DESIGN AND SIMULATION OF A WAVELENGTH DIVISION MULTIPLEXER DEMULTIPLEXER BASED ON PHOTONIC CRYSTAL FILTERS
Bibliographic record
Abstract
Optical properties of photonic crystal (PC) structures have attracted great interest in research area and industry field because of its capability to guide and control light propagation in compact device with feature sizes comparable to light wavelength.Optical filters are essential components of photonic integrated circuits and Wavelength Division Multiplexer (WDM).In this thesis, a WDM is designed based on PC with a defect gap mode.We first describe the design and simulation of the photonic band gap (PBG) in a perfect triangular lattice of air holes in Si.Then based on Silicon-On-Insulator (SOI) slab model, we will introduce pass band in the photonic band gap by moving the two slabs of PBG structure apart some distance, which results in a photonic crystal Fabry-Perot filter in an SOI waveguide.A WDM architecture with 3 channels is designed based on this kind of PC filter last and selective wavelength add/drop function in 2 channel filters is simulated with no more than 1.55dB loss using Finite-Difference Time-Domain (FDTD).PC has strong effects on optical group velocity.In addition to filter response, the dispersion properties in time domain are studied with FDTD.The dispersion was found to be about 45ps/(mm•µm) through the passband around 1.55µm, but due to the small length of the filter, it should have nominal effects for signal propagation up to the bit rate of 4000Gb/s.iii 5.2.2 Calculation of dispersion from time-domain pulse measurements............73 5.2.3 Simulated short pulse transmission ...........
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".