General-purpose mid-infrared micro-spectrometer based on hierarchical residual CNN and data augmentation

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.