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Register a new userA silicon photonics feed-forward neural network for nonlinear distortion mitigation in an optical link
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We design and model a single-layer, passive, all-optical silicon photonics neural network to mitigate optical link nonlinearities. The network nodes are formed by silicon microring resonators whose transfer function has been experimentally measured. Both the transmitted amplitude and phase maps of the nonlinear response of the microrings are parametrized as a function of the wavelength and of the signal power to form tunable activation functions of the single nodes in the complex valued network. Training of the network is achieved by a particle swarm optimizer which selects the complex weights and the activation functions. We demonstrate that a single feed-forward layer with a single node perceptron is effective in compensating linear and nonlinear distortions over a broad range of signal-to-noise-ratio and propagation lengths. We propose to implement this simple neuronal network as an optical link transparent layer to correct signal distortions.
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Photonic signal processing is essential in the optical communication and optical computing. Numerous photonic signal processors have been proposed, but most of them exhibit limited reconfigurability and automaticity. A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, we report and experimentally demonstrate a fully self-learning and reconfigurable photonic signal processor based on an optical neural network chip. The proposed photonic signal processor is capable of performing various functions including multichannel optical switching, optical multiple-input-multiple-output descrambler and tunable optical filter. All the functions are achieved by complete self-learning. Our demonstration suggests great potential for chip-scale fully programmable optical signal processing with artificial intelligence.
Fiber Kerr nonlinearity is a fundamental limitation to the achievable capacity of long-distance optical fiber communication. Digital back-propagation (DBP) is a primary methodology to mitigate both linear and nonlinear impairments by solving the inverse-propagating nonlinear Schrodinger equation (NLSE), which requires detailed link information. Recently, the paradigms based on neural network (NN) were proposed to mitigate nonlinear transmission impairments in optical communication systems. However, almost all neural network-based equalization schemes yield high computation complexity, which prevents the practical implementation in commercial transmission systems. In this paper, we propose a center-oriented long short-term memory network (Co-LSTM) incorporating a simplified mode with a recycling mechanism in the equalization operation, which can mitigate fiber nonlinearity in coherent optical communication systems with ultralow complexity. To validate the proposed methodology, we carry out an experiment of ten-channel wavelength division multiplexing (WDM) transmission with 64 Gbaud polarization-division-multiplexed 16-ary quadrature amplitude modulation (16-QAM) signals. Co-LSTM and DBP achieve a comparable performance of nonlinear mitigation. However, the complexity of Co-LSTM with a simplified mode is almost independent of the transmission distance, which is much lower than that of the DBP. The proposed Co-LSTM methodology presents an attractive approach for low complexity nonlinearity mitigation with neural networks.
Integrated quantum photonic applications, providing physially guaranteed communications security, sub-shot-noise measurement, and tremendous computational power, are nearly within technological reach. Silicon as a technology platform has proven formibable in establishing the micro-electornics revoltution, and it might do so again in the quantum technology revolution. Silicon has has taken photonics by storm, with its promise of scalable manufacture, integration, and compatibility with CMOS microelectronics. These same properties, and a few others, motivate its use for large-scale quantum optics as well. In this article we provide context to the development of quantum optics in silicon. We review the development of the various components which constitute integrated quantum photonic systems, and we identify the challenges which must be faced and their potential solutions for silicon quantum photonics to make quantum technology a reality.
Integrated optical isolators have been a longstanding challenge for photonic integrated circuits (PIC). An ideal integrated optical isolator for PIC should be made by a monolithic process, have a small footprint, exhibit broadband and polarization-diverse operation, and be compatible with multiple materials platforms. Despite significant progress, the optical isolators reported so far do not meet all these requirements. In this article we present monolithically integrated broadband magneto-optical isolators on silicon and silicon nitride (SiN) platforms operating for both TE and TM modes with record high performances, fulfilling all the essential characteristics for PIC applications. In particular, we demonstrate fully-TE broadband isolators by depositing high quality magneto-optical garnet thin films on the sidewalls of Si and SiN waveguides, a critical result for applications in TE-polarized on-chip lasers and amplifiers. This work demonstrates monolithic integration of high performance optical isolators on chip for polarization-diverse silicon photonic systems, enabling new pathways to impart nonreciprocal photonic functionality to a variety of integrated photonic devices.
Preceptron model updating with back propagation has become the routine of deep learning. Continuous feed forward procedure is required in order for backward propagate to function properly. Doubting the underlying physical interpretation on transformer based models such as GPT brought about by the routine explaination, a new method of training is proposed in order to keep self-consistency of the physics. By treating the GPT model as a space-time diagram, and then trace the worldlines of signals, identifing the possible paths of signals in order fot a self-attention event to occure. With a slight modification, self-attention can be viewed as an ising model interaction, which enables the goal to be designed as energy of system. Target is treated as an external magnetic field inducing signals modeled as magnetic dipoles. A probability network is designed to pilot input signals travelling for different durations through different routes. A rule of updating the probabilities is designed in order to form constructive interference at target locations so that instantaneous energy can be maximised. Experiment was conducted on a 4-class classification problem extracted from MNIST. The results exhibit interesting but expected behavours, which do not exist in a bp updated network, but more like learning in a real human, especially in the few-shot scenario.
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