Enhanced poling and infiltration for highly efficient electro-optic polymer-based Mach-Zehnder modulators
Bibliographic record
Abstract
An ultra-narrow 40-nm slotted waveguide is fabricated to enable highly efficient, electro-optic polymer modulators. Our measurement results indicate that VπL’s below ∼ 1.19 V.mm are possible for the balanced Mach-Zehnder modulators using this ultra-narrow slotted waveguide on a hybrid silicon-organic hybrid platform. Our simulations suggest that VπL’s can be further reduced to ∼ 0.35 V.mm if appropriate doping is utilized. In addition to adapting standard recipes, we developed two novel fabrication processes to achieve miniaturized devices with high modulation sensitivity. To boost compactness and decrease the overall footprint, we use a fabrication approach based on air bridge interconnects on thick, thermally-reflowed, MaN 2410 E-beam resist protected by an alumina layer. To overcome the challenges of high currents and imperfect infiltration of polymers into ultra-narrow slots, we use a carefully designed, atomically-thin layer of TiO2 as a carrier barrier to enhance the efficiency of our electro-optic polymers. The anticipated increase in total capacitance due to the TiO2 layer is negligible. Applying our TiO2 surface treatment to the ultra-narrow slot allows us to obtain an improved index change efficiency (∂n/∂V) of ∼ 22% for a 5 nm TiO2 layer. Furthermore, compared to non-optimized cases, our peak measured current during poling is reduced by a factor of ∼ 3.
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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) | 0.000 | 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".