A Neuromorphic Silicon Photonics Nonlinear Equalizer For Optical Communications With Intensity Modulation and Direct Detection

Katumba, Andrew; Yin, Xin; Dambre, Joni; Bienstman, Peter

doi:10.1109/jlt.2019.2900568

Cited by 52 publications

(27 citation statements)

References 37 publications

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“…While coherent feed-forward neural networks have been recently reported 18 , 19 , the number of experimental demonstrations of RC based on silicon photonics still remain very limited 15 , 20 , 21 . Most of the proposed schemes focus on passive spatially distributed reservoirs made by small matrices of waveguides 20 , 22 , photonic crystal cavities 23 or ring resonators 24 – 26 . The fading memory of these systems is given by the delay lines which connect the nodes, or by the photon lifetime of the cavities.…”

Section: Introductionmentioning

confidence: 99%

Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations

Borghi

Biasi

Pavesi

2021

Sci Rep

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Photonic implementations of reservoir computing (RC) promise to reach ultra-high bandwidth of operation with moderate training efforts. Several optoelectronic demonstrations reported state of the art performances for hard tasks as speech recognition, object classification and time series prediction. Scaling these systems in space and time faces challenges in control complexity, size and power demand, which can be relieved by integrated optical solutions. Silicon photonics can be the disruptive technology to achieve this goal. However, the experimental demonstrations have been so far focused on spatially distributed reservoirs, where the massive use of splitters/combiners and the interconnection loss limits the number of nodes. Here, we propose and validate an all optical RC scheme based on a silicon microring (MR) and time multiplexing. The input layer is encoded in the intensity of a pump beam, which is nonlinearly transferred to the free carrier concentration in the MR and imprinted on a secondary probe. We harness the free carrier dynamics to create a chain-like reservoir topology with 50 virtual nodes. We give proof of concept demonstrations of RC by solving two nontrivial tasks: the delayed XOR and the classification of Iris flowers. This forms the basic building block from which larger hybrid spatio-temporal reservoirs with thousands of nodes can be realized with a limited set of resources.

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Section: Introductionmentioning

confidence: 99%

Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations

Borghi

Biasi

Pavesi

2021

Sci Rep

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“…Here, we propose a tuneable optoelectronic solution to mitigate the CD in DD systems. The processing structure we are investigating is located close to the approach using a simplified optical reservoir processor, discussed in [15], together with an NN digital processor [14]. This work is an extension of our preliminary results of [16].…”

Section: Introductionmentioning

confidence: 97%

“…Works with an on-off keying (OOK) modulation format have also been presented in the literature [12]. Recently, further signal processing technologies for this application range have been proposed, such as DSP-implemented neural networks [13,14] or silicon photonic-based opto-electronic reservoir computers with analog electronic processing [15].…”

Section: Introductionmentioning

confidence: 99%

Tunable Optoelectronic Chromatic Dispersion Compensation Based on Machine Learning for Short-Reach Transmission

et al. 2019

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In this paper, a machine learning-based tunable optical-digital signal processor is demonstrated for a short-reach optical communication system. The effect of fiber chromatic dispersion after square-law detection is mitigated using a hybrid structure, which shares the complexity between the optical and the digital domain. The optical part mitigates the chromatic dispersion by slicing the signal into small sub-bands and delaying them accordingly, before regrouping the signal again. The optimal delay is calculated in each scenario to minimize the bit error rate. The digital part is a nonlinear equalizer based on a neural network. The results are analyzed in terms of signal-to-noise penalty at the KP4 forward error correction threshold. The penalty is calculated with respect to a back-to-back transmission without equalization. Considering 32 GBd transmission and 0 dB penalty, the proposed hybrid solution shows chromatic dispersion mitigation up to 200 ps/nm (12 km of equivalent standard single-mode fiber length) for stage 1 of the hybrid module and roughly double for the second stage. A simplified version of the optical module is demonstrated with an approximated 1.5 dB penalty compared to the complete two-stage hybrid module. Chromatic dispersion tolerance for a fixed optical structure and a simpler configuration of the nonlinear equalizer is also investigated.

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“…Another approach does not consider any nonlinearity inside the network, but rather only relies on the detector nonlinearity at the output of the system 7 , 14 . In that case, the nonlinearity mainly comes from the modulus-square of the optical detection from the photodetector.…”

Section: Introductionmentioning

confidence: 99%

Comparing different nonlinearities in readout systems for optical neuromorphic computing networks

Lambrecht

Laporte

et al. 2021

Sci Rep

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Nonlinear activation is a crucial building block of most machine-learning systems. However, unlike in the digital electrical domain, applying a saturating nonlinear function in a neural network in the analog optical domain is not as easy, especially in integrated systems. In this paper, we first investigate in detail the photodetector nonlinearity in two main readout schemes: electrical readout and optical readout. On a 3-bit-delayed XOR task, we show that optical readout trained with backpropagation gives the best performance. Furthermore, we propose an additional saturating nonlinearity coming from a deliberately non-ideal voltage amplifier after the detector. Compared to an all-optical nonlinearity, these two kinds of nonlinearities are extremely easy to obtain at no additional cost, since photodiodes and voltage amplifiers are present in any system. Moreover, not having to design ideal linear amplifiers could relax their design requirements. We show through simulation that for long-distance nonlinear fiber distortion compensation, using only the photodiode nonlinearity in an optical readout delivers BER improvements over three orders of magnitude. Combined with the amplifier saturation nonlinearity, we obtain another three orders of magnitude improvement of the BER.

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A Neuromorphic Silicon Photonics Nonlinear Equalizer For Optical Communications With Intensity Modulation and Direct Detection

Cited by 52 publications

References 37 publications

Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations

Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations

Tunable Optoelectronic Chromatic Dispersion Compensation Based on Machine Learning for Short-Reach Transmission

Comparing different nonlinearities in readout systems for optical neuromorphic computing networks

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