Analysis of evanescent fiber optic sensors using Meep as a simulation tool
Bibliographic record
Abstract
Optical fibers are commonly used as evanescent wave sensors. Many factors affect the performance of fiber optic-based evanescent sensors, including, but not limited to, cladding thickness and the refractive index of the external medium. A lot has been done on analytical and numerical models to predict the effects of these factors in order to aid sensor design. However, many of these models are often complicated and difficult to solve. In this paper, the free open source finitedifference time-domain (FDTD) simulation software called Meep (MIT Electromagnetic Equation Propagation) is used to predict the performance of a fiber optic evanescent wave sensor. The software is based on Maxwell’s equations and it is used for this analysis due to its simplicity of setting up the simulation as well as its reasonable running time. Electromagnetic flux is calculated at various points along the structure to determine the power loss based on the physical dimensions and refractive indices of the structure. The dimensions used in the simulation are relative and therefore the results offered in this work are mainly qualitative. However, they are useful in guiding the overall design of evanescent fiber optic sensors. To validate the accuracy of the simulation results experimentally, optical fibers of 105μm diameter core are etched with sensing regions of length 13mm and cladding thicknesses of 0.1-0.4μm. The change in transmitted power is then measured when an external medium of refractive index up to 1.466 is put in contact to the sensing region. The results are used to explore the advantages and limitations of using this free and open source software in the design, modeling and characterization of this type of photonic sensors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".