System-level integration of active silicon photonic biosensors
Bibliographic record
Abstract
Biosensors based on silicon photonic integrated circuits have attracted a growing interest in recent years. The use of sub-micron silicon waveguides to propagate near-infrared light allows for the drastic reduction of the optical system size, while increasing its complexity and sensitivity. Using silicon as the propagating medium also leverages the fabrication capabilities of CMOS foundries, which offer low-cost mass production. Researchers have deeply investigated photonic sensor devices, such as ring resonators, interferometers and photonic crystals, but the practical integration of silicon photonic biochips as part of a complete system has received less attention. Herein, we present a practical system-level architecture which can be employed to integrate the aforementioned photonic biosensors. We describe a system based on 1 mm<sup>2</sup> dies that integrate germanium photodetectors and a single light coupling device. The die are embedded into a 16x16 mm<sup>2</sup> epoxy package to enable microfluidic and electrical integration. First, we demonstrate a simple process to mimic Fan-Out Wafer-level-Packaging, which enables low-cost mass production. We then characterize the photodetectors in the photovoltaic mode, which exhibit high sensitivity at low optical power. Finally, we present a new grating coupler concept to relax the lateral alignment tolerance down to ± 50 μm at 1-dB (80%) power penalty, which should permit non-experts to use the biochips in a“plug-and-play” style. The system-level integration demonstrated in this study paves the way towards the mass production of low-cost and highly sensitive biosensors, and can facilitate their wide adoption for biomedical and agro-environmental applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".