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 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 | 0.000 | 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) | 0.000 | 0.002 |
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".