优化p-EBL合金组成提升深紫外LED高效率：分级设计与注入性能

0 下载量 14 浏览量 更新于2024-08-29 收藏 1.89MB PDF 举报
本文主要探讨了如何通过优化p型（AlGaN）电子阻挡层（p-EBL）的合金成分来提高深紫外发光二极管（Deep Ultraviolet Light-Emitting Diodes, DUV LEDs）的性能。在深紫外LEDs中，通常采用p-EBL来阻止电子注入，防止光致降级效应。然而，由于[0001]方向的DUV LEDs中存在极化诱导的电场，这会导致注入的空穴（holes）迁移性减弱，进一步降低了注入效率。 作者们注意到，通过减少空穴在p-EBL中的能量损失，可以有效地增强空穴注入。他们提出了一种创新的方法，即设计具有梯度AlN成分的p-EBL结构。这种梯度结构意味着在材料中AlN含量并非均匀分布，而是逐渐变化，从而在不同的区域提供不同程度的能量阻挡。这样做的好处在于，它能够在一定程度上抵消极化电场的影响，使得空穴在更小的能量损耗下更容易穿越p-EBL，从而提高了整体的光发射效率。 研究团队——来自河北工业大学微纳米光电与电磁技术创新研究院、台湾交通大学光子学系、加州大学伯克利分校电气工程与计算机科学系及TBSI——进行了深入的理论分析和实验验证，旨在通过精确控制AlN含量的变化，优化空穴传输路径，以实现非常高的DUV LED性能提升。他们在2018年12月12日收到了论文初稿，在经过两次修订后于2019年2月20日接受，并于同年2月21日在线发布，最终于3月22日正式发表。 这项工作对于提升深紫外LED技术的性能具有重要意义，因为它不仅解决了现有技术面临的挑战，还为开发出能在高功率和高效率下工作的DUV光源提供了新的设计策略。未来的研究可能会进一步探索其他掺杂元素或者复合结构，以进一步优化p-EBL的性能，推动深紫外LED技术在显示、医疗和通信等领域的发展。

【5】Very deep convolutional networks for large-scale image recognition.pdf

2019-08-26 上传
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of ...

High-Performance Computing Using FPGAs

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FPGAs offer very high I/O bandwidth and fine-grained, custom and flexible parallelism and with the ever-increasing computational needs coupled with the frequency/power wall, the increasing maturity ...

CUDA for Engineers An Introduction to High Performance Parallel Computing

2017-06-06 上传
### CUDA for Engineers: An Introduction to High-Performance Parallel Computing #### 关键知识点概述 1. **CUDA简介** - CUDA（Compute Unified Device Architecture）是NVIDIA公司开发的一种并行计算平台和...

Very Deep Convolutional Networks for Large-Scale Image Recognition" by Karen Simonyan and Andrew Zisserman (2014)

2023-05-12 上传
"Very Deep Convolutional Networks for Large-Scale Image Recognition" is a research paper by Karen Simonyan and Andrew Zisserman, which was published in 2014. The paper introduces the architecture of ...

请用英语把下段话修改的更加合理：When the radiation ratio is high, the conversion rate of controlled methane will decrease, which will increase the inlet flow under the control of the reactor, so as to increase the heat consumption and reduce the reactor temperature and improve the hydrogen yield. When the radiation ratio is low and the heat supply is insufficient, the inlet flow rate can be reduced by increasing the controlled methane conversion rate, thus reducing the heat release rate of the reactor and keeping the reactor stable for a long time at a low yield.

2023-02-22 上传
When the radiation ratio is high, the conversion rate of controlled methane will decrease, resulting in an increase in the inlet flow rate under the control of the reactor. This increased heat ...

Please revise the paper：Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.

2023-02-18 上传
通过基于聚类的底部反射率自适应分割，并采用PSO-LightGBM算法考虑多种因素，实现了对不同沉积物和水深的准确深度估算，与空中激光测深和多波束回声深度声纳联合实测结果相比，均方根误差值达到0.85m，是传统比值法...

Influence of Autoencoder-Based Data Augmentation on Deep Learning-Based Wireless Communication

2023-03-30 上传
By increasing the diversity of the training data, the deep learning model is better able to generalize to new data and improve its performance. Furthermore, autoencoder-based data augmentation can ...

Write a paper about Deep-learning based analysis of metal-transfer images in GMAW process , requiring 10000 words

2023-02-16 上传
To further analyze the performance of the method, we conducted a sensitivity analysis by varying the number of convolutional layers in the CNN. We found that the performance of the method improved ...

Action 4: increasing the number of books of a given user. When the user of the software specifies action 4, your program must ask the user to type the name of a user, and a number of books, and the program then uses that number to increase the number of books lent or borrowed by the user. Then the program goes back to the main menu. For example: Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): 3 Enter the name of the user: Anna Anna borrows -5 book(s). Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): 4 Enter the name of the user: Anna Enter the number of books: 2 Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): 3 Enter the name of the user: Anna Anna borrows -7 book(s). Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): 3 Enter the name of the user: Bob Bob borrows 10 book(s). Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): 4 Enter the name of the user: Bob Enter the number of books: 2 Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): 3 Enter the name of the user: Bob Bob borrows 12 book(s). Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): If the name of the user is wrong, then an UnknownUserException exception will be thrown by the Library object. The code of the main method of your CLI class must catch this exception, print the error message from the exception object, and then it just goes back to printing the menu of actions (by just going back to the beginning of the while loop). For example (where 4, aaaa, and 2 are inputs from the user): Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): 4 Enter the name of the user: aaaa Enter the number of books: 2 User aaaa unknown. Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): Note that, even if a consumer is a borrower, the readPosInt method prevents the typed number of books from being negative. This means a borrower will never throw a NotALenderException. Nevertheless the code of the main method of your CLI class must handle this exception by printing the error message "BUG! This must never happen!" and immediately terminating the program using System.exit(1). For example (where 3, Bob, 4, Bob, and -15 are inputs from the user): Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): 3 Enter the name of the user: Bob Bob borrows 12 book(s). Type an action (total:1 add:2 get:3 more:4 less:5 quit:6): 4 Enter the name of the user: Bob Enter the number of books: -15 Positive integers only! Enter the number of books:

2023-05-24 上传
Sure, here's the updated code for the CLI class: ``` import java.util.Scanner; public class CLI { private Library library; private Scanner scanner; public CLI(Library library) { this.library =...

Implement time_per_word, which takes in times_per_player, a list of lists for each player with timestamps indicating when each player finished typing each word. It also takes in a list words. It returns a game with the given information. A game is a data abstraction that has a list of words and times. The times are stored as a list of lists of how long it took each player to type each word. times[i][j] indicates how long it took player i to type word j. For example, if times_per_player = [[1, 3, 5], [2, 5, 6]], the corresponding time attribute of the game would be [[2, 2], [3, 1]]. Timestamps are cumulative and always increasing, while the values in time are differences between consecutive timestamps. Be sure to use the game constructor when returning a game, rather than assuming a particular data format.Given timing data, return a game data abstraction, which contains a list of words and the amount of time each player took to type each word. Arguments: times_per_player: A list of lists of timestamps including the time the player started typing, followed by the time the player finished typing each word. words: a list of words, in the order they are typed.

2023-06-01 上传
Note that this implementation assumes that all players typed the same set of words in the same order, and that the timestamps are cumulative and increasing (i.e., the time to type each word is always ...
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