Affiliation:
1. Chinese Academy of Sciences
2. Hangzhou Dianzi University
3. Zhejiang University
4. Zhejiang University City College
5. Zhejiang Laboratory
Abstract
Taking advantage of broad response range and snap-shot operation mode, reconstructive spectrometers based on integrated frequency-modulation microstructure and computational techniques attract lots of attention. The key problems in reconstruction are sparse samplings related with the limited detectors and generalization ability due to data-driving principle. Here, we demonstrate abstractly a mid-infrared micro-spectrometer covering 2.5–5 μm, which utilizes a grating-integrated lead selenide detector array for sampling and a hierarchal residual convolutional neural network (HRCNN) for reconstructions. Leveraging data augmentation and the powerful feature extraction ability of HRCNN, a spectral resolution of 15 nm is realized. Over one hundred chemicals, including untrained chemicals species tested with an average reconstruction error of ∼1E-4, exhibit the excellent reliability of the micro-spectrometer. The demonstration of the micro-spectrometer promotes the development of the reconstructed strategy.
Funder
National Natural Science Foundation of China
Zhejiang Provincial Natural Science Foundation of China
Hangzhou Key Research and Development Program
Hangzhou Science and Technology Bureau
National Science and Technology Key Laboratory Foundation
China Postdoctoral Science Foundation
Research Funds of Hangzhou Institute for Advanced Study
Subject
Atomic and Molecular Physics, and Optics