Traitements chimiques du Ge en vue de sa passivation, de son reconditionnement et de la réutilisation de substrats pour la fabricationde nanomembranes de Ge
Bibliographic record
Abstract
<div> Chemical treatment of germanium surfaces for passivation, reconditioning and substrate reuse Photovoltaic (PV) energy is playing an increasingly significant role in the energy mix. However, challenges such as production intermittency restrict its widespread use. Space-Based Solar Power (SBSP) emerges as a potential complementary alternative to terrestrial PV, harnessing solar energy through satellites equipped with solar cells. Among solar technologies, multijunction cells (MJSCs) based on germanium (Ge) substrates and III-V materials stand out for their efficiency. However, the use of Ge limits their exploitation due to its scarcity and high cost. This thesis focuses on the "Porous germanium Efficient Epitaxial LayEr Release" (PEELER) technology to create germanium nanomembranes (NMs) by porosifying the Ge substrate and epitaxial growth, enabling the reduction of Ge usage and production costs for these cells. Two main issues are addressed in this thesis: surface preparation for Ge membrane passivation and the reuse of the parent substrate after the initial manufacturing cycle. The thesis explores the influence of HF and HCl-based acid cleans on the chemical composition and band structure of the Ge surface. Results show that these cleans induce significant modifications in the band curvature of the Ge surface, resulting in positive band curvatures post-treatment and the removal of surface oxides. Additionally, this work contributes to a better understanding of surface oxidation phenomena related to Ge surface exposure to ambient air and their impact on passivation quality. These findings are crucial for understanding surface passivation phenomena and improving the lifetime of minority carriers in Ge. Furthermore, the thesis aims to develop a chemical reconditioning approach for substrates after membrane detachment within the PEELER framework. An etching solution based on HF, H 2 O 2 , and water proves effective in removing broken pillar residues left by mechanical membrane detachment and reducing the surface roughness of the substrate, enabling its reuse for a new manufacturing cycle. Moreover, the thesis demonstrates the multiple reuse of these substrates after reconditioning, enabling a cycling of the PEELER approach while allowing the sequential production of Ge membranes for MJSC development. This approach offers an industrializable and cost-effective alternative to chemical-mechanical polishing (CMP), leading to cost reduction and a decrease in the amount of Ge used in solar cell manufacturing. </div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".