Sub-10 μm-Thick Ge Thin Film Fabrication from Bulk-Ge Substrates via a Wet Etching Method
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Low-defect density Ge thin films are crucial for studying the impact of defect density on the performance limits of Ge-based optical devices (optical detectors, LEDs, and lasers). Ge thinning is also important for Ge-based multijunction solar cells. In this work, Ge wet etching using three acidic H 2 O 2 solutions (HF, HCl, and H 2 SO 4 ) was studied in terms of etching rate, surface morphology, and surface roughness. HCl–H 2 O 2 –H 2 O (1:1:5) was demonstrated to wet-etch 535 μm-thick bulk-Ge substrates to 4.1 μm with a corresponding RMS surface roughness of 10 nm, which was the thinnest Ge film from bulk-Ge via a wet etching method to the best of our knowledge. The good quality of pre-etched bulk-Ge was preserved, and the low threading dislocation density of 6000–7000 cm –2 was maintained after the etching process. This approach provides an inexpensive and convenient way for accurate Ge substrate thinning in applications such as multijunction solar cells and sub-10 μm-thick Ge thin film preparation, which enables future studies of low-defect density Ge-based devices such as photodetectors, LEDs, and lasers.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".