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Lett."],"published-print":{"date-parts":[[2026,8,15]]},"abstract":"<jats:p>Miniaturized spectrometers often trade spectral resolution and bandwidth against footprint and reconstruction fidelity. We report a structure\u2013algorithm co-designed computational spectrometer that combines a random micropinhole diffractive encoder with hybrid physics\u2013neural reconstruction. The encoder generates wavelength-sensitive diffraction patterns with low inter-wavelength correlation. The reconstruction first obtains a nonnegativity-constrained, smoothness-regularized Tikhonov estimate and then fuses it with radial speckle features using a neural network. Experiments over the 500\u2013700\u2009nm band achieve a reconstructed full width at half maximum (FWHM) of 1.46\u2009nm for a single spectral peak and resolve doublets separated by 1.80\u2009nm. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}