AI-enabled universal image-spectrum fusion spectroscopy based on self-supervised plasma modeling  

作　　者：Feiyu Guan Yuanchao Liu Xuechen Niu Weihua Huang Wei Li Peichao Zheng Deng Zhang Gang Xu Lianbo Guo 

机构地区：[1]Huazhong University of Science and Technology,Wuhan National Laboratory for Optoelectronics,Wuhan,China [2]City University of Hong Kong,Department of Physics,Hong Kong,China [3]Chongqing University of Posts and Telecommunications,School of Optoelectronic Engineering,Chongqing,China [4]Nanjing Normal University,School of Computer and Electronic Information,Nanjing,China [5]Huazhong University of Science and Technology,School of Optical and Electronic Information,Wuhan,China

出　　处：《Advanced Photonics Nexus》2024年第6期127-139,共13页先进光子学通讯(英文)

基　　金：supported by the National Key Research and Development Program of China(Grant No.2022YFE0118700);the National Natural Science Foundation of China(Grant No.62375101);the Fundamental Research Funds for the Central Universities(Grant No.YCJJ20230216).

摘　　要：Spectroscopy,especially for plasma spectroscopy,provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability.Artificial intelligence(AI)has the tremendous potential to build a universal quantitative framework covering all branches of plasma spectroscopy based on its unmatched representation and generalization ability.Herein,we introduce an AI-based unified method called self-supervised image-spectrum twin information fusion detection(SISTIFD)to collect twin co-occurrence signals of the plasma and to intelligently predict the physical parameters for improving the performances of all plasma spectroscopic techniques.It can fuse the spectra and plasma images in synchronization,derive the plasma parameters(total number density,plasma temperature,electron density,and other implicit factors),and provide accurate results.The experimental data demonstrate their excellent utility and capacity,with a reduction of 98%in evaluation indices(root mean square error,relative standard deviation,etc.)and an analysis frequency of 143 Hz(much faster than the mainstream detection frame rate of 1 Hz).In addition,as a completely end-to-end and self-supervised framework,the SISTIFD enables automatic detection without manual preprocessing or intervention.With these advantages,it has remarkably enhanced various plasma spectroscopic techniques with state-of-the-art performance and unsealed their possibility in industry,especially in the regions that require both capability and efficiency.This scheme brings new inspiration to the whole field of plasma spectroscopy and enables in situ analysis with a real-world scenario of high throughput,cross-interference,various analyte complexity,and diverse applications.

关 键 词：LASERS plasma spectroscopy self-supervised learning plasma information fusion AI-enabled plasma modeling 

分 类 号：Z89[文化科学]