Mid-infrared optical spin injection and coherent control
Bibliographic record
Abstract
The optical injection of charge and spin currents is investigated in ${\mathrm{Ge}}_{1\ensuremath{-}x}{\mathrm{Sn}}_{x}$ semiconductors as a function of Sn content. These emerging silicon-compatible materials enable the modulation of these processes across the entire mid-infrared range. Under the independent-particle approximation, the one- and two-photon interband absorption processes are elucidated, and the evolution of the coherent control is discussed for three different polarization configurations. To evaluate the contribution of high-energy transitions, a full-zone 30-band $\mathrm{k}\ifmmode\cdot\else\textperiodcentered\fi{}\mathrm{p}$ is employed in the calculations. It was found that, besides the anticipated narrowing of the direct gap and the associated shift of the absorption to longer wavelengths, incorporating Sn in Ge also increases the one-photon degree of spin polarization (DSP) at the ${E}_{1}$ resonance. Moreover, as the Sn content increases, the magnitude of the response tensors near the band edge exhibits an exponential enhancement. This behavior can be attributed to the Sn-incorporation-induced decrease in the carrier effective masses. This trend appears to hold also at the ${E}_{1}$ resonance for pure spin current injection, at least at low Sn compositions. The two-photon DSP at the band edge exceeds the value in Ge to reach 60% at a Sn content above 14%. These results demonstrate that ${\mathrm{Ge}}_{1\ensuremath{-}x}{\mathrm{Sn}}_{x}$ semiconductors can be exploited to achieve the quantum coherent manipulation in the molecular fingerprint region relevant to quantum sensing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".