Enhanced Nanoscale Ge Concentration Oscillations in Si/SiGe Quantum Well through Controlled Segregation
Bibliographic record
Abstract
The integration of electron spin qubits on Si/SiGe heterostructures requires precise control of valley splitting associated with conduction band degeneracy. This can be achieved by introducing nanoscale oscillating Ge concentration profiles, known as Wiggle Wells. However, the intermixing and segregation of Ge during growth have hindered their realization. We report the growth of Si/SiGe heterostructures with clear nanoscale composition modulation within the quantum well using molecular beam epitaxy. By oscillation of the growth temperature, Ge segregation is suppressed, achieving a Ge concentration modulation of 30%/nm, an order of magnitude higher than prior results. Tight-binding simulations suggest that Wiggle Well heterostructures with sharp compositional transitions significantly enhance valley splitting, yielding average values exceeding 200 μeV, with energy levels well separated from 0 μeV. Hence, Wiggle Wells are a promising approach for Si-based electronic qubits.
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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".