Broadband Content-Adaptive Moir\'e Meta-spectrometer

Abstract

Optical spectroscopy underpins material characterization, chemical sensing, and astronomy, but conventional instruments face a rigid trade-off between footprint, spectral range, and resolution. We demonstrate a content-adaptive spectrometer that overcomes this by co-designing dispersive Moir\'e meta-optics with a recursive sampling algorithm. Instead of using Moir\'e metalenses solely for varifocal tuning, we harness the strong chromatic aberration arising from phase-wrapping in their subwavelength metasurface architecture. This hyperchromaticity enables a deterministic, one-to-one mapping between the metasurfaces' mutual rotation angle and the sharply focused wavelength, repurposing the pair as a high-resolution spectral scanner. To accelerate data acquisition, we introduce a content-adaptive recursive sampling protocol that exploits the structural sparsity of physical spectra: a fast coarse sweep identifies high-information regions, followed by successively finer angular refinement only where needed. Using a laboratory prototype spanning 405-980 nm, we reconstruct diverse spectra -- from smooth broadband to sparse multi-line laser emissions -- with nearly 3x fewer measurements on average at matched fidelity (up to 7x for sparse line spectra), achieving 30 dB reconstruction 6.7x faster than conventional uniform sampling. This establishes a framework for intelligent, task-adaptive meta-optical sensors that tightly integrate physical dispersion with computational signal processing for real-time spectrometry.