Electron Transport through Nanosystems Driven by Coulomb Scattering

Electron transmission through nanosystems is blocked if there are no states connecting the left and the right reservoir. Electron-electron scattering can lift this blockade and we show that this feature can be conveniently implemented by considering a transport model based on many-particle states. We discuss typical signatures of this phenomena, such as the presence of a current signal for a finite bias window.Comment: final version, to appear in Physical Beview B (6 pages and 6 figures included in text, simulation details added and discussion clarified in comparison to first version

Quantum transport: The link between standard approaches in superlattices

Theories describing electrical transport in semiconductor superlattices can essentially be divided in three disjoint categories: i) transport in a miniband; ii) hopping between Wannier-Stark ladders; and iii) sequential tunneling. We present a quantum transport model, based on nonequilibrium Green functions, which, in the appropriate limits, reproduces the three conventional theories, and describes the transport in the previously unaccessible region of the parameter space.Comment: 4 Page

Theoretical analysis of spectral gain in a THz quantum cascade laser: prospects for gain at 1 THz

In a recent Letter [Appl. Phys. Lett. 82, 1015 (2003)], Williams et al. reported the development of a terahertz quantum cascade laser operating at 3.4 THz or 14.2 meV. We have calculated and analyzed the gain spectra of the quantum cascade structure described in their work, and in addition to gain at the reported lasing energy of ~= 14 meV, we have discovered substantial gain at a much lower energy of around 5 meV or just over 1 THz. This suggests an avenue for the development of a terahertz laser at this lower energy, or of a two-color terahertz laser.Comment: in press APL, tentative publication date 29 Sep 200

On the Convergence of the Laplace Approximation and Noise-Level-Robustness of Laplace-based Monte Carlo Methods for Bayesian Inverse Problems

The Bayesian approach to inverse problems provides a rigorous framework for the incorporation and quantification of uncertainties in measurements, parameters and models. We are interested in designing numerical methods which are robust w.r.t. the size of the observational noise, i.e., methods which behave well in case of concentrated posterior measures. The concentration of the posterior is a highly desirable situation in practice, since it relates to informative or large data. However, it can pose a computational challenge for numerical methods based on the prior or reference measure. We propose to employ the Laplace approximation of the posterior as the base measure for numerical integration in this context. The Laplace approximation is a Gaussian measure centered at the maximum a-posteriori estimate and with covariance matrix depending on the logposterior density. We discuss convergence results of the Laplace approximation in terms of the Hellinger distance and analyze the efficiency of Monte Carlo methods based on it. In particular, we show that Laplace-based importance sampling and Laplace-based quasi-Monte-Carlo methods are robust w.r.t. the concentration of the posterior for large classes of posterior distributions and integrands whereas prior-based importance sampling and plain quasi-Monte Carlo are not. Numerical experiments are presented to illustrate the theoretical findings.Comment: 50 pages, 11 figure