Scattering loss measurement of SOI waveguides using 5X17 integrated optical star coupler
Bibliographic record
Abstract
We describe a novel non-destructive technique to measure the sidewall roughness induced scattering loss of SOI ridge waveguides using an integrated 5x17 star coupler. The accuracy of our technique is independent of the coupling efficiency. In our technique, we capture the near field images of the full output waveguides array with varying width ranging from 0.2 to 2.0 micron and use the intensity maps of these images to produce normalized intensity profiles, from which the relative scattering losses of output waveguides are extracted. Using our technique, we have studied and compared the scattering and polarization dependent losses of three different sets of SOI waveguide samples fabricated by different processes. We have determined the root-mean-square roughness and autocorrelation length of these samples using scanning electron microscopy (SEM). Relating the loss and roughness analysis, we have showed that the process utilizing negative e-beam photoresist and Cr-hardmask with inductively coupled plasma (ICP) etching produced the smoothest waveguide sidewalls and lowest scattering losses. We have also successfully modeled the measured ridge waveguide losses as a function of waveguide width and demonstrated that the theoretical sidewall roughness is in reasonable agreement with the measured roughness from SEM. Our technique is capable of studying roughness induced scattering loss and thus provides an efficient way of optimizing and monitoring process parameters that affect sidewall roughness.
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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 | 0.000 | 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 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".