Wafer-scale detachable monocrystalline germanium nanomembranes for the growth of III–V materials and substrate reuse
Bibliographic record
Abstract
Germanium (Ge) is increasingly used as a substrate for high-performance optoelectronics, photovoltaics, and electronic devices. These devices are usually grown on thick and rigid Ge substrates manufactured by classical wafering techniques. Nanomembranes (NMs) provide an alternative to this approach while offering wafer-scale lateral dimensions, weight reduction, waste limitation, and cost effectiveness. Herein, we introduce the Porous germanium Efficient Epitaxial LayEr Release (PEELER) process, which consists of the fabrication of wafer-scale detachable Ge NMs on porous Ge (PGe) and substrate reuse. We demonstrate the growth of Ge NMs with monocrystalline quality as revealed by high-resolution transmission electron microscopy (HRTEM) characterization. Together with the surface roughness below 1 nm, it makes the Ge NMs suitable for growth of III-V materials. Additionally, the embedded nanoengineered weak layer enables the detachment of the Ge NMs. Finally, we demonstrate the wet-etch-reconditioning process of the Ge substrate, allowing its reuse, to produce multiple free-standing NMs from a single parent wafer. The PEELER process significantly reduces the consumption of Ge in the fabrication process, paving the way for a new generation of low-cost flexible optoelectronic 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".