Experimental characterization of the transition to coherence collapse in a semiconductor laser with optical feedback

TL;DR

This paper experimentally characterizes the transition to coherence collapse in semiconductor lasers with optical feedback, identifying regimes and boundaries using statistical analysis of intensity fluctuations and validating findings with simulations.

Contribution

It introduces a comprehensive experimental mapping of dynamical regimes in semiconductor lasers, using statistical measures to distinguish transitions and validate with Lang-Kobayashi model simulations.

Findings

Distribution shape of intensity fluctuations identifies regimes.

Ordinal analysis confirms regime boundaries.

Simulations agree qualitatively with experimental results.

Abstract

Semiconductor lasers with time-delayed optical feedback display a wide range of dynamical regimes, which have found various practical applications. They also provide excellent testbeds for data analysis tools for characterizing complex signals. Recently, several of us have analyzed experimental intensity time-traces and quantitatively identified the onset of different dynamical regimes, as the laser's current increases. Specifically, we identified the onset of low-frequency fluctuations (LFFs), where the laser intensity displays abrupt dropouts, and the onset of coherence collapse (CC), where the intensity fluctuations are highly irregular. Here we map these regimes when both, the laser current and the feedback strength vary. We show that the shape of the distribution of intensity fluctuations (characterized by the standard deviation, the skewness, and the kurtosis) allows to distinguish between noise, LFFs, and CC, and to quantitatively determine (in spite of the gradual nature of the transitions) the boundaries of the three regimes. Ordinal analysis of the inter-dropout time intervals consistently identifies the three regimes occurring in the same parameter regions as the analysis of the intensity distribution. Simulations of the well-known time-delayed Lang-Kobayashi model are in good qualitative agreement with the observations.