High-Efficiency Nanoplasmonic Wavelength Filters Based on MIM Waveguides
Bibliographic record
Abstract
Two ultra-compact nanoplasmonic wavelength filters based on stepped impedance low-pass prototype filter in metal-insulator-metal waveguides are suggested. The transmission and reflection efficiency of both high-pass wavelength and long-wavelength cutoff filter is more than 93%, respectively. Simulation results reveal that similar to the tunability of the structure's transmission spectrum by changing the thickness of the insulator medium, the variations of the width of steps' discontinuities in the high-pass device not only provide an effective method to adjust the transmission peak/cutoff wavelength position, but also give rise to the structure to realize a long-wavelength cutoff filter. On the contrary, modifying the value of aperture length in the proposed long-wavelength cutoff filter gives the capability to control the cutoff wavelength. Moreover, changing the insulator medium to silica converts the device as a dual band bandpass filter at the wavelengths of 1300 and 1550 nm with efficiency of more than 85.2% and 78.1%, respectively. Hence, because of their excellent performance, features, and optimized size, they may have applications in high density photonic integrated circuits.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".