用于125 km、20 Gbps PAM4信号色度色散补偿且可扩展至100 Gbps的硅光子神经网络
A Silicon Photonic Neural Network for Chromatic Dispersion Compensation in 20 Gbps PAM4 Signal at 125 km and Its Scalability up to 100 Gbps
AI总结：
提出一种基于硅光8抽头复数感知机与光电探测非线性的前馈PNN，实验实现125 km、20 Gbps PAM4色散补偿，并仿真验证可扩展至100 Gbps。
摘要：
本文测试了一种前馈光子神经网络（PNN），用于强度调制/直接检测光链路中的色度色散补偿。该PNN基于一系列线性和非线性变换。线性级由在Silicon-On-Insulator（绝缘体上硅）平台上实现的8抽头时延复数感知机构成，并充当可调谐光滤波器。非线性级由施加在线路末端光电探测器上的电场平方模提供。训练过程最大化光电平之间的间隔（即眼图张开度），从而降低误码率。实验证明，对于长达125 km的20 Gbps四电平脉冲幅度调制信号，该方法可实现有效均衡。本文测试了一种进化算法和一种基于梯度的训练方法，并从可重复性和收敛时间方面对二者进行比较。由训练得到的最优权重结合光纤的理论传递函数进行了解释。最后，一项仿真研究证明了该布局向更大带宽扩展的能力，最高可达100 Gbps。
英文摘要：
A feed-forward photonic neural network (PNN) is tested for chromatic dispersion compensation in Intensity Modulation/Direct Detection optical links. The PNN is based on a sequence of linear and nonlinear transformations. The linear stage is constituted by an 8-tap time-delayed complex perceptron implemented on a Silicon-On-insulator platform and acting as a tunable optical filter. The nonlinear stage is provided by the square modulus of the electrical field applied at the end-of-line photodetector. The training maximizes the separation between the optical levels (i.e. the eye diagram aperture), with consequent reduction of the Bit Error Rate. Effective equalization is experimentally demonstrated for 20 Gbps 4-level Pulse Amplitude Modulated signal up to 125 km. An evolutionary algorithm and a gradient-based approach are tested for the training and then compared in terms of repeatability and convergence time. The optimal weights resulting from the training are interpreted in light of the theoretical transfer function of the optical fiber. Finally, a simulative study proves the scalability of the layout to larger bandwidths, up to 100 Gbps.
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。
