Deep Learning-Based Miniaturized All-Dielectric Ultracompact Film Spectrometer

Abstract

Conventional benchtop spectrometers with bulky dispersive optics and long optical path lengths display limitations where the significance of miniaturization, real-time detection, and low cost transcend the ultrafine resolution and wide spectral range. Here, we demonstrate a miniaturized all-dielectric ultracompact film spectrometer based on deep learning working in the single-shot mode. The scheme employs 16 spectral encoders with simple five-layer film stacks where merely the thickness of the intermediate high-index modulation layer is varied to realize unique encoded transmission spectra. Structural parameters as well as transmission spectra of the filters are predesigned to guarantee weak correlation and highly efficient encoding. Leveraging a trained reconstruction network, the absolute spectra of various nonluminous samples are successfully reconstructed excluding the emitting spectrum of the light source and the spectral response of the detector. The remarkable reconstructed spectral imaging result for the color board is presented and the reconstructed spectra match well with the measured ones for different patches using the identical network. We utilized the least number of spectral encoders ever since to guarantee efficient encoding, along with the single thickness-variant modulation layer, which shows potential for mass, rapid, large-area production by combining deposition with nanoimprint. Instead of the synthetic Gaussian line shape spectra, a training dataset composed of diverse spectrum types is adopted to achieve fine generalization of the trained reconstruction network. In addition, by retraining the neural network, the reconstruction network is modified to fit for the actual filter functions of the spectral encoders, thus better reconstruction performance. The proposed miniaturized spectrometer has great prospects in the fields of consumer electronics, environmental monitoring, and disaster prevention.