Development of High Efficiency Deep Ultraviolet Light Emitting Diodes
高效深紫外发光二极管的研制
基本信息
- 批准号:1408364
- 负责人:
- 金额:$ 33.69万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2014
- 资助国家:美国
- 起止时间:2014-08-01 至 2017-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Title: Development of High Efficiency Deep Ultraviolet Light Emitting DiodesThe objective of this research is to develop high efficiency (20-30%) deep ultraviolet light emitting diodes. The proposed research has the potential for a strong impact on the environmental and medical sectors, due to specific applications to water / air / food sterilization and for diagnostic and therapeutic uses. Furthermore, the proposed research activities will promote education through the training of students in a variety of disciplines, ranging from growth of semiconductor thin films, device fabrication and characterization, and emerging technologies. To increase the effectiveness and scope of the program, the involvement of undergraduates and high-school interns will be emphasized by taking advantage of several existing channels at Boston University, many of which have a strong focus on the recruitment of underrepresented minorities. The NSF support of this project is justified since it is basic science and the technology it addresses has a large number of societal and educational benefits. The specific goal is to demonstrate high efficiency (20-30%) deep UV LEDs based on AlGaN alloys grown on p-SiC substrates. This efficiency value is 3 times higher than the best result currently reported in the literature. In order to accomplish this goal long standing problems of low internal quantum efficiency (IQE), low injection efficiency (IE), and low extraction efficiency (EE) will be addressed. The AlGaN structures will be grown on p-SiC substrates, to which they are better lattice matched than the commonly used sapphire substrates. Improvements in IQE will be obtained by growing the active region of the device under a growth mode, which leads to band structure potential fluctuations and thus efficient radiative recombination. The expected IQE value will be 70% or higher. The LED device will be an inverted structure on a degenerately doped p-SiC substrate, which together with polarization assisted injection of holes is expected to lead to IE 50% or higher. A conducting AlGaN-based p-type DBR will be incorporated between the substrate and the active region to prevent absorption in the p-SiC substrate. Furthermore, the inverted structure will allow light extraction from the transparent n-AlGaN side of the device, which can textured for maximum light extraction leading to EE of 70% or higher.
职务名称:高效率深紫外发光二极管的研制本研究的目的是研制高效率(20-30%)的深紫外发光二极管。拟议的研究有可能对环境和医疗部门产生强烈的影响,因为它具体应用于水/空气/食品消毒以及诊断和治疗用途。此外,拟议的研究活动将通过培训学生学习各种学科来促进教育,这些学科包括半导体薄膜的生长、器件制造和表征以及新兴技术。为了提高该方案的有效性和范围,将利用波士顿大学现有的几个渠道,强调本科生和高中实习生的参与,其中许多渠道非常注重招聘代表性不足的少数民族。美国国家科学基金会对该项目的支持是合理的,因为它是基础科学,它所涉及的技术具有大量的社会和教育效益。具体目标是展示基于在p-SiC衬底上生长的AlGaN合金的高效率(20-30%)深紫外LED。该效率值比目前文献中报道的最佳结果高3倍。为了实现这一目标,将解决长期存在的低内量子效率(IQE)、低注入效率(IE)和低提取效率(EE)的问题。AlGaN结构将在p-SiC衬底上生长,与常用的蓝宝石衬底相比,AlGaN结构与p-SiC衬底具有更好的晶格匹配。IQE的改进将通过在生长模式下生长器件的有源区来获得,这导致带结构势波动,从而导致有效的辐射复合。预期IQE值将为70%或更高。LED器件将是简并掺杂的p-SiC衬底上的倒置结构,其与空穴的偏振辅助注入一起预期导致IE 50%或更高。导电AlGaN基p型DBR将结合在衬底和有源区之间,以防止p-SiC衬底中的吸收。此外,倒置结构将允许从器件的透明n-AlGaN侧提取光,其可以纹理化以获得最大光提取,从而导致70%或更高的EE。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Theodore Moustakas其他文献
