Efficient and Unidirectional Launching of Surface Plasmons from a Hyperbolic Meta‐Antenna
Bibliographic record
Abstract
Abstract Tunnel nanojunctions associated with inelastic electron tunneling have demonstrated crucial applications in on‐chip photonic and plasmonic circuitries due to their high photon modulation speed, large‐scale integration capability, and working‐wavelengths range tunability. However, because most electrons tunnel through a junction elastically, the external quantum efficiency of a nanojunction‐based plasmonic source tends to be around 10−4, severely limiting their applications to date. In this work, an integrated high‐efficiency unidirectional plasmonic source composed of an edge‐to‐edge thickness gradient hyperbolic meta‐antenna is proposed. By engineering the extra wavevector dimension, this study demonstrates a theoretical external quantum efficiency of up to 23% for this system. This is attributed to the large local density of optical states from hyperbolic dispersion and wavevector‐match conditions provided by the optical antennas. Furthermore, this study also demonstrates the tunability of this system across a range of wavelengths from 1300 to 1700 nm. The implementations of these metamaterial‐based tunneling structures enable fast and tunable on‐chip high‐efficiency sources for applications in high‐performance plasmonic circuitries.
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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) | 0.001 | 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".