On-Chip Sensing System Employing Wavelength Splitting for Noise Suppression
Bibliographic record
Abstract
In this work, we present a novel refractive index (RI) sensing system capable of suppressing optical phase errors (noise). Phase errors, for instance, due to process and temperature variations, limit the sensor's accuracy and limit-of-detection (LoD). The proposed system uses four loop-terminated Mach-Zehnder Interferometers (LT-MZI) to achieve wavelength splitting. LT-MZI allows us to tune the output spectrum using its directional coupler coefficients. Wavelength splitting occurs by the RI change, using two LT-MZIs with opposite wavelength sensitivities. By determining two independent parameters, namely the wavelength splitting and the average wavelength, the system can differentiate between phase changes due to medium index change and phase changes due to any other effects (noise), which maximizes the detection accuracy. This wavelength splitting cannot be achieved using the conventional Mach-Zhender Interferometer (MZI). Another two LT-MZIs with a quarter of the length are used to increase the detection range. This system is used to design a liquid sensor based on CMOS-compatible silicon-on-insulator (SOI) technology, operating in the near-infrared range. The SOI platform achieves high sensitivity to changes in the medium's refractive index and enables compact device designs due to its high index contrast. However, it is also susceptible to optical phase errors. Our proposed system effectively mitigates these errors, enhancing accuracy and LoD. Our designed sensor achieves an intrinsic LoD of 8e-4, and a sensitivity as high as 7890 nm/RIU with a sensing arm length of only 500 µm, which are 3 and 2 times higher than single MZI, respectively. In addition, this sensor has a much higher detection range, 6.3 times higher than a single MZI, and can suppress optical phase noise. Finally, our proposed system was fabricated and experimentally characterized, with measurements aligning closely with simulation results, verifying the reliability of our design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".