A photonic complex perceptron for ultrafast data processing

Mancinelli, Mattia; Bazzanella, Davide; Bettotti, Paolo; Pavesi, L.

doi:10.1038/s41598-022-08087-2

Cited by 12 publications

(11 citation statements)

References 24 publications

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“…The small PNN device, whose design is shown in Fig. 1, is based on a delayed complex perceptron [17]. The input signal [ut, the input complex field] is split into four waveguides by a cascade of 1 × 2 multimode interferometers (MMIs).…”

Section: Methodsmentioning

confidence: 99%

“…Here, we propose and validate the use of a small silicon photonics 4-channels delayed complex perceptron [17] to equalize a 10 Gbps IMDD 100-km-long optical link. In the proposed photonic neural network (PNN), the input signal is split into 4 channels where the combined actions of delay lines and tunable phase shifters create the desired interference pattern at the output that counteracts the intersymbol interference.…”

Section: Introductionmentioning

confidence: 99%

“…PNN training is based on a particle swarm optimizer (PSO) [18]. Moreover, being the PNN of the feed-forward type [17], the latency induced in signal processing is maximally reduced.…”

Section: Introductionmentioning

confidence: 99%

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Equalization of a 10 Gbps IMDD signal by a small silicon photonics time delayed neural network

Staffoli¹,

Mancinelli²,

Bettotti³

et al. 2023

Photon. Res.

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A small 4-channel time-delayed complex perceptron is used as a silicon photonic neural network (PNN) device to compensate for chromatic dispersion in optical fiber links. The PNN device is experimentally tested with non-return-to-zero optical signals at 10 Gbps after propagation through up to 125 km optical fiber link. During the learning phase, a separation-loss function is optimized in order to maximally separate the transmitted levels of 0s from the 1s, which implies an optimization of the bit-error-rate. Testing of the PNN device shows that the excess losses introduced by the PNN device are compensated by the gain in the transmitted signal equalization for a link longer than 100 km. The measured data are reproduced by a model that accounts for the optical link and the PNN device. This allows simulating the network performances for higher data rates, where the device shows improvement with respect to the benchmark both in terms of performance and ease of use.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Equalization of a 10 Gbps IMDD signal by a small silicon photonics time delayed neural network

Staffoli¹,

Mancinelli²,

Bettotti³

et al. 2023

Photon. Res.

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“…5 We proposed a Silicon-integrated Photonic Neural Network (PNN) for channel equalization in IM-DD schemes. 6,7 Compared to other impairment mitigation methods, such as dispersion-compensating fibers 8 (DCFs), our solution is reconfigurable, has low production costs, and introduces minimum latency (being the PNN of the feed-forward type). The PNN also features reduced power consumption compared to thermally or electrically tunable chirped fiber Bragg gratings.…”

Section: Introductionmentioning

confidence: 99%

“…The linear stage of the implemented PNN is based on an N-channel Delayed Complex Perceptron, 6 whose design is presented in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Chromatic dispersion compensation via an all-optical perceptron

Staffoli,

Maddinelli,

Mancinelli

et al. 2024

Next-Generation Optical Communication: Components, Sub-Systems, and Systems XIII

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We experimentally demonstrate the use of a feed-forward photonic neural network (PNN) for chromatic dispersion compensation in fiber transmission within IM-DD protocols. The PNN device is constituted by an 8-channel alloptical delayed complex perceptron integrated on a Silicon-On-Insulator platform. The PNN device is inserted after the transmitter and before the fiber, thus acting as a pre-compensator. The training is performed via a Particle Swarm Optimizer and aims to provide an open eye diagram at the end-of-line receiver. We observe a 5-order of magnitude Bit Error Rate reduction for -7 dBm of power at the receiver between bare and equalized transmission for 10 Gbps Non-Return-to-Zero signals in a 125 km fiber link (average excess loss of 15 dB). We also perform a study on the minimum number of channels in the PNN needed for full equalization. Overall, the experimental results validate our solution for channel equalization via a PNN with negligible latency and a power consumption of 250 mW on average.

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