Relaxation of the electro-optic response in thin-film lithium niobate modulators
Bibliographic record
Abstract
Thin-film lithium niobate (TFLN) is a promising electro-optic (EO) photonics platform with high modulation bandwidth, low drive voltage, and low optical loss. However, EO modulation in TFLN is known to relax on long timescales. Instead, thermo-optic heaters are often used for stable biasing, but heaters incur challenges with cross-talk, high power, and low bandwidth. Here, we characterize the low-frequency (1 mHz to 1 MHz) EO response of TFLN modulators, investigate the root cause of EO relaxation and demonstrate methods to improve bias stability. We show that relaxation-related effects can enhance EO modulation across a frequency band spanning 1kHz to 20kHz in our devices – a counter-intuitive result that can confound measurement of half-wave voltage ( V π ) in TFLN modulators. We also show that EO relaxation can be slowed by more than 10 4 -fold through control of the LN-metal interface and annealing, offering progress toward lifetime-stable EO biasing. Such robust EO biasing would enable applications for TFLN devices where cross-talk, power, and bias bandwidth are critical, such as quantum devices, high-density integrated photonics, and communications.
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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 | 0.001 |
| 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".