On-chip resonance peak extraction in evanescent field silicon photonic biosensors
Bibliographic record
Abstract
Micro ring resonators (MRR) based evanescent field biosensors have shown excellent potential in medical diagnostics due to their performance, scalability, and ability to integrate multiple sensors in a small area to detect various biomarkers simultaneously. The quest to improve the performance and feature size of such sensors has led to the development of cutting-edge photonic integrated circuits (PIC). However, chip-scale implementation of readout and data analysis still needs to be addressed adequately. State-of-the-art evanescent field biosensors rely on off-chip data processing for better results, making the system bulky and ill-suited for point-of-care (PoC) and point-of-use (PoU) applications. In this work, we implement an MRR biosensor in a silicon photonic (SiP) SOI process and demonstrate resonance peak extraction with its measurement data using an application-specific integrated circuit (ASIC) simulated in a 16nm FinFET process with performance similar to external processors.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".