Low-Temperature Electroluminescence Excitation Mapping of Excitons and Trions in Short-Channel Monochiral Carbon Nanotube Devices.
Marco GaulkeAlexander JanissekNaga Anirudh PeyyetyImtiaz AlamgirAdnan RiazSimone DehmHan LiUli LemmerBenjamin Scott FlavelManfred M KappesFrank HennrichLi WeiYuan ChenFelix PyatkovRalph KrupkePublished in: ACS nano (2020)
Single-walled carbon nanotubes as emerging quantum-light sources may fill a technological gap in silicon photonics due to their potential use as near-infrared, electrically driven, classical or nonclassical emitters. Unlike in photoluminescence, where nanotubes are excited with light, electrical excitation of single tubes is challenging and heavily influenced by device fabrication, architecture, and biasing conditions. Here we present electroluminescence spectroscopy data of ultra-short-channel devices made from (9,8) carbon nanotubes emitting in the telecom band. Emissions are stable under current biasing, and no enhanced suppression is observed down to 10 nm gap size. Low-temperature electroluminescence spectroscopy data also reported exhibit cold emission and line widths down to 2 meV at 4 K. Electroluminescence excitation maps give evidence that carrier recombination is the mechanism for light generation in short channels. Excitonic and trionic emissions can be switched on and off by gate voltage, and corresponding emission efficiency maps were compiled. Insights are gained into the influence of acoustic phonons on the line width, absence of intensity saturation and exciton-exciton annihilation, environmental effects such as dielectric screening and strain on the emission wavelength, and conditions to suppress hysteresis and establish optimum operation conditions.
Keyphrases
- energy transfer
- carbon nanotubes
- high resolution
- quantum dots
- solid state
- walled carbon nanotubes
- electronic health record
- light emitting
- big data
- single molecule
- life cycle
- human health
- dna damage
- photodynamic therapy
- high intensity
- mass spectrometry
- machine learning
- dna repair
- oxidative stress
- risk assessment
- artificial intelligence
- data analysis
- climate change
- tissue engineering