  Computational spectrometers have mobile application potential, such as
on-site detection and self-diagnosis, by offering compact size, fast operation
time, high resolution, wide working range, and low-cost production. Although
these spectrometers have been extensively studied, demonstrations are confined
to a few examples of straightforward spectra. This study demonstrates deep
learning (DL)-based single-shot computational spectrometer for narrow and broad
spectra using a multilayer thin-film filter array. For measuring light
intensities, the device was built by attaching the filter array, fabricated
using a wafer-level stencil lithography process, to a complementary
metal-oxide-semiconductor image sensor. All the intensities were extracted from
a monochrome image captured with a single exposure. A DL architecture
comprising a dense layer and a U-Net backbone with residual connections was
employed for spectrum reconstruction. The measured intensities were fed into
the DL architecture for reconstruction as spectra. We reconstructed 323
continuous spectra with an average root mean squared error of 0.0288 in a
500-850 nm wavelength range with 1-nm spacing. Our computational spectrometer
achieved a compact size, fast measuring time, high resolution, and wide working
range.
