论文标题
硅光子学馈送前馈神经网络，用于光学链接中非线性失真的神经网络
A silicon photonics feed-forward neural network for nonlinear distortion mitigation in an optical link
论文作者
论文摘要
我们设计并建模单层，被动，全光硅光子神经网络，以减轻光学链路非线性。网络节点是由硅微孔谐振器形成的，其传递函数已经过实验测量。微通的非线性响应的传输幅度和相位图都作为波长的函数和信号功率的函数，以形成复杂值网络中单个节点的可调激活函数。网络的训练是通过选择复杂权重和激活功能的粒子群优化器来实现的。我们证明，具有单个节点感知器的单个馈送层有效地补偿了在广泛的信噪比和传播长度上的线性和非线性畸变。我们建议将这个简单的神经元网络作为光学链路透明层，以纠正信号失真。
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.
