We introduce for the first time the utilization of Long short-term memory (LSTM) neural network architectures for the compensation of fiber nonlinearities in digital coherent systems. We conduct numerical simulations considering either C-band or O-band transmission systems for single channel and multi-channel 16-QAM modulation format with polarization multiplexing. A detailed analysis regarding the effect of the number of hidden units and the length of the word of symbols that trains the LSTM algorithm and corresponds to the considered channel memory is conducted in order to reveal the limits of LSTM based receiver with respect to performance and complexity. The numerical results show that LSTM Neural Networks can be very efficient as post processors of optical receivers which classify data that have undergone non-linear impairments in fiber and provide superior performance compared to digital back propagation, especially in the multi-channel transmission scenario. The complexity analysis shows that LSTM becomes more complex as the number of hidden units and the channel memory increase, however LSTM can be less complex than Digital Back Propagation in long distances (> 1000 km).

In this paper, an alternative approach for an integrated photonic reservoir computer is presented. The fundamental building block of the reservoir is based on the nonlinear response of a ring resonator, where effects such as two-photon absorption and nonlinear refractive index variation were taken into consideration. In order to investigate the validity of this scheme, the response of a single add/drop micro ring was simulated through a traveling wave numerical model, and the parameters that affect the nonlinearity of the response were identified. Based on these results, a $5\times 5$ matrix of randomly interconnected resonators was utilized in order to classify different high-bit-rate digital patterns. Simulations confirmed that the proposed system could offer a classification error of 0.5% for bit rates up to 160 Gbps and for 8-bit-length digital words.

Physical unclonable functions are the physical equivalent of one-way mathematical transformations that, upon external excitation, can generate irreversible responses. Exceeding their mathematical counterparts, their inherent physical complexity renders them resilient to cloning and reverse engineering. When these features are combined with their time-invariant and deterministic operation, the necessity to store the responses (keys) in non-volatile means can be alleviated. This pivotal feature, makes them critical components for a wide range of cryptographic-authentication applications, where sensitive data storage is restricted. In this work, a physical unclonable function based on a single optical waveguide is experimentally and numerically validated. The system’s responses consist of speckle-like images that stem from mode-mixing and scattering events of multiple guided transverse modes. The proposed configuration enables the system’s response to be simultaneously governed by multiple physical scrambling mechanisms, thus offering a radical performance enhancement in terms of physical unclonability compared to conventional optical implementations. Additional features like physical re-configurability, render our scheme suitable for demanding authentication applications.

We investigate the complexity and performance of recurrent neural network (RNN) models as post-processing units for the compensation of fibre nonlinearities in digital coherent systems carrying polarization multiplexed 16-QAM and 32-QAM signals. We evaluate three bi-directional RNN models, namely the bi-LSTM, bi-GRU and bi-Vanilla-RNN and show that all of them are promising nonlinearity compensators especially in dispersion unmanaged systems. Αs far as inference is concerned, οur simulations show that the three models provide similar compensation performance, therefore, in real-life systems, the simplest scheme based on Vanilla-RNN units should be preferred. We compare bi-Vanilla-RNN in its many-to-many form with Volterra nonlinear equalizers and exhibit its superiority both in terms of performance and complexity, thus highlighting that RNN processing is a very promising pathway for the upgrade of long-haul optical communication systems utilizing coherent detection.

Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons. Here, we propose an integrated all-optical neuron based on an InAs/InGaAs semiconductor quantum-dot passively mode-locked laser. The multi-band emission capabilities of these lasers allows, through waveband switching, the emulation of the excitation and inhibition modes of operation. Frequency-response effects, similar to biological neural circuits, are observed just as in a typical two-section excitable laser. The demonstrated optical building block can pave the way for high-speed photonic integrated systems able to address tasks ranging from pattern recognition to cognitive spectrum management and multi-sensory data processing.

Neuromorphic computing using photonic hardware is a promising route towards ultrafast processing while maintaining low power consumption. Here we present and numerically evaluate a hardware concept for realizing photonic recurrent neural networks and reservoir computing architectures. Our method, called Recurrent Optical Spectrum Slicing Neural Networks (ROSS-NNs), uses simple optical filters placed in a loop, where each filter processes a specific spectral slice of the incoming optical signal. The synaptic weights in our scheme are equivalent to the filters’ central frequencies and bandwidths. Numerical application to high baud rate optical signal equalization (>100 Gbaud) reveals that ROSS-NN extends optical signal transmission reach to > 60 km, more than four times that of two state-of-the-art digital equalizers. Furthermore, ROSS-NN relaxes complexity, requiring less than 100 multiplications/bit in the digital domain, offering tenfold reduction in power consumption with respect to these digital counterparts. ROSS-NNs hold promise for efficient photonic hardware accelerators tailored for processing high-bandwidth (>100 GHz) optical signals in optical communication and high-speed imaging applications.