Quantum DOT opto-electronic devices
Abstract
▪ Abstract Highly strained semiconductors grow epitaxially on mismatched substrates in the Stranski-Krastanow growth mode, wherein islands are formed after a few monolayers of layer-by-layer growth. Elastic relaxation on the facet edges, renormalization of the surface energy of the facets, and interaction between neighboring islands via the substrate are the driving forces for self-organized growth. The dimensions of the defect-free islands are of the order λB, the de Broglie wavelength, and provide three-dimensional quantum confinement of carriers. Self-organized In(Ga)As/GaAs quantum dots, or quantum boxes, are grown by molecular beam expitaxy (MBE) or metal-organic vapor phase epitaxy (MOVPE) on GaAs, InP, and other substrates and are being incorporated in microelectronic and opto-electronic devices. The use of strain to produce self-organized quantum dots has now become a well-accepted approach and is widely used in III–V semiconductors and other material systems. Much progress has been made in the area of growth, where focus has been on size control, and on optical characterization, where the goal has been the application to lasers and detectors. The unique carrier dynamics in the dots, characterized by femtosecond pump-probe spectroscopy, has led to novel device applications. This article reviews the growth and electronic properties of InGaAs quantum dots and the characteristics of interband and intersublevel lasers and detectors and modulation devices.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.