Temperature Characteristics of Silicon-Polymer Hybrid Material Photonic Resonator
Bibliographic record
Abstract
This paper presents temperature sensitivity analysis of a silicon-polymer hybrid photonic micro-ring resonator. Photonic micro-ring resonators have hailed for their potential used in the next generation optical communications. However, like any other optical devices, temperature fluctuations alter the functionality of the micro-ring resonator. Hence, this paper provides a proposal for solution to minimize the temperature sensitive effect on the resonance characteristics. The temperature sensitivity effect of the pure silicon micro-ring resonaltor has been investigated. It is shown that the pure silicon micro-ring resonator is very sensitive to temperature. We added a negative thermo-optic polymer material with the positive thermo-optic silicon material to investigate the efeect of temperature sensitivity. Simulation tool Lumerical solution has been used to simulate the ring resonator. A polymer material of LFR is used to analyze the temperature effects. The hybrid composition of silicon-polymer were as 30%-70%, 40%-60%, 50%-50% and 60%-40% to investigate the temperature effect. The simulation was carried out using a ring size of 5 um, 10 um, 15 um and 20 um. The results show that the temperature insensitive ring resonator is possible with a 42% LFR polymer mixwd with 58% silicon.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".