Extended Short-Wave Infrared Absorption in Group-IV Nanowire Arrays
Bibliographic record
Abstract
Engineering light absorption in the extended short-wave infrared (ESWIR) range using scalable materials is a long-sought-after capability that is crucial to implement cost-effective and high-performance sensing and imaging technologies. Herein, we demonstrate enhanced, tunable ESWIR absorption using silicon-integrated platforms consisting of ordered arrays of metastable ${\mathrm{Ge}}_{1\ensuremath{-}x}{\mathrm{Sn}}_{x}$ nanowires with Sn content reaching 9 at.% and variable diameters. Detailed simulations are combined with experimental analyses to systematically investigate light\ensuremath{-}${\mathrm{Ge}}_{1\ensuremath{-}x}{\mathrm{Sn}}_{x}$ nanowire interactions to tailor and optimize the nanowire-array geometrical parameters and the corresponding optical response. The diameter-dependent leaky-mode resonance peaks are theoretically predicted and experimentally confirmed with a tunable wavelength from 1.5 to 2.2 \textmu{}m. A threefold enhancement in the absorption with respect to ${\mathrm{Ge}}_{1\ensuremath{-}x}{\mathrm{Sn}}_{x}$ layers at 2.1 \textmu{}m is achieved using nanowires with a diameter of 325 nm. Finite-difference time-domain simulations unravel the underlying mechanisms of the ESWIR-enhanced absorption. The coupling of the ${\mathrm{HE}}_{11}$ and ${\mathrm{HE}}_{12}$ resonant modes to nanowires is observed at diameters above 325 nm, while at smaller diameters and longer wavelengths the ${\mathrm{HE}}_{11}$ mode is guided into the underlying Ge layer. The presence of tapering in nanowires further extends the absorption range while minimizing reflection. This ability to engineer and enhance ESWIR absorption lays the groundwork to implement alternative photonic devices exploiting all-group-IV platforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".