Kramers' escape problem for white noise driven switching in ferroelectrics

TL;DR

This paper investigates stochastic resonance in ferroelectric capacitors through simulations, demonstrating potential for weak signal detection by leveraging noise to enhance system response, with implications for semiconductor applications.

Contribution

It introduces a simulation framework for stochastic resonance in ferroelectrics using LGD theory and extends it to multidomain models, highlighting practical detection applications.

Findings

SR enhances weak signal detection in ferroelectrics

Simulation results confirm feasibility for real-world applications

Multidomain models support practical implementation

Abstract

A simulation-based study of stochastic resonance (SR) in a ferroelectric capacitor is presented. The SR phenomenon involves the detection of weak signals by adding an optimal amount noise to a non-linear system. This is linked with Kramers' escape problem, which deals with the escape of a particle undergoing Brownian motion over an energy barrier. The position of the particle is analogous to the polarisation dynamics of a ferroelectric. Within this framework, we numerically investigate SR in single domain ferroelectrics using the Landau-Ginzburg-Devonshire (LGD) theory. In addition, we use a model for multidomain ferroelectrics to demonstrate feasibility in real world applications. Our results show that SR in ferroelectrics is promising for the purpose of weak signal detection, given that these materials are widely used for various applications in the semiconductor industry.