Graphene Oxide Waveguide and Micro‐Ring Resonator Polarizers
Bibliographic record
Abstract
Abstract Integrated waveguide polarizers and polarization‐selective micro‐ring resonators (MRRs) incorporated with graphene oxide (GO) films are experimentally demonstrated. CMOS‐compatible doped silica waveguides and MRRs with both uniformly coated and patterned GO films are fabricated based on a large‐area, transfer‐free, layer‐by‐layer GO coating method that yields precise control of the film thickness. Photolithography and lift‐off processes are used to achieve photolithographic patterning of GO films with precise control of the placement and coating length. Detailed measurements are performed to characterize the performance of the devices versus GO film thickness and coating length as a function of polarization, wavelength and power. A high polarization dependent loss of ≈53.8 dB is achieved for the waveguide coated with 2‐mm‐long patterned GO films. It is found that intrinsic film material loss anisotropy dominates the performance for less than 20 layers whereas polarization‐dependent mode overlap dominates for thicker layers. For the MRRs, the GO coating length is reduced to 50 µm, yielding a ≈8.3 dB polarization extinction ratio between transverse electric (TE) and transverse magnetic (TM) resonances. These results offer interesting physical insights and trends of the layered GO films and demonstrate the effectiveness of introducing GO films into photonic‐integrated devices to realize high‐performance polarization selective components.
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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) | 0.001 | 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".