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

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 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 (asymptotic to 60 x 60 mu m(2)), which represents, to the best of our knowledge, the highest channel-to-footprint ratio (asymptotic to 0.69 mu m(-2)) and spectral-to-spatial ratio (> 2501) ever demonstrated to date.

Automated summary

This paper presents a spectrometer design that surpasses the resolution-bandwidth limit, by tailoring the dispersion of mode splitting in a photonic molecule to identify spectral information at different free-spectral ranges (FSRs). Experimental results demonstrate that this approach can resolve arbitrary spectra with discrete, continuous, or hybrid features.