Time domain simulation of photonic crystals using the transmission line matrix method
Bibliographic record
Abstract
This paper presents the transmission line matrix method (TLM) as an alternative efficient simulation tool for the analysis of photonic crystals (PCs). The paper describes important aspects for the computation of the photonic band structures of infinitely periodic PCs within the formulation of the TLM method. In addition, we propose two methods for reducing the computational effort involved in the simulations of PCs. One method is based on a real-valued implementation of the periodicity (Bloch) condition, and the other one is based on the use of a multi-grid mesh. Depending on the physical geometry of the crystal, computational savings of over 50% can be easily achieved. The advantages and limitations of these methods are described. Given the popularity of the finite differences time domain (FDTD) method for the simulation of PCs, we briefly compare the performance of the TLM method with that of the FDTD and show that under various circumstances, the use of the TLM method can be advantageous. The suitability of the TLM method to handle PCs with more general material properties such as frequency dependent metals and semiconductors is also demonstrated. Finally, we validate these simulation aspects of the TLM method by simulating various photonic crystals composed of dielectric, metallic and semiconducting materials using uniform and multi-grid meshes. The results are compared with those predicted by alternative methods such as the plane wave expansion method for verification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".