Answers are grounded in this paper and cite their sources.
Demonstrates enhanced mid-infrared sensing in new photodiode design, suggesting improved accuracy in detection.
Miniaturized infrared spectrometers are vital for portable sensing, yet most single-detector computational schemes prioritize spectral encoding while overlooking weak-signal requirements in mid-infrared detection, where high absorption, low dark current, and internal gain are essential. Here, we propose and numerically validate a voltage-programmable spectral sensing concept based on a graded-bandgap HgCdTe avalanche photodiode. In this design, the composition gradient is repurposed as an active spectral-encoding coordinate. Through bias-dependent depletion, the device selectively accesses different absorption depths, while an N/P double-buffer architecture confines the high electric field within the wide-bandgap multiplication region, preserving avalanche gain while suppressing tunneling-related dark current in the absorber. Crucially, the multi-physics TCAD framework—incorporating wavelength-dependent absorption, carrier transport, tunneling, recombination, and impact ionization—is rigorously calibrated against experimental data from a baseline HgCdTe diode. These experimentally anchored simulations reveal distinct electrical fingerprints across 3–7 μm, driven by the coupled effects of absorption depth, photon flux, and wavelength-dependent avalanche path length. Compared to a conventional PIN structure, this architecture reduces dark-current density by one to two orders of magnitude at comparable gain. By processing these fingerprints with a noise-aware dual-branch neural network, discrete-band spectral reconstruction achieves a mean R2 of 0.9894 and a total-energy error of 3.21%. This work establishes a detector-physics-driven route toward filter-free, gain-assisted mid-infrared spectral sensing.
No takes yet. Share an insight, caveat, or question.
Chen et al. (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: