High-performance end-to-end deep learning IM/DD link using optics-informed neural networks.

In this paper, we introduce optics-informed Neural Networks and demonstrate experimentally how they can improve performance of End-to-End deep learning models for IM/DD optical transmission links. Optics-informed or optics-inspired NNs are defined as the type of DL models that rely on linear and/or nonlinear building blocks whose mathematical description stems directly from the respective response of photonic devices, drawing their mathematical framework from neuromorphic photonic hardware developments and properly adapting their DL training algorithms. We investigate the application of an optics-inspired activation function that can be obtained by a semiconductor-based nonlinear optical module and is a variant of the logistic sigmoid, referred to as the Photonic Sigmoid, in End-to-End Deep Learning configurations for fiber communication links. Compared to state-of-the-art ReLU-based configurations used in End-to-End DL fiber link demonstrations, optics-informed models based on the Photonic Sigmoid show improved noise- and chromatic dispersion compensation properties in fiber-optic IM/DD links. An extensive simulation and experimental analysis revealed significant performance benefits for the Photonic Sigmoid NNs that can reach below BER HD FEC limit for fiber lengths up to 42 km, at an effective bit transmission rate of 48 Gb/s.

Temperature and wavelength drift tolerant WDM transmission and routing in on-chip silicon photonic interconnects.

We demonstrate a temperature and wavelength shift resilient silicon transmission and routing interconnect system suitable for multi-socket interconnects, utilizing a dual-strategy CLIPP feedback circuitry that safeguards the operating point of the constituent photonic building blocks along the entire on-chip transmission-multiplexing-routing chain. The control circuit leverages a novel control power-independent and calibration-free locking strategy that exploits the 2nd derivative of ring resonator modulators (RMs) transfer function to lock them close to the point of minimum transmission penalty. The system performance was evaluated on an integrated Silicon Photonics 2-socket demonstrator, enforcing control over a chain of RM-MUX-AWGR resonant structures and stressed against thermal and wavelength shift perturbations. The thermal and wavelength stress tests ranged from 27°C to 36°C and 1309.90 nm to 1310.85 nm and revealed average eye diagrams Q-factor values of 5.8 and 5.9 respectively, validating the system robustness to unstable environments and fabrication variations.