Advances in Modeling, Design, and Fabrication of Deep-Etched Multilayer Resonators
Bibliographic record
Abstract
We present recent advances in modeling, design, and fabrication of in-plane multilayer optical resonators fabricated by high aspect ratio etching of silicon. We first revisit the model of Gaussian beam divergence proposed by A. Lipson to correct a mistake that leads to an underestimation of the losses affecting this type of resonator. Secondly, we discuss the influence of surface roughness at the silicon-air interfaces of multilayered structures. Roughness profiles-measured by white light interferometry on the sidewalls of silicon trenches etched by deep reactive ion etching (DRIE)-are presented. The single absorbing layer model of Carniglia is used to predict the influence of the measured roughness ( nm RMS). This model is combined with the corrected model for Gaussian beam divergence and is compared with recent experimental results obtained for a new generation of deep-etched Fabry-Perot refractive index sensors. These sensors are fabricated using the contour lithography method, which is demonstrated to greatly improve the predictability of their optical characteristics. The combined model for roughness and divergence is found to correspond remarkably well with the experimental results, with predictions of loss and finesse of the resonances within an average error of 1.3 dB and 25%, respectively. We therefore expect the models and the simulations presented in this article to become a useful tool for the design of devices based on deep-etched multilayer resonators.
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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 | 0.000 |
| 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 | 0.000 | 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".