Simulation of Organic Semiconductor Devices

Organic electronics is a continuously growing field and in the last decades it has been recognized that organic semiconductors can have an important role in the fabrication of a wide variety of electronic devices.

Several applications have been intensively studied and developed, such as organic light emitting diodes (OLED), organic photovoltaics (OPV), organic field effect transistors (OFET).

Research efforts are motivated by the fact that organic devices are cheaper, easier to fabricate and suitable for new applications. Moreover, chemistry offers a high control level on the tuning of material properties.

From a theoretical point of view, all these devices require a correct understanding of several aspects:
charge injection and transport, energy structure of the organic semiconductors, energy levels alignment at the interface between different materials, the role of trap states, charge recombination and generation.

A comprehensive model would be of great help for devices development and optimization.

Atomistic Quantum Transport Simulations

Molecular electronics is considered one of the promising technologies which are based on bottom-up approach, in which microscopic components are assembled together to build complex macroscopic systems.

This is opposite to the current semiconductor technology which is based on top-down approach aiming at reducing the dimension of components that build the system.

Molecular devices are capable of providing features like rectification, negative differential resistance, conductance switching, magnetoresistance, coulomb blockade and Kondo effect, which opens the way for a variety of novel electronic applications.

Molecular electronics is an interdisciplinary topic between two major fields of research namely physical chemistry and electrical engineering.

Simulation of charge and heat transport at this scales requires full quantum mechanical models (Non equilibrium Green function, Density Functional Theory).

Non-equilibrum Thermodynamics

In the last few years a great advancement has been obtained also in the direction of thermodynamics at the small scale (stochastic thermodynamics), thanks to a collection of theorems called fluctuation theorems (FT).

FT relations have been found for many thermodynamic quantities, in particular one of the most important FT theorem deals with the relative probability that the entropy of a system which is currently away from thermodynamic equilibrium (i.e., maximum entropy) will increase or decrease over a given amount of time.

While the second law of thermodynamics predicts that the entropy of an isolated system should tend to increase until it reaches equilibrium, it became apparent after the discovery of statistical mechanics that the second law is only a statistical one, suggesting that there should always be some nonzero probability that the entropy of an isolated system might spontaneously decrease; the fluctuation theorem precisely quantifies this probability.

More recently, several authors, mainly Sagawa and Ueda of the University of Tokyo, have shown an interesting link between information theory and FT, thus between information and non equilibrium thermodynamics, for the special case of a system under the control of an external feedback.

The feedback comprises a probe that via appropriate sensors can gather information about the system microstate. A computing unit can process the information and define appropriate actions via a controller able to manipulate the system microstate. During this process, energy can flow in or out the system.

The main result of Sagawa and Ueda is that the amount of work extracted by a Szilard engine working in a cycle is at most proportional to the information gathered by the feedback.

This link between information theory and non equilibrium thermodynamics with external feedback opens interesting new perspectives for general non equilibrium thermodynamics and the possibility to apply most of information theory formalism to describe devices in non equilibrium conditions.

In particular many systems like biosystems or complex networks can be properly modelled by this formalism.