• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2010년도 춘계학술회의 초록집
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• pp.16-16
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• 2010

GaN-based light-emitting diodes (LEDs) are attracting great interest as candidates for next-generation solid-state lighting, because of their long lifetime, small size, high efficacy, and low energy consumption. However, for general illumination applications, the external quantum efficiency of LEDs, determined by the internal quantum efficiency (IQE) and the light extraction efficiency, must be further increased. The IQE is determined by crystal quality and epitaxial layer structure and high value of IQE more than 70% for blue LEDs have been already reported. However, there is much room for improvement of light extraction efficiency because most of the generated photons from active layer remain inside LEDs by total internal reflection at the interface of semiconductor with air due to the high refractive index difference between LEDs epilayer (for GaN, n=2.5) and air (n=1). The light confining in LEDs will be reabsorbed by the metal electrode or active layer, reducing the efficacy of LEDs. Here, we present the first demonstration of enhanced light extraction by forming a MgO nano-pyramids structure on the surface of vertical-LEDs. The MgO nano-pyramids structure was successfully fabricated at room temperature using conventional electron-beam evaporation without any additional process. The nano-sized pyramids of MgO are formed on the surface during growth due to anisotropic characteristics between (111) and (200) plane of MgO. The ZnO layer with quarter-wavelength in thickness is inserted between GaN and MgO layers to increase the critical angle for total internal reflection, because the refractive index of ZnO (n=1.94) could be matched between GaN (n=2.5) and MgO (n=1.73). The MgO nano-pyramids structure and ZnO refractive-index modulation layer enhanced the light extraction efficiency ofV-LEDs with by 49%, comparing with the V-LEDs with a flat n-GaN surface. The angular-dependent emission intensity shows the enhanced light extraction through the side walls of V-LEDs as well as through the top surface of the n-GaN, because of the increase in critical angle for total internal reflection as well as light scattering at the MgO nano-pyramids surface.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2014년도 제46회 동계 정기학술대회 초록집
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• pp.246.1-246.1
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• 2014

Over the past several years, colloidal core/shell type quantum dots lighting-emitting diodes (QDLEDs) have been extensively studied and developed for the future of optoelectronic applications. In the work, we fabricate an inverted CdSe/ZnS quantum dot (QD) based light-emitting diodes (QDLED). In order to reduce work function of indium tin oxide (ITO) electrode for inverted structure, a very thin (<10 nm) polyethylenimine ethoxylated (PEIE) is used as surface modifier[1] instead of conventional metal oxide electron injection layer. The PEIE layer substantially reduces the work function of ITO electrodes which is estimated to be 3.08 eV by ultraviolet photoemission spectroscopy (UPS). From transmission electron microscopy (TEM) study, CdSe/ZnS QDs are uniformly distributed and formed by a monolayer on PEIE layer. In this inverted QDLEDs, blend of poly (9,9-di-n-octyl-fluorene-alt-benzothiadiazolo) and poly(N,N'-bis(4-butylphenyl)-N,N'-bis(phenyl)benzidine] are used as hole transporting layer (HTL) to improve hole transporting property. At the operating voltage of 8 V, the QDLED device emitted spectrally orange color lights with high luminance up to 2450 cd/m2, and showed current efficacy of 0.6 cd/A, respectively.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2014년도 제46회 동계 정기학술대회 초록집
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• pp.432.2-432.2
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• 2014

Recently, colloidal core/shell type quantum dots lighting-emitting diodes (QDLEDs) have been extensively studied and developed for the future of optoelectronic applications. In the work, we fabricate an inverted CdSe/ZnS quantum dot (QD) based light-emitting diodes (QDLED)[1]. In order to reduce work function of indium tin oxide (ITO) electrode for inverted structure, a very thin (<10 nm) polyethylenimine ethoxylated (PEIE) is used as surface modifier[2] instead of conventional metal oxide electron injection layer. The PEIE layer substantially reduces the work function of ITO electrodes which is estimated to be 3.08 eV by ultraviolet photoemission spectroscopy (UPS). From transmission electron microscopy (TEM) study, CdSe/ZnS QDs are uniformly distributed and formed by a monolayer on PEIE layer. In this inverted QD LED, two kinds of hybrid organic materials, [poly (9,9-di-n-octyl-fluorene-alt-benzothiadiazolo)(F8BT) + poly(N,N'-bis (4-butylphenyl)-N,N'-bis(phenyl)benzidine (poly-TPD)] and [4,4'-N,N'-dicarbazole-biphenyl (CBP) + poly-TPD], were adopted as hole transport layer having high highest occupied molecular orbital (HOMO) level for improving hole transport ability. At a low-operating voltage of 8 V, the device emits orange and red spectral radiation with high brightness up to 2450 and 1420 cd/m2, and luminance efficacy of 1.4 cd/A and 0.89 cd/A, respectively, at 7 V applied bias. Also, the carrier transport mechanisms for the QD LEDs are described by using several models to fit the experimental I-V data.

• Smart Structures and Systems
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• 제29권1호
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.