Sustainable Production of Ultrathin Ge Freestanding Membranes
Bibliographic record
Abstract
Germanium (Ge) is a critical material for applications in space solar cells, integrated photonics, infrared imaging, sensing, and photodetectors. However, the corresponding cost and limited availability hinder its potential for widespread applications. However, using Ge freestanding membranes (FSMs) allows for a significant reduction in the material consumption during device fabrication while offering additional advantages such as lightweight and flexible form factor for novel applications. In this work, we present the Ge FSM production process involving sequential porous Ge (PGe) structure formation, Ge membrane epitaxial growth, detachment, substrate cleaning, and subsequent reuse. This process enables the fabrication of multiple high-quality monocrystalline Ge FSMs from the same substrate through efficient substrate reuse at a 100 mm wafer scale by a simple and low-cost chemical cleaning process. A uniform, high-quality PGe layer is produced on the entire recovered substrate. By circumventing the use of conventional high-cost chemical–mechanical polishing or even substantial chemical wet-etching, and by using an optimized PGe structure with reduced thickness, the developed process allows for both cost and an environmental impact reduction in Ge FSMs production, lowering the amount of Ge used per membrane fabrication. Moreover, this process employs large-scale compatible techniques paving the way for the sustainable production of group IV FSMs for next-generation flexible optoelectronics.
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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 | 0.001 |
| Research integrity | 0.001 | 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".