Study of the C-band dynamical response of an injection locked LA-EEL for
fully integrated telecommunication data processing
Romain Lance1, Anas Skalli1, Xavier Porte1, 2, Daniel Brunner1
1. FEMTO-ST/Optics Department, UMR CNRS 6174, Université Franche-Comté, 15B avenue
10 des Montboucons, 25030 Besançon Cedex, France
2. Now with: Institute of Photonics, Department of Physics, University of Strathclyde, Glasgow G1 1RD, UKSummary. We implement a fully parallel photonic reservoir in a semiconductor laser operating attelecommunication wavelengths. With this, we process signals with bandwidths exceeding 20 GHz
in real time, targeting telecom applications. Spatially projected and imaged on a digital micromirror
device, our concept provides passive readout weights that can be programmed with standard
software libraries.

In the context of developing new architectures for Neural Networks (NNs) in Photonics, optical reservoir
computing is a suitable candidate to fully integrate such systems. At the same time, telecommunication
digital signal processing (DSP) algorithms are exceedingly challenged by the operation rate of opticaltransmission lines. Several NNs to further improve DSP performance, have been developed on top ofexisting optical signal receiver for modulation format and bit rate identifications (MFI) [1] as well as signal
reconditioning, showing that machine learning is able to tackle such task. However, these systems sufferfrom high computational loads and consume an excess of 50 W for long haul communications. In contrast,
neuromorphic analog photonic approaches propose fully parallel photonic architectures using optical data
injection. They are capable to process information up to signal bandwidth of 20GHz using a learning-based
approach [2] ultimately achieving the full migration of DSP to the analog domain [3]. Integrating operations
like MFI, data recovery, channel equalization, and optical header recognition [3], would allow to reduceenergy consumption as well as the number of data processing operations and suppress the need to transduce
signals between electronics and optics [4].
Semiconductor lasers (SLs) can provide highly nonlinear dynamics as well as several hundred degrees
of freedom, exploitable for nonlinear information transformations, thus a hardware platform for computing[5]. A common challenge is to fully integrate photonic reservoirs including adjustable hardware output
weights. This is tackled here using a digital micromirror device, which allows to spatially select spectral
components of the signal, providing tuneable Boolean output weights. A schematic of the experiment isshown in Fig1(a).

(a)

(b)

(c)

Fig. 1 – (a) Schematic of the experimental setup (b) Spectral response in both fully-locked and free running regimes, for clarity a
60dB offset is added to the free running signal. (c) Spatially selected responses for four exemplary DMD output weight
configurations, and the full DMD mirrors on. For all cases the input modulated signal is sinusoidal of frequency 2GHz, and thedifferent configurations show the response diversity in the different dimensions of the LA-EEL photonic reservoir.

A commercially available Large Area Edge Emitting Laser (LA-EEL) at telecommunication
wavelength, whose spectrum is shown in Fig 1(b), when operated under normal conditions is highly
multimode, both along its transversal and longitudinal directions. We show that such SL device can belocked to a single mode state by an external distributed feedback laser, as depicted in Fig 1(c). Such
injection locking is the prerequisite for injecting information into a semiconductor laser and to use its
nonlinear transient response to embed a photonic NN in its state space. Modulating the DFB signal using aMach–Zehnder Modulator (MZM) provides the means to encode at ultra-high rates the injected signal, i.e.,
the to be processed information, which is nonlinearly transformed along the different spatial and spectral
dimensions of the LA-EEL, see Fig. 1(c). We therefore leverage, both the spectral and spatial degrees offreedom to implement the real photonic neurons. Transversal modes are directly accessible by imaging theoptical signal and its longitudinal components are projected from the spectral domain to the spatial domainusing a blaze grating. This distribution is then imaged on a digital micromirror device (DMD) which acts
as the network output weights. The resulting signal is retrieved with a high bandwidth photodiode, thenanalysed. In Fig 1(c) we show the signal for some exemplary readout weight configurations loaded ontothe DMD. As a next step, we will optimize the readout weights in order to, first, demonstrate specificnonlinear transformations of the input data, to determine the system's fully implemented memory capacity,and then to finally inject corrupted telecommunication signals and to train the LA-EEL RC to recondition
the original signal.
We successfully injection locked the LA-EEL with a modulated input signal at 2GHz. We
dynamically read our reservoir specific neural responses using hyper-spectral imaging of the signal on theDMD, to implement programmable weights of our fully parallel high bandwidth photonic reservoir. Thisis the proof-of-concept of using such commercial and high-performance lasers for real time processing of
ultra-fast signals in the telecommunication signal domain. Our experiments therefore open a wide range of
future applications in this commercially relevant field, making a compelling case for neuromorphic
photonics in a commercially relevant setting.
References[1] Faisal Nadeem Khan, Yudi Zhou, Alan Pak Tao Lau, and Chao Lu, "Modulation format identification in heterogeneous fiber-optic networks
using artificial neural networks," Opt. Express 20, 12422-12431 (2012)[2] Brunner, D., Soriano, M., Mirasso, C. et al. Parallel photonic information processing at gigabyte per second data rates using transient states.
Nat Commun 4, 1364 (2013).
[3] Argyris, Apostolos. "Photonic neuromorphic technologies in optical communications" Nanophotonics, vol. 11, no. 5, 2022, pp. 897-916.
[4] Arregui, Guillermo, et al., "Injection locking in an optomechanical coherent phonon source", Nanophotonics, vol. 10, no. 4, 2021, pp.
5 Anas Skalli, Xavier Porte, Nasibeh Haghighi, Stephan Reitzenstein, James A. Lott, and Daniel Brunner, "Computational metrics andparameters of an injection-locked large area semiconductor laser for neural network computing [Invited]," Opt. Mater. Express 12, 2793-2804(2022)