用于减轻光链路中的非线性失真的硅光子学前馈神经网络
A silicon photonics feed-forward neural network for nonlinear distortion mitigation in an optical link
摘要：
我们设计和建模了一个单层的，无源的，全光硅光子学神经网络，以减轻光链路的非线性。网络节点由硅微谐振器组成，其传递函数已被实验测量。微环的非线性响应的透射振幅和相位图都被参数化为波长和信号功率的函数，以形成复值网络中的单个节点的可调谐激活函数。网络的训练是通过粒子群优化算法来实现的，该算法选择了复杂的权值和激活函数。我们证明了一个单一的前馈层与一个单一的节点感知器是有效的补偿线性和非线性失真在很宽的范围内的信号噪声比和传播长度。我们建议实现这个简单的神经网络作为光链路透明层，以纠正信号失真。
英文摘要：
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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