Nuclear Spin‐Depleted, Isotopically Enriched <sup>70</sup>Ge/<sup>28</sup>Si<sup>70</sup>Ge Quantum Wells
Bibliographic record
Abstract
Abstract The p‐symmetry of the hole wavefunction is associated with a weaker hyperfine interaction, which makes hole spin qubits attractive candidates to implement quantum processors. However, recent studies demonstrate that hole qubits are still very sensitive to nuclear spin bath, thus highlighting the need for nuclear spin‐free germanium (Ge) qubits to suppress this decoherence channel. Herein, this work demonstrates the epitaxial growth of 73Ge‐ and 29Si‐depleted, isotopically enriched 70Ge/silicon‐germanium (SiGe) quantum wells. The growth is achieved by reduced pressure chemical vapor deposition using isotopically purified monogermane 70GeH4 and monosilane 28SiH4 with an isotopic purity higher than 99.9% and 99.99%, respectively. The quantum wells consist of a series of 70Ge/SiGe heterostructures grown on Si wafers. The isotopic purity is investigated using atom probe tomography (APT) following an analytical procedure addressing the discrepancies caused by the overlap of isotope peaks in mass spectra. The nuclear spin background is found to be sensitive to the growth conditions with the lowest concentration of 73Ge and 29Si is below 0.01% in the Ge well and SiGe barriers. The measured average distance between nuclear spins reaches 3–4 nm in 70Ge/28Si70Ge, which is an order of magnitude larger than in natural Ge/SiGe heterostructures. The spread of the hole wavefunction and the residual nuclear spin background in APT voluminals comparable to the size of realistic quantum dots are also discussed.
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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".