Post-growth tuning of detachable Ge membranes adhesion strength via porous Ge transformation
Bibliographic record
Abstract
Single crystal germanium (Ge) membranes have recently gained increasing interest for lightweight and low-cost solar cells and flexible optoelectronic devices. These membranes achieved similar material quality as bulk Ge substrate. However, the control of the membrane detachment is still challenging. In this work, we explore post-growth engineering of the adhesion strength of a Ge membrane on a porous germanium (PGe) substrate by inducing morphological transformations in the separation layer through Thermal Budget (TB) control. Indeed, the pillars formed through PGe sintering during epitaxy are found to evolve with post-growth thermal annealing. Scanning electron microscopy (SEM) based analysis of the residue of the post-detachment broken pillars has been performed showing that the pillar's diameter and density can be tuned by thermal annealing. Depending on the post-growth annealing temperature, the membrane adhesion strength can be successively tailored from 0.5 to up to 3.5 MPa while ensuring 100 % detachment yield. The experimental results have been correlated with Finite Element Modeling (FEM) considering realistic pillar distribution revealing that pillar size and density are the dominant factors influencing the membrane adhesion strength.
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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".