现代电子技术2026，Vol.49Issue(2)：49-53,5.DOI:10.16652/j.issn.1004-373x.2026.02.008
基于PSO-BP神经网络的硅基光子器件光损耗异常监测系统
Optical loss anomaly monitoring system of silicon-based photonic devices based on PSO-BP neural network
闵月淇 1谢亮2
作者信息
- 1. 北方工业大学 机械与材料工程学院,北京 100144
- 2. 北方工业大学 理学院,北京 100144
- 折叠
摘要
Abstract
The optical loss of silicon-based photonic devices is affected by various operating parameters,which causes the deviations or omissions to occur in the monitoring of abnormal optical loss.In order to comprehensively consider the influence of operating parameters and achieve accurate monitoring of optical loss anomalies,a silicon-based photonic device optical loss anomaly monitoring system based on PSO-BP neural network is designed.The data acquisition module of the system is used to collect various operating parameters such as wavelength and temperature of silicon-based photonic devices in real time.After processing by the data preprocessing module,they are input into the optical loss detection module with PSO-BP neural network as the core,thereby obtaining optical loss detection values under various operating parameters.The abnormal monitoring and warning module can be used to compare the obtained optical loss detection value with the set threshold to determine whether the optical loss is abnormal.If it is abnormal,a warning is issued.The user interaction module can present abnormal monitoring and warning information,completing the monitoring of abnormal optical loss in silicon-based photonic devices.The results show that the designed system can comprehensively monitor the abnormal optical loss of silicon-based photonic devices for different operating parameters such as wavelength,temperature,waveguide length,and output optical power,and effectively warn against various abnormal optical loss scenarios.关键词
硅基光子器件/光损耗/异常监测/PSO-BP神经网络/异常预警/波导长度Key words
silicon-based photonic device/optical loss/anomaly monitoring/PSO-BP neural network/abnormal early warning/waveguide length分类
信息技术与安全科学引用本文复制引用
闵月淇,谢亮..基于PSO-BP神经网络的硅基光子器件光损耗异常监测系统[J].现代电子技术,2026,49(2):49-53,5.
