Hole mobility in compressively strained germanium on silicon exceeds 7 × 106 cm2V-1s−1
Bibliographic record
Abstract
The concurrent achievement of the record-low resistance charge transport and compatibility with spin qubit technology in solid-state materials is a critical milestone for advancing high-speed, energy-efficient classical and quantum electronics technologies. Here, we demonstrate that holes, the positively charged counterparts of electrons, can propagate with exceptional ease in a nanometres-thin compressively strained germanium layer epitaxially grown on a silicon substrate. Through precise material engineering, we achieve a record-breaking hole mobility of 7.15 × 10 6 cm 2 V -1 s −1 at a density of 1.7 × 10 11 cm −2 , establishing a new benchmark for hole transport in group-IV semiconductor materials, importantly, epitaxially grown on a silicon substrate. Our work outlines a design strategy for realising an ultra-clean, low-dimensional system that confines highly mobile holes within a quantum well, while maintaining excellent electrostatic tunability. Crucially, the observed high hole mobility is achieved in gated Hall-bar devices, demonstrating their practical viability for scalable cryogenic classical and quantum electronics applications. These findings unlock new opportunities for a high-performance semiconductor platform capable of underpinning the next generation of quantum information processing, cloud data centres, AI-driven technologies and energy-efficient electronics.
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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".