A computational spectrometer for the visible, near, and mid-infrared enabled by a single-spinning film encoder

• Published in: Communications engineering Vol. 4; no. 1; pp. 37 - 11
• Main Authors: Wen, Junren, Shi, Weiming, Gao, Cheng, Liu, Yujie, Feng, Shuaibo, Shao, Yu, Gao, Haiqi, Shao, Yuchuan, Zhang, Yueguang, Shen, Weidong, Yang, Chenying
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.02.2025; Springer Nature B.V; Nature Portfolio
• Subjects: 639/166; 639/624; Algorithms; Chemical compounds; Classification; Coders; Deep learning; Engineering; Fourier transforms; Infrared radiation; Light; Machine learning; Manufacturing; Narrowband; Neural networks; Particle swarm optimization; Reconstruction; Sensors; Spectrometers; Spectrum analysis; Unmanned aerial vehicles
• ISSN: 2731-3395; 2731-3395
• DOI: 10.1038/s44172-025-00379-5

Computational spectrometers enable low-cost, in-situ, and rapid spectral analysis, with applications in chemistry, biology, and environmental science. Traditional filter-based spectral encoding approaches typically use filter arrays, complicating the manufacturing process and hindering device consistency. Here we propose a computational spectrometer spanning visible to mid-infrared by combining the Single-Spinning Film Encoder (SSFE) with a deep learning-based reconstruction algorithm. Optimization through particle swarm optimization (PSO) allows for low-correlation and high-complexity spectral responses under different polarizations and spinning angles. The spectrometer demonstrates single-peak resolutions of 0.5 nm, 2 nm, 10 nm, and dual-peak resolutions of 3 nm, 6 nm, 20 nm for the visible, near, and mid-infrared wavelength ranges. Experimentally, it shows an average MSE of 1.05 × 10⁻³ for narrowband spectral reconstruction in the visible wavelength range, with average center-wavelength and linewidth errors of 0.61 nm and 0.56 nm. Additionally, it achieves an overall 81.38% precision for the classification of 220 chemical compounds, showcasing its potential for compact, cost-effective spectroscopic solutions. Junren Wen, Weiming Shi and colleagues propose a computational spectrometer spanning visible to mid-infrared by integrating a Single-Spinning Film Encoder with deep learning-based reconstruction. This approach enables high-resolution spectral analysis and accurate chemical classification.