Accurate and efficient sensitivity extraction of complex structures using FDTD
Bibliographic record
Abstract
We discuss a novel FDTD-based technique for estimating accurate sensitivities of the desired response. Our technique utilizes the central adjoint variable method (CAVM) for estimating the response sensitivities. This approach features accuracy comparable to that of the central finite difference (CFD) approximation at the response level. Using only two simulations, of the original and the adjoint photonic structures, the sensitivities with respect to all the designable parameters are obtained regardless of their number. Our approach uses the same update equations of the conventional FDTD for the adjoint problem, which simplifies the implementation. A self-adjoint approach based on CAVM (SA-CAVM) is also proposed to extract the sensitivities of the power reflectivity. Using this self-adjoint approach, only the original simulations are needed to evaluate the objective function and its sensitivities as well. Our approach can also supply wideband sensitivities. The additional cost in this case is mainly that of performing the discrete Fourier transform (DFT) which is negligible compared to the FDTD simulation cost. Our SA-CAVM approach is also utilized to minimize the power reflectivity of deeply etched waveguide terminators, and double layer antireflection coatings on laser diode (LD) facets which can be used as an optical amplifier. The accuracy of our approaches is illustrated by comparing the results with the second order accurate CFD. Our results show a very good agreement between the CAVM-based sensitivities and those obtained using the expensive central finite difference approximation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".