Dense-1D-U-Net:用于自参考光谱干涉飞秒脉冲相位测量  

Dense-ID-U-Net: Encoder-Decoder Networks for Self-Referenced Spectral Interferometry

作　　者：况琪 申雄[2] 徐艺林 白丽华[1] 刘军[2,3] Kuang Qi;Shen Xiong;Xu Yilin;Bai Lihua;Liu Jun(Department of Physics,Shanghai University,Shanghai 200444,China;State Key Laboratory of High Field Laser Physics,Shanghai Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Shanghai 201800,China;Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]上海大学物理系,上海200444 [2]中国科学院上海光学精密机械研究所强场激光物理国家重点实验室,上海201800 [3]中国科学院大学材料科学与光电工程中心,北京100049

出　　处：《中国激光》2022年第9期39-51,共13页Chinese Journal of Lasers

基　　金：国家自然科学基金(61527821,61521093);上海市自然科学基金(18ZR1413600)。

摘　　要：超快激光脉冲形状宽度测量的核心是光谱相位的精确测量。本文提出了一种结合深度学习的自参考光谱干涉(SRSI)方法,并用该方法进行了飞秒脉冲相位的测量。该方法基于针对一维信号的Dense-1D-U-Net神经网络,采用经典的编码-解码网络结构并加入稠密连接和跳跃连接来提高网络的性能。结合SRSI法的特点,本文设计出结合了稠密连接块的Dense-1D-U-Net神经网络。基于大量接近真实光谱相位的模拟光谱相位数据可以发现,基于Dense-1D-U-Net的SRSI算法的计算结果的均方根误差相比传统SRSI算法至少降低一个数量级。与有无稠密连接、跳跃连接的对照组神经网络进行对比,分析了Dense-1D-U-Net的优势。最后用实验测量数据验证了使用模拟数据训练的Dense-1D-U-Net具有计算实验数据的能力。Dense-1D-U-Net神经网络未来可以拓展应用到超快光谱等其他一维信息研究领域。Objective Ultrashort laser pulses have been widely used as essential tools in many scientific research fields,such as ultrahigh intense laser physics,ultrafast spectroscopy,and nonlinear optical microscopy.The key aspect of measuring the temporal profile of an ultrafast laser pulse is the accurate characterization of its spectral phase.Selfreferenced spectral interferometry(SRSI)is a relatively new characterization technique for measuring the intensity and phase of ultrashort laser pulses with attractive capacity introduced in 2010.SRSI is an analytical,sensitive,accurate,and fast method.The development of SRSI in recent years is to simplify the setup,optimize the reference pulse,or adapt to different situations.However,SRSI still uses the initially proposed algorithm,Fourier transform spectral interferometry(FTSI),based on spectral interferometry and few iterations.Thus,the approximate calculation is used in this algorithm to simplify the calculation process,leading to the loss of some details,and the calculation accuracy is not sufficiently high.Therefore,research on new algorithms that can improve the measurement performance and accuracy of SRSI is of great significance to promote the development of SRSI technology and ultrafast laser technology.With the rapid improvement of computer computing power,deep learning has recently achieved great success.This study proposes a deep learning method using a neural network called Dense-ID-U-Net used for one-dimensional signal processing to measure spectral phases of femtosecond pulses with the SRSI method.Furthermore,on our simulated datasets,the measurement of spectral phase accuracy using Dense-lD-U-Net is at least about one order of magnitude improved than that of the traditional SRSI algorithm.Additionally,measured data are used to verify that Dense-ID-U-Net,trained by simulated data,can calculate experimental data.Methods A one-dimensional U-Net neural network structure combined with self-designed dense blocks,called Dense-ID-U-Net(Fig.2),was designed for one-dimen

关 键 词：测量 深度学习 编码-解码 自参考光谱干涉 神经网络 稠密连接 

分 类 号：O346[理学—固体力学] TP183[理学—力学]