Epitaxial lift-off of InGaAs solar cells from InP substrate using a strained AlAs/InAlAs superlattice as a novel sacrificial layer
Bibliographic record
Abstract
In this work we present a new epitaxial lift-off (ELO) approach based on the use of a strained AlAs/InAlAs superlattice (SL) as sacrificial layer for InP related materials. Such an ELO process enables the fabrication and transfer of a thin active III-V heterostructure via its separation from its III-V parent substrate using selective chemical etching. The strategy is of particular interest for large area devices such as solar cells. The process studied here also allows the substrate reuse for a low-cost approach based on III-V-based device fabrication. In order to realize the ELO process on InP substrates, the main difficulty is the lack of lattice-matched materials offering the high chemical etching selectivity needed over both the substrate and the lattice-matched alloys of the active heterostructure. The present study therefore contributes effective strategy for overcoming the latter constraints. The AlAs/InAlAs SL was thus explored as a potential candidate as sacrificial layer for the InP lattice matched materials. The growth conditions of such SLs were investigated to produce low defect SLs compatible with the properties of an optimal sacrificial layer. The under-etching behavior of such SLs in a hydrofluoric acid-based solution was also studied in detail. The results show that advantageous under-etching rates, high enough for a full wafer detachment, combined with a low defect density, can be obtained with novel sacrificial layers based on such thin AlAs/InAlAs SL. Finally, the fabrication of solar cells via an active heterostructure grown over an optimized SL on a monolithic substrate and via a thin reported active heterostructure was performed. The solar cells perform well and demonstrate the suitability of such SLs as a sacrificial layer for InP related materials.
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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".