A Deep Ultraviolet Mode-locked Laser Based on a Neural Network

Deep ultraviolet lasers based on the phenomenon of mode-locking have been used widely in many areas in recent years, for example, in semiconductors, the environment and biomedicine. In the development of a mode-locked deep ultraviolet laser, one of the most important aspects is to optimize the multiple parameters of the complex system. Traditional optimization methods require experimenters with more optimization experience, which limits the wide application of the lasers. In this study, we optimize the deep ultraviolet mode-locked laser system using an online neural network to solve this problem. The neural network helps us control the position of the crystal, the length of the cavity, the position of the focusing lens and the temperature of the frequency doubling crystal. We generate a deep ultraviolet mode-locked laser with a power of 18 mW and a spectral center at 205 nm. This result is greatly improved compared to previous results with the same pump power. This technology provides a universal solution to multiparameter problems in the optimization of lasers.

[1]  Chun-Hua Yan,et al.  Paradigms and challenges for bioapplication of rare earth upconversion luminescent nanoparticles: small size and tunable emission/excitation spectra. , 2014, Accounts of chemical research.

[2]  T. Hänsch,et al.  A deep-UV optical frequency comb at 205 nm. , 2009, Optics express.

[3]  Chuji Wang,et al.  Breath Analysis Using Laser Spectroscopic Techniques: Breath Biomarkers, Spectral Fingerprints, and Detection Limits , 2009, Sensors.

[4]  Robert F. Mills,et al.  Using Spectral Fingerprints to Improve Wireless Network Security , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[5]  Jianye Zhao,et al.  Erbium fiber laser-based direct frequency comb spectroscopy of Rb two-photon transitions. , 2013, Optics letters.

[6]  A. Engel,et al.  Spectral fingerprints of large-scale neuronal interactions , 2012, Nature Reviews Neuroscience.

[7]  A. Jayich,et al.  Direct frequency comb laser cooling and trapping , 2016, 1603.08053.

[8]  Jung Ho Yu,et al.  High-resolution three-photon biomedical imaging using doped ZnS nanocrystals. , 2013, Nature materials.

[9]  Tze Chien Sum,et al.  Photon Upconversion in Hetero‐nanostructured Photoanodes for Enhanced Near‐Infrared Light Harvesting , 2013, Advanced materials.

[10]  Meschede,et al.  Absolute frequency measurement of the hydrogen 1S-2S transition and a new value of the Rydberg constant. , 1992, Physical review letters.

[11]  Mads Kock Pedersen,et al.  Exploring the quantum speed limit with computer games , 2015, Nature.

[12]  A. Zvezdin,et al.  Temporal and spectral fingerprints of ultrafast all-coherent spin switching , 2019, Nature.

[13]  Alexander Maedche,et al.  Gamified crowdsourcing: Conceptualization, literature review, and future agenda , 2017, Int. J. Hum. Comput. Stud..

[14]  P. Vary,et al.  Spectral fingerprints of bacterial strains by laser-induced breakdown spectroscopy , 2004 .

[15]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[16]  B. B. Laud.  Lasers and nonlinear optics , 1985 .

[17]  Xiaomin Liu,et al.  Excitation energy migration dynamics in upconversion nanomaterials. , 2015, Chemical Society reviews.

[18]  Jun Ye,et al.  United Time-Frequency Spectroscopy for Dynamics and Global Structure , 2004, Science.

[19]  L. Kaltenegger,et al.  EFFECT OF UV RADIATION ON THE SPECTRAL FINGERPRINTS OF EARTH-LIKE PLANETS ORBITING M STARS , 2015, 1506.07202.

[20]  Feng Zhang,et al.  Sub-200 fs soliton mode-locked fiber laser based on bismuthene saturable absorber. , 2018, Optics express.

[21]  Laser spectroscopy of the 1S-2S transition in hydrogen and deuterium: Determination of the 1S Lamb shift and the Rydberg constant. , 1989, Physical review. A, General physics.

[22]  A. Alivisatos.  Semiconductor Clusters, Nanocrystals, and Quantum Dots , 1996, Science.

[23]  Salomon,et al.  Measurement of the hydrogen 1S- 2S transition frequency by phase coherent comparison with a microwave cesium fountain clock , 2000, Physical review letters.

[24]  Steven L. Brunton,et al.  Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers , 2017, ArXiv.

[25]  S. Friend,et al.  Crowdsourcing biomedical research: leveraging communities as innovation engines , 2016, Nature Reviews Genetics.

[26]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[27]  Chao Lu,et al.  Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks. , 2012, Optics express.

[28]  K. Kawase,et al.  Non-destructive terahertz imaging of illicit drugs using spectral fingerprints. , 2003, Optics express.

[29]  Kristin H. Jarman,et al.  Extracting and visualizing matrix-assisted laser desorption/ionization time-of-flight mass spectral fingerprints. , 1999, Rapid communications in mass spectrometry : RCM.

[30]  G T Campbell,et al.  Multiparameter optimisation of a magneto-optical trap using deep learning , 2018, Nature Communications.

[31]  Urs Eppelt,et al.  Improving the laser cutting process design by machine learning techniques , 2017, Prod. Eng..

[32]  W. Bian,et al.  Ultrafast Deep-Ultraviolet Laser Ionization Mass Spectrometry Applicable To Identify Phenylenediamine Isomers. , 2018, Analytical chemistry.

[33]  A. Scrinzi,et al.  Plasmon-enhanced-attosecond-extreme ultraviolet source. , 2012, Physical review letters.

[34]  E. Giannelis,et al.  Carbon dots—Emerging light emitters for bioimaging, cancer therapy and optoelectronics , 2014 .

[35]  Wei Huang,et al.  Temporal full-colour tuning through non-steady-state upconversion. , 2015, Nature nanotechnology.

[36]  S. Y. Zhang,et al.  Direct frequency comb optical frequency standard based on two-photon transitions of thermal atoms , 2015, Scientific Reports.

[37]  Xiaobo Chen,et al.  Increasing Solar Absorption for Photocatalysis with Black Hydrogenated Titanium Dioxide Nanocrystals , 2011, Science.

[38]  Min Zhang,et al.  System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm , 2017 .