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A silicon photonic–electronic neural network for fibre nonlinearity compensation
Nature Electronics ( IF 34.3 ) Pub Date : 2021-11-22 , DOI: 10.1038/s41928-021-00661-2
Chaoran Huang 1, 2 , Thomas Ferreira de Lima 1 , Alexander N. Tait 1 , Eric C. Blow 1 , Simon Bilodeau 1 , Aashu Jha 1 , Hsuan-Tung Peng 1 , Bhavin J. Shastri 1, 3 , Paul R. Prucnal 1 , Shinsuke Fujisawa 4, 5 , Yue Tian 4 , Fatih Yaman 4 , Hussam G. Batshon 4 , Ting Wang 4 , Yoshihisa Inada 5
Affiliation  

In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but increasing speed and computational complexity create challenges for such approaches. Highly parallel, ultrafast neural networks using photonic devices have the potential to ease the requirements placed on digital signal processing circuits by processing the optical signals in the analogue domain. Here we report a silicon photonic–electronic neural network for solving fibre nonlinearity compensation in submarine optical-fibre transmission systems. Our approach uses a photonic neural network based on wavelength-division multiplexing built on a silicon photonic platform compatible with complementary metal–oxide–semiconductor technology. We show that the platform can be used to compensate for optical fibre nonlinearities and improve the quality factor of the signal in a 10,080 km submarine fibre communication system. The Q-factor improvement is comparable to that of a software-based neural network implemented on a workstation assisted with a 32-bit graphic processing unit.



中文翻译:

一种用于光纤非线性补偿的硅光子电子神经网络

在光通信系统中,光纤非线性是提高传输容量的主要障碍。通常,数字信号处理技术和硬件用于处理光通信信号,但不断提高的速度和计算复杂性为此类方法带来了挑战。使用光子器件的高度并行、超快神经网络有可能通过处理模拟域中的光信号来减轻对数字信号处理电路的要求。在这里,我们报告了一种用于解决海底光纤传输系统中光纤非线性补偿的硅光子电子神经网络。我们的方法使用基于波分复用的光子神经网络,该网络建立在与互补金属氧化物半导体技术兼容的硅光子平台上。我们表明,该平台可用于补偿光纤非线性并提高 10,080 公里海底光纤通信系统中信号的品质因数。这Q因子改进可与在 32 位图形处理单元辅助的工作站上实现的基于软件的神经网络相媲美。

更新日期:2021-11-22
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