Tuning Thermal Induced Porous-Ge Reconstruction for Layer Transfer and Substrate Re-use
Bibliographic record
Abstract
Owing to their high efficiency, and heat and radiation resistance, III-V semiconductor multi-junction solar cells are dominating the space PV market. However, a lot of work has still to be done in terms of mass and cost reduction. Accordingly, reliable reduction of the substrate thickness can be obtained by solar cell detachment and substrate reuse allowing reducing both solar cells' weight. and cost. The use of porous germanium as weak layer for solar cell detachment is one of the most promising approaches ensuring scalability and cost-effectiveness. In seek of Ge substrate design providing both epitaxial seed layer and voided weak layer underneath suitable for III-V materials growth and subsequent detachment, we provide systematic investigation of thermal induced reorganization of porous germanium with various porosity levels and thicknesses. Indeed, high porosity structure shows fast reconstruction rate with increasing the thermal budget testifying its aptitude to form controllable voided separation layer. Meanwhile, low porosity structure’ reconstruction is found to be mediated by pores transformation to faceted small voids, giving rise to monocrystalline material with stable thickness potentially useful as a template for epitaxial growth. Epitaxial template on weak layer design with tunable morphological and mechanical properties has been fabricated by considering a structure with gradual low porosity on top to high porosity in depth. Almost non-porous, suspended thin Ge layer connected to the bulk substrate trough pillars of few tenths of nm in diameter with micrometer scale spacing, has been successfully demonstrated. Our results show that Ge layer with gradual porosity constitute a viable approach for solar cell detachment offering tunable properties depending on the porous layers thicknesses and porosity.
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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".