Nano-fabrication dependent quality factor in photonic crystal slab biosensors
Bibliographic record
Abstract
Photonic crystal slabs (PCS) are attractive for label-free optical bio-sensors inside micro-fluidic portable diagnostic systems due to their high sensitivity and easy coupling to external radiation. Obtaining high quality factor (Q) values for the guided resonances in these index-of-refraction PCS biosensors is crucial for high sensitivity. Non-ideal fabrication of the hole array in the PCS due to electron beam writing, pattern transfer, and reactive ion etching (RIE) steps will result in imperfect circular hole shapes, and non vertical hole profile that can reduce the Q values. We evaluate the effect of nano-fabrication on the quality factor of guided resonances in PCS biosensors and investigate the potential limitations on sensitivity with current fabrication technologies in a realistic PCS biosensor due to fabrication errors. Spectral broadening of the guided resonances (lower Q values) is found for the fundamental guided resonance modes but no significant changes were observed in higher order guided resonances. These fin dings are consistent with reduced bio-sensing sensitivity in higher order modes due to reduced field overlap with the analyte in side the micro-fluidic channels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".