Chaos Synchronization in Semiconductor Lasers

Ohtsubo, Junji

doi:10.1007/978-3-642-30147-6_12

Cited by 8 publications

(17 citation statements)

References 66 publications

(71 reference statements)

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“…They also have many practical applications due to their ability to deliver chaotic outputs [ 16 ]. Lasers fall into three classes: A, B and C [ 17 ]. Class C lasers are described by three differential equations for electric field, polarisation and population inversion.…”

Section: Introductionmentioning

confidence: 99%

“…They deliver the most stable outputs. Class B lasers, including solid-state lasers, semiconductor lasers and CO 2 lasers, can be described by two rate equations [ 17 ] as only the fast relaxing polarisation can be eliminated. Thus, they are intrinsically stable [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, with the increase in the number of degrees of freedom in the system when subject to an external perturbation, the output can become chaotic. Such perturbations can include direct modulation of laser operating parameters such as the injection current [ 20 ], optical injection of light from another laser [ 21 ] or optical feedback, where light is reflected back into a laser from an external mirror [ 17 , 22 – 24 ]. Class B lasers have many practical uses because a number of system parameters can be tuned to deliver the type of output that an operator desires: continuous wave (CW), oscillatory, pulsed, or chaotic outputs [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Insights on correlation dimension from dynamics mapping of three experimental nonlinear laser systems

2017

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BackgroundWe have analysed large data sets consisting of tens of thousands of time series from three Type B laser systems: a semiconductor laser in a photonic integrated chip, a semiconductor laser subject to optical feedback from a long free-space-external-cavity, and a solid-state laser subject to optical injection from a master laser. The lasers can deliver either constant, periodic, pulsed, or chaotic outputs when parameters such as the injection current and the level of external perturbation are varied. The systems represent examples of experimental nonlinear systems more generally and cover a broad range of complexity including systematically varying complexity in some regions.MethodsIn this work we have introduced a new procedure for semi-automatically interrogating experimental laser system output power time series to calculate the correlation dimension (CD) using the commonly adopted Grassberger-Proccacia algorithm. The new CD procedure is called the ‘minimum gradient detection algorithm’. A value of minimum gradient is returned for all time series in a data set. In some cases this can be identified as a CD, with uncertainty.FindingsApplying the new ‘minimum gradient detection algorithm’ CD procedure, we obtained robust measurements of the correlation dimension for many of the time series measured from each laser system. By mapping the results across an extended parameter space for operation of each laser system, we were able to confidently identify regions of low CD (CD < 3) and assign these robust values for the correlation dimension. However, in all three laser systems, we were not able to measure the correlation dimension at all parts of the parameter space. Nevertheless, by mapping the staged progress of the algorithm, we were able to broadly classify the dynamical output of the lasers at all parts of their respective parameter spaces. For two of the laser systems this included displaying regions of high-complexity chaos and dynamic noise. These high-complexity regions are differentiated from regions where the time series are dominated by technical noise. This is the first time such differentiation has been achieved using a CD analysis approach.ConclusionsMore can be known of the CD for a system when it is interrogated in a mapping context, than from calculations using isolated time series. This has been shown for three laser systems and the approach is expected to be useful in other areas of nonlinear science where large data sets are available and need to be semi-automatically analysed to provide real dimensional information about the complex dynamics. The CD/minimum gradient algorithm measure provides additional information that complements other measures of complexity and relative complexity, such as the permutation entropy; and conventional physical measurements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Insights on correlation dimension from dynamics mapping of three experimental nonlinear laser systems

2017

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“…Our experimental implementation is based on the well-studied semiconductor laser systems with delayed optical feedback (see for example refs 14 , 15 and references therein). Here, however, we replace the common mirror or fibre loop with fixed delay by two feedback loops with mutually exclusive spectral reflection properties and different lengths, resulting in different delay times.…”

Section: Resultsmentioning

confidence: 99%

Dynamical properties induced by state-dependent delays in photonic systems

et al. 2015

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In many dynamical systems and complex networks time delays appear naturally in feedback loops or coupling connections of individual elements. Moreover, in a whole class of systems, these delay times can depend on the state of the system. Nevertheless, so far the understanding of the impact of such state-dependent delays remains poor with a particular lack of systematic experimental studies. Here we fill this gap by introducing a conceptually simple photonic system that exhibits dynamics of self-organised switching between two loops with two different delay times, depending on the state of the system. On the basis of experiments and modelling on semiconductor lasers with frequency-selective feedback mirrors, we characterize the switching between the states defined by the individual delays. Our approach opens new perspectives for the study of this class of dynamical systems and enables applications in which the self-organized switching can be exploited.

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“…The field has the relation of 〈 E sp ( t ) E * ( t ) 〉 = R sp / 2 . 15 The term R sp is usually used for the effect of spontaneous emission in the photon number equation and is given by: 16…”

Section: Qd-led Model With Fofmentioning

confidence: 99%

Dynamics of quantum dot light-emitting diode with filtered optical feedback

Husseini

Naimee

Khidhir

et al. 2016

Nanomaterials and Nanotechnology

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We study a quantum dot light-emitting diodes (QD-LEDs) subject to filtered optical feedback, where the filter is characterized by a mean frequency O m and a filter width λ. In the limit of a narrow filter (λ ¼ 0), the QD-LED equations reduce under some conditions to the equations for a QD-LED with optical feedback, whereas they become the Lang Kobayashi equations in the limit of an unbounded filter width (λ > 0). Through simulations based on the rate equations for a QD-LED with filtered external optical feedback modes, we show that the output's nonlinear dynamical system attractors can be controlled through the filter parameters: the filter's spectral width λ and its central frequency O m. This is illustrated for a filter-induced global bistability.

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Chaos Synchronization in Semiconductor Lasers

Cited by 8 publications

References 66 publications

Insights on correlation dimension from dynamics mapping of three experimental nonlinear laser systems

Insights on correlation dimension from dynamics mapping of three experimental nonlinear laser systems

Dynamical properties induced by state-dependent delays in photonic systems

Dynamics of quantum dot light-emitting diode with filtered optical feedback

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