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

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. © The Authors. Published by SPIE and CLP under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.