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The increasing demand for optical technologies with dynamic spectral control has driven interest in chromogenic materials, particularly for applications in tunable infrared metasurfaces. Phase-change materials such as vanadium dioxide and germanium–antimony–tellurium, for instance, have been widely used in the infrared regime. However, their reliance on thermal and electrical tuning introduces challenges such as high power consumption, limited emissivity tuning, and slow modulation speeds. Photochromic materials may offer an alternative approach to dynamic infrared metasurfaces, potentially overcoming these limitations through rapid, light-induced changes in their optical properties. This manuscript explores the potential of thiazolothiazole-embedded polymers, known for their reversible photochromic transitions and strong infrared absorption changes, for use in tunable infrared metasurfaces. The material exhibits low absorption and a strong photochromic contrast in the spectral range from 1500 cm−1 to 1700 cm−1, making it suitable for dynamic infrared light control. This manuscript reports on infrared imaging experiments demonstrating the photochromic contrast in thiazolothiazole-embedded polymer, and thereby provides compelling evidence for its potential applications in dynamic infrared metasurfaces. 

Spectral analysis of light is one of the oldest and most versatile scientific methods and the basis of countless techniques and instruments. Miniaturized spectrometers have recently seen great advances, but challenges remain before they are widely deployed. We report an integrated photonic spectrometer that achieves high performance with minimal component complexity by combining imaging of light propagation patterns in multi-mode interference waveguides with machine learning analysis. We demonstrate broadband operation in the visible and near-infrared, 0.05 nm spectral resolution, and an array of four spectrometers on a single chip. Two canonical applications are implemented: spectral analysis of the solar spectrum with neural network reconstruction and detection of Rayleigh scattering from microbeads on an optofluidic chip using principal component classification. These results illustrate the potential of this approach for high-performance spectroscopy across disciplines. 

Chip-scale integrated imaging spectrometers show significant potential for high-performance spectral analysis due to advancements in fabrication and computational techniques. Many practical applications, such as astronomy and molecular spectroscopy, require analyzing light at sub-nanowatt levels, where inherent enhancement in spectrometer signals can reduce the need for expensive photodetectors or long integration time. Previously, we introduced an integrated spectrometer scheme using machine learning to reconstruct spectra from imaging the wavelength-dependent patterns scattered out of a multimode interference (MMI) waveguide. In this work, we report a signal enhancement of 13.6 dB and an increase of device sensitivity and dynamic range by 15 dB by selective roughening of the waveguide surface via plasma etching. By imaging interference patterns at various points along the waveguide, we determine that the best spectrometer performance is achieved by imaging MMI sections with highest pattern variation. We report accurate spectral measurements using convolutional neural network-based spectral reconstruction with 1 nm resolution at input powers as low as 300 pW for the present experimental configuration, and a scattering coefficient of 1.109 cm^-1from the etched section.