Analysis of a dynamical system modeling lasers and applications for optical neural networks

TL;DR

This paper analyzes the dynamical behavior of a semiconductor laser with optical injection, revealing how equilibrium points change with injection strength, and proposes an optical neural network based on injection locking.

Contribution

It provides an analytical characterization of laser equilibria under various injection conditions and introduces a novel optical neural network architecture utilizing injection locking.

Findings

Nine equilibrium points with weak injection, only one stable.

Number of equilibrium points decreases as injection strength increases.

Proposes an optical neural network based on injection locking.

Abstract

An analytical study of dynamical properties of a semiconductor laser with optical injection of arbitrary polarization is presented. It is shown that if the injected field is sufficiently weak, then the laser has nine equilibrium points, however, only one of them is stable. Even if the injected field is linearly polarized, six of the equilibrium points have a state of polarization that is elliptical. Dependence of the equilibrium points on the injected field is described, and it is shown that as the intensity of the injected field increases, the number of equilibrium points decreases, with only a single equilibrium point remaining for strong enough injected fields. As an application, a complex-valued optical neural network with working principle based on injection locking is proposed.