  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 the digital signal
processing circuits by processing the optical signals in the analogue domain.
Here we report a silicon photonice-lectronic neural network for solving fibre
nonlinearity compensation of submarine optical fibre transmission systems. Our
approach uses a photonic neural network based on wavelength-division
multiplexing built on a CMOS-compatible silicon photonic platform. We show that
the platform can be used to compensate optical fibre nonlinearities and improve
the signal quality (Q)-factor 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 32-bit graphic processing unit-assisted
workstation. Our reconfigurable photonic-electronic integrated neural network
promises to address pressing challenges in high-speed intelligent signal
processing.
