Microcavity platform for widely tunable optical double resonance
Bibliographic record
Abstract
Tunable open-access Fabry–Perot microcavities are versatile and widely applied in different areas of photonics research. The open geometry of such cavities enables the flexible integration of thin dielectric membranes. Efficient coupling of solid-state emitters in various material systems has been demonstrated based on the combination of high quality factors and small mode volumes with a large-range in situ tunability of the optical resonance frequency. Here, we demonstrate that by incorporating a diamond micromembrane with a small thickness gradient, both the absolute frequency and the frequency difference between two resonator modes can be controlled precisely. Our platform allows both the mirror separation and, by lateral displacement, the diamond thickness to be tuned. These two independent tuning parameters enable the double-resonance enhancement of nonlinear optical processes with the capability of tuning the pump laser over a wide frequency range. As a proof of concept, we demonstrate a > T H z continuous tuning range of doubly resonant Raman scattering in diamond, a range limited only by the reflective stopband of the mirrors. Based on the experimentally determined quality factors exceeding 300,000, our theoretical analysis suggests that, with realistic improvements, a ∼ m W threshold for establishing Raman lasing is within reach. Our findings pave the way to the creation of a universal, low-power frequency shifter. The concept can be applied to enhance other nonlinear processes such as second harmonic generation or optical parametric oscillation across different material platforms.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".