Improving silicon nitride ring resonator performances on 300 mm industrial environment for point of care applications (Conference Presentation)
Bibliographic record
Abstract
Point-of-care tests (POCT) are important for detecting illnesses and monitoring patients without the need of a medical laboratory. To be useful, POCT must be sensitive, specific, integrated, and affordable. Since the early 2000s, integrated photonics have offered a possible solution for this problem. In particular, silicon micro-ring resonators represent a compact and sensitive choice known in the industry. This paper details the design, fabrication, and characterization of two methods for improving the performance of ring resonators by engineering their cross section. More precisely, improving devices made out of silicon nitride in an industrial environment to work in the infrared (around 1.31 µm). The first approach is to selectively excite the first order mode of the ring resonator’s waveguide. The first order mode, with its greater exposure to the sensing liquid than the fundamental mode, results in a higher device sensitivity. The second method consists in coupling a dielectric mode with a surface plasmon polariton (SPP) forming a hybrid plasmonic waveguide. Hybrid plasmonic waveguides combine the low losses of the dielectric mode with the high sensitivity of the SPP, which increases the sensitivity in comparison to conventional dielectric ring resonators. Furthermore, hybrid plasmonic micro-ring resonators make possible a stable and easy differential functionalization. Through the optical characterization of the devices, this study shows an experimental sensitivity of first order ring resonators of over 200 nm/RIU* and of hybrid plasmonic devices of 300 nm/RIU*. This demonstrates improvement with respect to the reference silicon nitride dielectric ring (120 nm/RIU*). Characterizations were performed using a PolyDiMethylSiloxane (PDMS) fluidic system to prove the compatibility of the substrate to POCT applications. This paper shows two alternative approaches to integrated nano-photonic sensing for point of care testing. The proposed structures, demonstrate not only a higher sensitivity, but consider selectivity and manufacturing issues, which are fundamental for POCT development. *RIU = Refractive Index Unit
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".