基于BP神经网络的LED可靠性模型研究

黄伟明; 文尚胜; 夏云云

doi:10.3788/fgxb20153608.0962

您当前的位置：

首页 >

文章列表页 >

基于BP神经网络的LED可靠性模型研究

发光学应用及交叉前沿 | 更新时间：2020-08-12

- 基于BP神经网络的LED可靠性模型研究
- Reliability Model of LEDs Based on Artificial Neural Network
- 发光学报 2015年36卷第8期页码：962-968
- 作者机构：
  
  发光材料与器件国家重点实验室华南理工大学,广东广州,510640
- 作者简介：
- 基金信息：
  
  广东省战略性新兴产业专项(2011A081301017,2012A080304012,2012A080304001)();广州市科技计划(2013J4300021)资助
- DOI：10.3788/fgxb20153608.0962
  中图分类号： TN312+.8
- 纸质出版日期：2015-8-3，
  
  收稿日期：2015-4-27，
  
  修回日期：2015-6-26，
扫描看全文
黄伟明, 文尚胜, 夏云云. 基于BP神经网络的LED可靠性模型研究[J]. 发光学报, 2015,36(8): 962-968

HUANG Wei-ming, WEN Shang-sheng, XIA Yun-yun. Reliability Model of LEDs Based on Artificial Neural Network[J]. Chinese Journal of Luminescence, 2015,36(8): 962-968
黄伟明, 文尚胜, 夏云云. 基于BP神经网络的LED可靠性模型研究[J]. 发光学报, 2015,36(8): 962-968 DOI： 10.3788/fgxb20153608.0962.

HUANG Wei-ming, WEN Shang-sheng, XIA Yun-yun. Reliability Model of LEDs Based on Artificial Neural Network[J]. Chinese Journal of Luminescence, 2015,36(8): 962-968 DOI： 10.3788/fgxb20153608.0962.

摘要

根据LED可靠性与相关参数的映射关系

建立拓扑结构为6-12-1的BP神经网络.以实测白光LED芯片的理想因子、结温、色温漂移等参数为输入量

以寿命为输出量

计算模型精度.研究结果表明

该模型有良好的外推能力及鲁棒性

可在短时间内成功预测LED寿命

神经网络训练结果相关系数为99.8%

检验组误差小于3%.

Abstract

We proposed the 6-12-1 topology BP neural network according to the LED reliability and relevant element. The ideal factor

junction temperature

color temperature drift of the white LED chip and so on were measured as the input and the life as output to calculate the precision of the model. The model shows a good ability of extrapolation and robustness

and can predict the life of the LED in a short time. The linear correlation of ANN reaches 99.8%

and the inspection group error is less than 3%.

关键词

发光二极管可靠性BP神经网络权重分析

Keywords

LEDreliabilityBP neural networkweight analysis

references

McPherson J W. Reliability Physics and Engineering: Time-to-failure Modeling [M]. Berlin: Springer, 2010.

Rumyantseva S L, Shur M S. Low frequency noise and long-term stability of noncoherent light sources [J]. J. Appl. Phys., 2004, 96(2):966-969.

Rumyantsev S L, Wetzel C, Shur M S. Wavelength-resolved low-frequency noise of GalnN/GaN green light emitting diodes [J]. J. Appl. Phys., 2006, 100(8):084506-1-5

Qian M H, Lin Y D, Sun Y J, et al. Life prediction model for LEDs based on the photo-electrical-thermal-life theory [J]. Acta Optica Sinica (光学学报), 2012, 32(8):823001-1-6 (in Chinese).

Guo W L, Fan X, Cui D S. Rapid reliability evaluation method of LED based on pseudo-failure lifetime [J]. Chin. J. Lumin.(发光学报), 2013, 34(2):213-217 (in Chinese).

Hu Z F, Zhang L D, Wan J X. Application of BP neural network in predicting winter wheat yield based on thermography technology [J]. Spectrosc. Spect. Anal.(光谱学与光谱分析), 2013, 33(6):1587-1592 (in Chinese).

Zheng L H, Li M Z, Pan L, et al. Estimation of soil organic matter and soil total nitrogen based on NIR spectroscopy and BP neural network [J]. Spectrosc. Spect. Anal.(光谱学与光谱分析), 2008, 28(5):1160-1164 (in Chinese).

Sun Y, Zeng W D, Zhao Y Q, et al. Model prediction of processing-property of TC11 titanium alloy using artificial neural network [J]. Rare Met. Mater. Eng.(稀有金属材料与工程), 2011, 40(11):1951-1955 (in Chinese).

Shen H P. Research on Reliability Prediction Mechanism for High-power LEDs . Hangzhou: Zhejiang University, 2008 (in Chinese).

Keppens A, Ryckaert W R, Deconinck G, et al. High power light-emitting diode junction temperature determination from current-voltage characteristics [J]. J. Appl. Phys., 2008, 104(9):093104-1-6.

Keppens A, Ryckaert W R, Deconinck G, et al. Modeling high power light-emitting diode spectra and their variation with junction temperature [J]. J. Appl. Phys., 2010, 108(4):043104-1-6.

Lee C Y, Su A, Liu Y C, et al. In situ measurement of the junction temperature of light emitting diodes using a flexible micro temperature sensor [J]. Sensors, 2009, 9:5068-5075.

Kang J M, Kim J W, Choi J H, et al. Life-time estimation of high-power blue light-emitting diode chips [J]. Microelectron. Reliab., 2009, 49(9-11):1231-1235.

Chen Z Z, Liu P, Qi S L, et al. Junction temperature and reliability of high-power flip-chip light emitting diodes [J]. Mater. Sci. Semicond. Proc., 2007, 10(4-5):206-210.

Garson G D. Interpreting neural-network connection weights [J]. AI Expert, 1991, 6(7):47-51.

浏览量

148

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

电流拥挤效应与LED器件可靠性分析

氮和硼元素共掺杂对碳点荧光的调控机制

高气密性的深紫外LED半无机封装技术

晶相对碳点多色发光的调制及其在白光发光二极管器件中的应用

邻苯二甲酸结晶诱导荧光碳点制备及其在白光发光二极管中的应用