A generalized drift-diffusion model for rectifying Schottky contact simulation

We present a discussion on the modeling of Schottky barrier rectifying contacts (diodes) within the framework of partial-differential-equation-based physical simulations. We propose a physically consistent generalization of the drift-diffusion model to describe the boundary layer close to the Schottky barrier where thermionic emission leads to a non-Maxwellian carrier distribution, including a novel boundary condition at the contact. The modified drift-diffusion model is validated against Monte Carlo simulations of a GaAs device. The proposed model is in agreement with the Monte Carlo simulations not only in the current value but also in the spatial distributions of microscopic quantities like the electron velocity and concentratio

Dynamic computing random access memory

The present von Neumann computing paradigm involves a significant amount of information transfer between a central processing unit and memory, with concomitant limitations in the actual execution speed. However, it has been recently argued that a different form of computation, dubbed memcomputing (Di Ventra and Pershin 2013 Nat. Phys. 9 200–2) and inspired by the operation of our brain, can resolve the intrinsic limitations of present day architectures by allowing for computing and storing of information on the same physicalplatform. Here we show a simple and practical realization of memcomputing that utilizes easy-to-build memcapacitive systems. We name this architecture dynamic computing random access memory (DCRAM). We show that DCRAM provides massively-parallel and polymorphic digital logic, namely it allows for different logic operations with the same architecture, by varying only the control signals. In addition, by taking into account realistic parameters, its energy expenditures can be as low as a few fJ per operation. DCRAM is fully compatible with CMOS technology, can be realized with current fabrication facilities, and therefore can really serve as an alternative to the present computing technology

Kuramoto-like model of noisy oscillators

Abstract: The Kuramoto model is a paradigm to describe the dynamics of nonlinear oscillators under the influence of external perturbations or couplings. It is based on the idea to reduce the state equations to a scalar differential equation, that defines the time evolution for the phase of the oscillator. In this paper we discuss the reduction procedure for nonlinear oscillators subject to stochastic perturbations. The result is that phase noise is a drift-diffusion process. It is shown that the unavoidable amplitude fluctuations do change the expected frequency, and the frequency shift depends on the amplitude variance. The theoretical results are illustrated with the help of an example

Vibration energy harvesting enhancement in systems with modulated noise

Energy harvesting promises to make self-powered electronic systems feasible, and to increase the lifetime of battery powered systems almost indefinitely. It is based on the idea to scavenge power from the surrounding environment, in the form of electromagnetic radiation, solar light, temperature gradients, or mechanical vibrations, and to convert it into usable electrical energy. One bottleneck in this technology is the limited energy density of ambient noise, which requires high efficient energy harvesting systems. We show that, for systems with modulated noise, the collected energy can be maximized by a proper control of the state of the system, and we develop a procedure to design the optimal state