A Light‐Hole Germanium Quantum Well on Silicon
Bibliographic record
Abstract
The quiet quantum environment of holes in solid-state devices is at the core of increasingly reliable architectures for quantum processors and memories. However, due to the lack of scalable materials to properly tailor the valence band character and its energy offsets, the precise engineering of light-hole (LH) states remains a serious obstacle toward coherent optical photon-spin interfaces needed for a direct mapping of the quantum information encoded in photon flying qubits to stationary spin processors. Herein, to alleviate this long-standing limitation, an all-group-IV low-dimensional system is demonstrated, consisting of a highly tensile strained germanium quantum well grown on silicon allowing new degrees of freedom to control and manipulate the hole states. Wafer-level, high bi-isotropic in-plane tensile strain (<1%) is achieved using strain-engineered, metastable germanium-tin alloyed buffer layers yielding quantum wells with LH ground state, high g-factor anisotropy, and a tunable splitting of the hole sub-bands. The epitaxial heterostructures display sharp interfaces with sub-nanometer broadening and show room-temperature excitonic transitions that are modulated and extended to the mid-wave infrared by controlling strain and thickness. This ability to engineer quantum structures with LH selective confinement and controllable optical response enables manufacturable silicon-compatible platforms relevant to integrated quantum communication and sensing technologies.
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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".