Group IV mid-IR photonics
Bibliographic record
Abstract
Silicon and germanium are transparent up to approximately 8 μm and 15 μm, respectively, thus offering a range of applications in biochemical and environmental sensing, medicine, astronomy and communications [1]. Silicon-on-insulator (SOI), can be used only up to 4 μm due to the high absorption loss of silicon dioxide, and therefore alternative material platforms have to be utilized for longer wavelengths. Also, to fully exploit the transparency range of SOI, 400 or 500 nm thick overlayers need to be used rather than the most popular 220 nm platform [2]. In this paper we report record low loss MIR SOI strip and slot waveguides, as well as Vernier racetrack configurations. If the buried oxide can be removed and replaced with air, such a platform would be transparent up to 8 μm. We report a robust design based on single etch suspended Si waveguides. Ge-on-Si waveguides have already been demonstrated with losses of 2.5–3.0 dB/cm at λ=5.8 μm by Chang et. al [3] and Shen et. al [4]. We report a record low loss in Ge-on-Si and a demonstration of all optical modulation in such waveguides. Although Si is transparent beyond 1.1 μm, it has been demonstrated that it can be used as a photodetector if mid-bandgap states are created by ion implantation. In this paper we show that detection in Si can be extended to up to 2.5 μm by implantation of SOI waveguides with boron. Finally, we also report theoretical analysis of electroabsorption and electrorefraction in Ge.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.023 |
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".