基于卷积神经网络智能识别吸收峰的激光稳频方法  

Laser Frequency Stabilization Method Based on Intelligent Identifying Absorption Peaks with Convolutional Neural Network

作　　者：陈本永[1] 赵勇[1] 楼盈天[1] 严利平[1] 谢建东[1] 于良 唐健钧 Chen Benyong;Zhao Yong;Lou Yingtian;Yan Liping;Xie Jiandong;Yu Liang;Tang Jianjun(Precision Measurement Laboratory,School of Information Science and Engineering,Zhejiang Sci-TechUniversity,Hangzhou 310018,Zhejiang,China)

机构地区：[1]浙江理工大学信息科学与工程学院精密测量技术实验室,浙江杭州310018

出　　处：《中国激光》2024年第17期105-115,共11页Chinese Journal of Lasers

基　　金：国家自然科学基金(52035015,52375552);国家重点研发计划(2022YFF0705803)。

摘　　要：针对饱和吸收光谱激光稳频技术中多吸收峰难以自动识别、误差信号灵敏度低的问题,提出了一种基于卷积神经网络(CNN)智能识别铷原子吸收峰的激光频率锁定方法。首先设计了大、小卷积核相结合的5个卷积-Re LU-最大池化模块+两层全连接层的一维卷积神经网络;然后对激光器进行线性扫频,获得了包含24个饱和吸收峰的铷原子的饱和吸收光谱信号S_(sa)(t),提取每个吸收峰的位置序号,将其与饱和吸收光谱信号作为卷积神经网络训练的标签和数据,利用训练后的卷积神经网络实现吸收峰的智能识别;最后采用正交解调技术精确提取饱和吸收光谱信号的相位,使本振信号相位与其自动匹配,提高误差信号的灵敏度。设计了基于上位机卷积神经网络智能寻峰+现场可编程门阵列(FPGA)实时信号处理的激光频率锁定系统,锁定后的激光频率与光频梳拍频结果表明:在7500 s内,激光频率波动标准差为7.94 k Hz;平均时间为64 s时,相对Allan方差为3.50×10^(-12)。所提出的方法可被广泛应用于饱和吸收光谱激光稳频领域。Objective To address the challenges of automatically identifying absorption peaks and the low sensitivity of error signals in the saturated absorption spectrum laser frequency stabilization technique,a method using convolutional neural network(CNN)is proposed for recognizing rubidium atomic absorption peaks.This approach is highly applicable in the realm of saturated absorption spectroscopy laser frequency stabilization.Traditional techniques are limited to identifying and locking onto specific absorption peaks within a narrow laser tuning range,necessitating manual pre-adjustment of the laser frequency close to the absorption peak.However,in practice,the initial laser operating point is often unknown,requiring broad frequency scans to locate the target absorption peak signal.This can result in detecting multiple groups of absorption peaks.Moreover,the process of deriving error signals is complicated with respect to the phase delay between the saturated absorption signal and local oscillator signal,impacting error signal sensitivity.Typically,phase adjustment of the local oscillator signal is manually performed and monitored with an oscilloscope to capture the most sensitive error signal.This method is inefficient and inaccurate,and thereby,fails to satisfy the demands of high-precision automatic laser frequency stabilization.Consequently,a CNN-based laser frequency stabilization method,which intelligently recognizes rubidium atomic absorption peaks and automatically adjusts for phase delay,is introduced to realize long-term precision stabilization of laser frequency.Methods Initially,a one-dimensional convolutional neural network(CNN)was designed,incorporating a combination of five large and small convolution kernels.This design included“convolution-ReLU-maxpooling”modules followed by two fully connected layers.A linear sweep of the laser frequency was then performed to acquire a spectrum signal from rubidium atoms,containing 24 saturated absorption peaks.The sequence number of each absorption peak was extra

关 键 词：物理光学 饱和吸收光谱 激光频率锁定 卷积神经网络 吸收峰智能识别 铷原子 

分 类 号：O436[机械工程—光学工程]