Injection of spin current in semiconductor nanostructures by infrared optical processes
Bibliographic record
Abstract
A controllable delivery of spins in nanodevices is required for applications in spintronics technologies. A pure spin current, in which oppositely oriented spins move in opposite directions, is a phenomenon that could be used for this purpose. Various optical techniques can efficiently excite such spin currents in bulk semiconductors and nanostructures. We here propose and analyze two new optical infrared-light techniques for the injection of a pure spin current in nanostructures. The techniques are based on the intersubband light absorption (one-photon process) and stimulated Raman scattering (two-photon process). The infrared light absorption deposits approximately 100 meV for each absorption event associated with current injection. In the spin-flip Raman process which is possible due to spin-orbit (SO) coupling, the corresponding energy transfer to the system, is on the order of 1 meV. The stimulated Raman process depends on the electron momentum, and therefore, electrons with different spins can be launched in different directions. The infrared-injected pure spin currents can be engineered by changing the Rashba spin-orbit coupling using an external bias across the quantum well. The injected spin current should be detectable by pump-probe optical spectroscopy, and thus points the way toward the design of full-optical write-and-read spintronics devices.
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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) | 0.000 | 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 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".