Breaking the resolution-bandwidth limit of chip-scale spectrometry by harnessing a dispersion-engineered photonic molecule

Abstract

The chip-scale integration of optical spectrometers may offer new opportunities for in situ bio-chemical analysis, remote sensing, and intelligent health care. The miniaturization of integrated spectrometers faces the challenge of an inherent trade-off between spectral resolutions and working bandwidths. Typically, a high resolution requires long optical paths, which in turn reduces the free-spectral range (FSR). In this paper, we propose and demonstrate a ground-breaking spectrometer design beyond the resolution-bandwidth limit. We tailor the dispersion of mode splitting in a photonic molecule to identify the spectral information at different FSRs. When tuning over a single FSR, each wavelength channel is encoded with a unique scanning trace, which enables the decorrelation over the whole bandwidth spanning multiple FSRs. Fourier analysis reveals that each left singular vector of the transmission matrix is mapped to a unique frequency component of the recorded output signal with a high sideband suppression ratio. Thus, unknown input spectra can be retrieved by solving a linear inverse problem with iterative optimizations. Experimental results demonstrate that this approach can resolve any arbitrary spectra with discrete, continuous, or hybrid features. An ultrahigh resolution of <40 pm is achieved throughout an ultrabroad bandwidth of >100 nm far exceeding the narrow FSR. An ultralarge wavelength-channel capacity of 2501 is supported by a single spatial channel within an ultrasmall footprint (≈60 × 60 μm2), which represents, to the best of our knowledge, the highest channel-to-footprint ratio (≈0.69 μm−2) and spectral-to-spatial ratio (>2501) ever demonstrated to date.

Integrated Microring Spectrometer with In‐Hardware Compressed Sensing to Break the Resolution‐Bandwidth Limit for General Continuous Spectrum Analysis

“…Dual MRR [32] 0.04 100 2501 1 2501 Active 60×60 μm 2 Sparse 2D disordered microring lattice [33] 0.015 nm 40 nm 2667 4096 0.65 Passive 1000×1000 μm 2 -Multimode spiral Waveguide [34] 0 of the random grating is nearly orthogonal, the sparsity limit approaches the number of random gratings M. It can always attain a larger spectral bandwidth consisting of more resonant peaks by simply using more random gratings, which does not increase the reconstruction error (Figure S4d in Note S3, Supporting Information). Therefore, to effectively increase spectral channels, we can independently enhance the spectral resolution (using a higher quality-factor MRR) and the spectral bandwidth (via a larger FSR, tuning range, and sparsity).…”

“…A complex spectrum requires a complex reconstruction model such as multifeature reconstruction via a modified regularization method with more weight coefficients and more computation power. [28,32] Moreover, the reconstructive error of reconstructive spectrometers increases as the spectrum's complexity. Therefore, it is desired for the reconstructive spectrometers to recover particular and simple spectra by CS method in software.…”

“…Moreover, there is an inherent dilemma of balancing power consumption and device footprint. In the past decade, a new type of spectrometer based on computational spectral reconstruction has emerged, [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] since it breaks through the design limitation of conventional spectrometers. Unlike conventional spectrometers based on dispersive optics and narrowband filters by spectrum-to-space mapping principle (one frequency mapped to one spatial position), reconstructive spectrometers simultaneously address a full range of spectral components at each detector, which has a robust performance to fabrication imperfections.…”

Advancing multi-dimensional vision: AI-driven imaging using unique photodetectors with integrated surface nanostructures

“…where 𝑅 ! (𝜆) represents the spectral response of the i-th photodiode in the array [22]. The goal of the reconstruction problem is to solve 𝐹(𝜆) from equation (1).…”

“…Furthermore, nanostructures have unique coupling properties with different wavelengths of light, which enables them to reproducibly fabricate detectors with distinct responsivities [18], [19]. This, combined with AI's power, allows hyperspectral imaging possible on a chip-size, low-cost, deployable fashion [20]- [22]. With further development, these technologies could reduce costs and elevate imaging capabilities across scientific, commercial, and consumer applications [23].…”