Theodore Moustakas的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Theodore Moustakas', 18)}}的其他基金
EAGER: Highly Doped p-type Distributed Bragg Reflectors based on AlGaN for Deep UV Optoelectronic Devices
EAGER:用于深紫外光电器件的基于 AlGaN 的高掺杂 p 型分布式布拉格反射器
- 批准号:
1313625 - 财政年份:2013
- 资助金额:
$ 33.69万 - 项目类别:
Standard Grant
Ultraviolet electroabsorption modulators based on III-nitride quantum wells
基于III族氮化物量子阱的紫外电吸收调制器
- 批准号:
0725786 - 财政年份:2007
- 资助金额:
$ 33.69万 - 项目类别:
Standard Grant
Growth of Diamond and Cubic Boron Nitride Single Crystalline Films
金刚石和立方氮化硼单晶薄膜的生长
- 批准号:
9014370 - 财政年份:1991
- 资助金额:
$ 33.69万 - 项目类别:
Continuing grant
相似海外基金
CRII: CNS: A Systematic Multi-Task Learning Framework for Improving Deep Learning Efficiency on Edge Platforms
CRII:CNS:用于提高边缘平台深度学习效率的系统多任务学习框架
- 批准号:
2245765 - 财政年份:2023
- 资助金额:
$ 33.69万 - 项目类别:
Standard Grant
CRII: OAC: High-Efficiency Serverless Computing Systems for Deep Learning: A Hybrid CPU/GPU Architecture
CRII:OAC:用于深度学习的高效无服务器计算系统:混合 CPU/GPU 架构
- 批准号:
2153502 - 财政年份:2022
- 资助金额:
$ 33.69万 - 项目类别:
Standard Grant
Increasing the scanning time efficiency of magnetic resonance imaging systems through data integration and deep learning
通过数据集成和深度学习提高磁共振成像系统的扫描时间效率
- 批准号:
580297-2022 - 财政年份:2022
- 资助金额:
$ 33.69万 - 项目类别:
Alliance Grants
Bridging Statistical Hypothesis Tests and Deep Learning for Reliability and Computational Efficiency
连接统计假设检验和深度学习以提高可靠性和计算效率
- 批准号:
2134037 - 财政年份:2022
- 资助金额:
$ 33.69万 - 项目类别:
Continuing Grant
Applying Deep Learning Techniques to Improve Probabilistic Path Planning Efficiency for Mobile Manipulators
应用深度学习技术提高移动机械手的概率路径规划效率
- 批准号:
575601-2022 - 财政年份:2022
- 资助金额:
$ 33.69万 - 项目类别:
Alexander Graham Bell Canada Graduate Scholarships - Master's
CAREER: IIS: RI: Foundations of Deep Neural Network Robustness and Efficiency
职业:IIS:RI:深度神经网络鲁棒性和效率的基础
- 批准号:
2144960 - 财政年份:2022
- 资助金额:
$ 33.69万 - 项目类别:
Continuing Grant
Communication Environment Estimation by Deep Learning for Improving Frequency Utilization Efficiency and its Application to Adaptive Modulation Coding
提高频率利用效率的深度学习通信环境估计及其在自适应调制编码中的应用
- 批准号:
22K14253 - 财政年份:2022
- 资助金额:
$ 33.69万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
Collaborative Research: Probing the Ventilation Efficiency of the Deep Ocean with Conservative Dissolved Gas Tracers in Archived Samples
合作研究:利用存档样本中的保守溶解气体示踪剂探测深海的通风效率
- 批准号:
2122446 - 财政年份:2021
- 资助金额:
$ 33.69万 - 项目类别:
Standard Grant
Collaborative Research: Probing the Ventilation Efficiency of the Deep Ocean with Conservative Dissolved Gas Tracers in Archived Samples
合作研究:利用存档样本中的保守溶解气体示踪剂探测深海的通风效率
- 批准号:
2122427 - 财政年份:2021
- 资助金额:
$ 33.69万 - 项目类别:
Standard Grant
Collaborative Research: Probing the Ventilation Efficiency of the Deep Ocean with Conservative Dissolved Gas Tracers in Archived Samples
合作研究:利用存档样本中的保守溶解气体示踪剂探测深海的通风效率
- 批准号:
2122429 - 财政年份:2021
- 资助金额:
$ 33.69万 - 项目类别:
Standard Grant