Fig. 1. Schematic diagram of working process of computational reconstruction spectrometer

Fig. 2. Silicon photonic computational reconstruction spectrometer based on multimode waveguides. (a) Schematic diagram of the spectrometer based on multimode helical waveguides, spectral autocorrelation function, and test results of narrowband laser spectral lines^[18]; (b) technology for expanding the spectral detection bandwidth based on cascaded optical switch arrays and test results^[19]

Fig. 3. Computational reconstruction spectrometer designed by Tsang's team. (a) Schematic diagram of computational reconstruction spectrometer based on multimode waveguides with integrated photonic lanterns, spectral autocorrelation function, and spectral reconstruction results^[20]; (b) schematic diagram of the two-stage computational reconstruction spectrometer based on multimode waveguides and spectral reconstruction results^[21]

Fig. 4. Computational reconstruction spectrometer based on disordered scattering media. (a) SEM images of the spectrometer based on disordered photonic structures, with the numerical simulation result diagram for TE polarization light at 1500 nm on the right, and the experimental result image at 1500 nm; (b) spectral correlation function; (c) reconstructed spectra for a series of narrow spectra with a bandwidth of 25 nm; (d) test results for narrowband laser spectral lines^[22]

Fig. 5. Computational reconstruction spectrometer with a disordered scattering structure based on a silicon nitride platform. (a) Transmission matrices and corresponding spectral correlation functions for the near-infrared region around 1550 nm and 900 nm, and the visible light region around 765 nm; (b) reconstructed spectra for different narrow spectra across various wavelength ranges^[24]

Fig. 6. Computational reconstruction spectrometer based on silicon-based stratified waveguide filters. (a) Schematic diagram of the device; (b) spectral reconstruction results^[25]

Fig. 7. Computational reconstruction spectrometer based on a linear coherent network. (a) Schematic diagram of the device; (b) spectral correlation function; (c) reconstruction results for 5 spectral lines within a 3.24 nm range, and reconstruction results for 2 spectral lines within a 12 nm range^[26]

Fig. 8. Mode demultiplexing spectrometer based on deep learning. (a) Schematic diagram of the mode-division multiplexing computational reconstruction spectrometer; (b) framework of the deep learning algorithms used in spectral computational reconstruction; (c) reconstruction results for dual spectral peaks separated by 3 nm, single-peak reconstruction results with a FWHM of 3 nm, and reconstruction results for randomly constructed spectra^[12]

Fig. 9. Photonic molecule-based computational reconstruction spectrometer. (a) Schematic diagram of the device; (b) spectral correlation function; (c) spectral reconstruction results^[27]

Fig. 10. Computational reconstruction spectrometer based on a silicon photonics reconfigurable network. (a) Schematic diagram of the device; (b) reconstruction results for dual spectral lines at different wavelengths; (c) reconstruction results for continuous broadband spectra^[15]

Fig. 11. Silicon photonics programmable circuit-based computational reconstruction spectrometer. (a) Microscopic image of the chip; (b) spectral correlation function; (c) reconstructed spectra for three spectral lines of different wavelength lengths (with a spectral spacing of 10 pm between two of the lines); (d) reconstruction results for a hybrid spectrum with both broadband and narrowband spectral components^[14]

Fig. 12. Computational reconstruction spectrometer based on an array of multi-point self-coupled waveguide (MPSCW) filters. (a) Schematic diagram of the device; (b) 3D schematic of the MPSCW filter; (c) spectral reconstruction results; (d) reconstruction results under different temperatures^[28]

Fig. 13. Silicon photonic computational reconstruction spectrometer based on a programmable silicon photonic filter: (a) Schematic diagram of the device structure; (b) spectral reconstruction algorithm based on deep learning; (c) spectral reconstruction simulation results: average relative error and resolution of reconstructed spectra under different number of sampling states, reconstruction results for dual spectral lines separated by 0.1 nm and 0.2 nm, and reconstruction results for ASE light source spectrum^[13]