Microwave-to-optical conversion using lithium niobate thin-film acoustic resonators
Bibliographic record
Abstract
Acoustic or mechanical resonators have emerged as a promising means to mediate efficient microwave-to-optical conversion. Here, we demonstrate conversion of microwaves up to 4.5 GHz in frequency to 1500 nm wavelength light using optomechanical interactions on suspended thin-film lithium niobate. Our method uses an interdigital transducer that drives a freestanding 100 μm-long thin-film acoustic resonator to modulate light traveling in a Mach–Zehnder interferometer or racetrack cavity. The strong microwave-to-acoustic coupling offered by the transducer in conjunction with the strong photoelastic, piezoelectric, and electro-optic effects of lithium niobate allows us to achieve a half-wave voltage of Vπ = 4.6 V and Vπ = 0.77 V for the Mach–Zehnder interferometer and racetrack resonator, respectively. The acousto-optic racetrack cavity exhibits an optomechanical single-photon coupling strength of 1.1 kHz. To highlight the versatility of our system, we also demonstrate a microwave photonic link with unitary gain, which refers to a 0 dB microwave power transmission over an optical channel. Our integrated nanophotonic platform, which leverages the compelling properties of lithium niobate, could help enable efficient conversion between microwave and optical fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 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".