Miniaturizing a Chip-Scale Spectrometer Using Local Strain Engineering and Total-Variation Regularized Reconstruction

CMOS-compatible reconstructive spectrometers with self-referencing integrated Fabry-Perot resonators

Miniaturized reconstructive spectrometers play a pivotal role in on-chip and portable devices, offering high-resolution spectral measurement through precalibrated spectral responses and AI-driven reconstruction. However, two key challenges persist for practical applications: artificial intervention in algorithm parameters and compatibility with complementary metal-oxide-semiconductor (CMOS) manufacturing. We present a cutting-edge miniaturized reconstructive spectrometer that incorporates a self-adaptive algorithm referenced with Fabry-Perot resonators, delivering precise spectral tests across the visible range. The spectrometers are fabricated with CMOS technology at the wafer scale, achieving a resolution of ~2.5 nm, an average wavelength deviation of ~0.27 nm, and a resolution-to-bandwidth ratio of ~0.46%. Our approach provides a path toward versatile and robust reconstructive miniaturized spectrometers and facilitates their commercialization.

Computational spectrometer based on local feature-weighted spectral reconstruction

The computational spectrometer enables the reconstruction of spectra from precalibrated information encoded. In the last decade, it has emerged as an integrated and low-cost paradigm with vast potential for applications, especially in portable or handheld spectral analysis devices. The conventional methods utilize a local-weighted strategy in feature spaces. These methods overlook the fact that the coefficients of important features could be too large to reflect differences in more detailed feature spaces during calculations. In this work, we report a local feature-weighted spectral reconstruction (LFWSR) method, and construct a high-accuracy computational spectrometer. Different from existing methods, the reported method learns a spectral dictionary via L ₄-norm maximization for representing spectral curve features, and considers the statistical ranking of features. According to the ranking, weight features and update coefficients then calculate the similarity. What's more, the inverse distance weighted is utilized to pick samples and weight a local training set. Finally, the final spectrum is reconstructed utilizing the local training set and measurements. Experiments indicate that the reported method's two weighting processes produce state-of-the-art high accuracy.