{+}\textrm{}^{-}$ pair(H4: 0.56eV)는 $\textrm{LN}_{2}$ quenching한 경우에서만 나타났다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(I)
• /
• pp.465-468
• /
• 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.

• 한국IT서비스학회지
• /
• 제17권1호
• /
• pp.79-89
• /
• 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.

• 디지털산업정보학회논문지
• /
• 제16권4호
• /
• pp.19-28
• /
• 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
• /
• 제18권4호
• /
• pp.587-598
• /
• 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.

• 반도체디스플레이기술학회지
• /
• 제21권2호
• /
• pp.123-126
• /
• 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.

• 전기전자학회논문지
• /
• 제28권1호
• /
• pp.104-109
• /
• 2024

본 연구에서는 다양한 가스 분위기에서 후열처리를 진행한 후 Ga₂O₃/SiC 이종접합 다이오드의 깊은 준위 결함 변화를 Deep Level Transient Spectroscopy(DLTS) 기법으로 분석하여 깊은 준위 결함의 변화가 Ga₂O₃/SiC 이종접합 소자의 전기적 특성에 미치는 영향을 조사하였다. 또한, J-V 측정 및 Hall 측정을 통한 전기적 특성 분석을 실시하였고, N₂ 분위기에서 열처리된 소자에서 3.06 × 10^-2 A/cm²로 가장 높은 on-state current가 측정되었으며, carrier concentration은 3.8 × 10¹⁴ cm^-3로 증가하는 것이 관측되었다. 이는 후열처리 분위기에 따른 깊은 준위 결함의 변화가 전기적 특성에 영향을 미칠 수 있음을 시사한다.