.png){kind=icon}
.png){kind=icon}
Return
Highly-efficient electrically-driven localized surface plasmon source enabled by resonant inelastic electron tunneling
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-021-23512-2.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
On-chip plasmonic circuitry offers a promising route to meet the ever-increasing requirement for device density and data bandwidth in information processing. As the key building block, electrically-driven nanoscale plasmonic sources such as nanoLEDs, nanolasers, and nanojunctions have attracted intense interest in recent years. Among them, surface plasmon (SP) sources based on inelastic electron tunneling (IET) have been demonstrated as an appealing candidate owing to the ultrafast quantum-mechanical tunneling response and great tunability. However, the major barrier to the demonstrated IET-based SP sources is their low SP excitation efficiency due to the fact that elastic tunneling of electrons is much more efficient than inelastic tunneling. Here, we remove this barrier by introducing resonant inelastic electron tunneling (RIET)-follow a recent theoretical proposal-at the visible/near-infrared (NIR) frequencies and demonstrate highly-efficient electrically-driven SP sources. In our system, RIET is supported by a TiN/Al2O3 metallic quantum well (MQW) heterostructure, while monocrystalline silver nanorods (AgNRs) were used for the SP generation (localized surface plasmons (LSPs)). In principle, this RIET approach can push the external quantum efficiency (EQE) close to unity, opening up a new era of SP sources for not only high-performance plasmonic circuitry, but also advanced optical sensing applications. On-chip circuits based on plasmonic systems are a promising potential technology. Here the authors present efficient, on-chip, localized plasmonic excitation based on resonant inelastic electron tunneling with metallic quantum well junction.
Keywords:
LIGHT-EMISSION
GENERATION
EXCITATION
TRANSPORT
MODULATOR
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
15.7
Papers:
9.2W
Citations:
91.2W
