Comparative Study of Photonic Platforms and Devices for On-Chip Sensing
Bibliographic record
Abstract
Chemical and biological detection is now an indispensable task in many fields. On-chip refractive index (RI) optical sensing is a good candidate for mass-scale, low-cost sensors with high performance. While most literature works focus on enhancing the sensors’ sensitivity and detection limit, other important parameters that determine the sensor’s yield, reliability, and cost-effectiveness are usually overlooked. In this work, we present a comprehensive study of the different integrated photonic platforms, namely silica, silicon nitride, and silicon. Our study aims to determine the best platform for on-chip RI sensing, taking into consideration the different aspects affecting not only the sensing performance of the sensor, but also the sensor’s reliability and effectiveness. The study indicates the advantages and drawbacks of each platform, serving as a guideline for RI sensing design. Modal analysis is used to determine the sensitivity of the waveguide to medium (analyte) index change, temperature fluctuations, and process variations. The study shows that a silicon platform is the best choice for high medium sensitivity and a small footprint. On the other hand, silica is the best choice for a low-loss, low-noise, and fabrication-tolerant design. The silicon nitride platform is a compromise of both. We then define a figure of merit (FOM) that includes the waveguide sensitivity to the different variations, losses, and footprint to compare the different platforms. The defined FOM shows that silicon is the best candidate for RI sensing. Finally, we compare the optical devices used for RI sensing, interferometers, and resonators. Our analysis shows that resonator-based devices can achieve much better sensing performance and detection range, due to their fine Lorentzian spectrum, with a small footprint. Interferometer based-sensors allow engineering of the sensors’ performance and can also be designed to minimize phase errors, such as temperature and fabrication variations, by careful design of the interferometer waveguides. Our analysis and conclusions are also verified by experimental data from other published work.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".