基于小型硅光子时延神经网络的10 Gbps IMDD信号均衡
Equalization of a 10 Gbps IMDD signal by a small silicon photonics time delayed neural network
AI总结：
本研究提出用4通道时延复数感知机构成的小型硅光子神经网络器件补偿光纤色度色散，经10Gbps信号实验验证，100km以上链路中其额外损耗可被均衡增益抵消，更高速率仿真下性能与易用性均优于基准。
摘要：
一款小型4通道时延复数感知机被用作硅光子神经网络（NN）器件，用于补偿光纤链路中的色度色散。该神经网络器件在经过最长125 km光纤链路传输后的10 Gbps非归零光信号下开展了实验测试。在学习阶段，通过优化分离-损失函数，最大程度地将传输的0电平和1电平区分开，这意味着实现了误码率的优化。对该神经网络器件的测试表明，对于长度超过100 km的链路，该器件引入的额外损耗可由传输信号均衡带来的增益所补偿。实测数据通过一个同时考虑光链路和神经网络器件的模型得到了复现。这使得可以仿真该网络在更高速率下的性能，仿真结果显示，该器件在性能和易用性方面均优于基准方案。
英文摘要：
A small 4-channels time-delayed complex perceptron is used as a silicon photonics neural network (NN) device to compensate for chromatic dispersion in optical fiber links. The NN device is experimentally tested with non-return-to-zero optical signals at 10 Gbps after propagation through up to 125 km optical fiber link. During the learning phase, a separation-loss function is optimized in order to maximally separate the transmitted levels of 0s from the 1s, which implies an optimization of the bit-error-rate. Testing of the NN device shows that the excess losses introduced by the NN device are compensated by the gain in transmitted signal equalization for a link longer than 100 km. The measured data are reproduced by a model which accounts for the optical link and the neural network device. This allows simulating the network performances for higher data rates, where the device shows improvement with respect to the benchmark both in terms of performance as well as ease of use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
