Significant photoluminescence improvements from bulk germanium-based thin films with ultra-low threading dislocation densities
Bibliographic record
Abstract
Bulk Ge crystals, characterized by significantly lower threading dislocation densities (TDD) than their epitaxial counterparts, emerge as optimal candidates for studying and improving Ge laser performance. Our study focused on the Ge thickness and TDD impacts on Ge's photoluminescence (PL). The PL peak intensity of a bulk Ge sample (TDD = 6000 cm-2, n-doping = 1016 cm-3) experiences a remarkable 32-fold increase as the thickness is reduced from 535 µm to 2 µm. This surpasses the PL peak intensity of a best-performing epitaxial-Ge on Si (epi-Ge) (0.75 µm thick, biaxial tensile strain= 0.2%, n-doping = 7 ×1018 cm-3) by a factor of 2.5. Furthermore, the PL peak intensity of a 405 µm thick zero-TDD bulk Ge sample (n-doping = 2.5 × 1018 cm-3) is 9.7 times that of the 0.75 µm thick epi-Ge, rising to 12.1 times when thinned to 1 µm. Although the bulk Ge-based TDD reduction approach doesn’t boost the direct band transitions, it can work alongside n-type doping and strain engineering to enhance Ge laser performance and relax the requirement on the latter two approaches, which reduce the associated side effects of high optical absorption, high non-radiative recombination, and large footprint.
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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".