INTEGRATED PHOTONIC GAS AND LIQUID SENSORS
Bibliographic record
Abstract
Chemical and biological detection is critical in various fields, yet conventional methods often suffer from limitations such as low sensitivity, restricted dynamic range, complex preparation, and bulky equipment. These shortcomings necessitate the development of an affordable, compact, simple, real-time, and accurate detection approach. Refractive index (RI) sensors are particularly well-suited for integrated on-chip optical sensing due to their high sensitivity to small-volume samples. Even minor changes in the refractive index over short distances can induce significant phase shifts in the propagating wave, enabling exceptionally high sensitivities. By leveraging CMOS-compatible technologies, RI sensors offer low-cost and large-scale fabrication capabilities while enabling the monolithic integration of electrical circuitry with optical sensors, resulting in compact, distributed, real-time, and remote sensing systems. This thesis addresses four critical aspects of RI sensing: 1. Identifying the optimal platform and device for RI sensing. 2. Enhancing sensor selectivity to accurately identify medium compositions. 3. Improving sensor robustness against process and temperature variations. 4. Determining the optimal operating wavelength for surface sensing. The work begins with a comprehensive comparison of various on-chip RI platforms and optical devices, including a detailed analysis of their performance parameters. Based on this study, a compact and highly sensitive interferometric gas sensor using a slot-based loop-terminated Mach-Zehnder Interferometer (LT-MZI) design is proposed. Subsequently, a micro-ring resonator design is introduced to address the issue of RI sensor selectivity. This design enables the simultaneous detection of both the real and imaginary parts of the medium's refractive index at multiple wavelengths, facilitating the determination of concentration composition in multi-element mediums. Artificial intelligence algorithms are employed to enhance detection accuracy, extend dynamic range, and mitigate noise in the measured spectrum. To address robustness challenges, a system of four LT-MZIs utilizing wavelength splitting is developed. This system demonstrates significant improvements over conventional MZI-based designs in mitigating process and temperature-induced variations. Finally, an extensive surface sensitivity analysis of silicon nitride waveguides is conducted over a broad wavelength range, from visible to mid-infrared. This study identifies the optimal wavelength for surface sensing and supports the development of a RI-based virus sensor and edge coupler designs for efficient light coupling from fibers.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".