GeSn quantum wells as a platform for spin-resolved hole transport
Bibliographic record
Abstract
The nascent group IV GeSn alloys are highly attractive for spintronics applications, including quantum computing, due to their ability to enable highly scalable fabrication and all-electrical spin manipulation. In this work, we conduct an in-depth study of a two-dimensional hole gas in a Ge/GeSn quantum well, exhibiting the integer quantum Hall effect and distinct Shubnikov-de Haas oscillations. Emphasis is given to the determination of the Landé g-factor and its pronounced anisotropy in this two-dimensional system, revealing values significantly higher than those in conventional Ge or SiGe/Ge systems. Moreover, by modeling the spin-orbit interaction using the Iordanskii-Lyanda-Geller-Pikus theory, crucial cubic Rashba spin-orbit interaction coefficients, are extracted and their significance is highlighted. This work provides the experimental validation of the theoretically predicted enhancements in spin-orbit interaction and g-factors in GeSn alloys compared to Ge. Additionally, it delivers essential parameters for the design of hole spin devices, such as hole qubits, utilizing GeSn-based structures on the Si platform. GeSn alloys hold promise for spintronics and quantum computing due to their scalable fabrication and spin manipulation capabilities. Here, the authors study a two-dimensional hole gas in a Ge/GeSn quantum well, revealing enhanced spin-orbit interactions and g-factors, providing key insights for designing GeSn-based spintronic 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) | 0.001 | 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".