Eric Blow is a Researcher in the Optical Networking and Sensing Department at NEC Laboratories America in Princeton, NJ. He received his Bachelor’s degree in Electrical and Computer Engineering from The College of New Jersey, while conducting research focused on nonlinear photonic effects within organic chromophores. He received his MA and PhD in Electrical Engineering from Princeton University. His PhD research focused on developing novel integrated photonic systems for microwave signal processing. With a background in photonics engineering, he contributes to the development of next-generation high-speed real-time optical artificial intelligent computing. Dr. Blow’s work includes design, testing, and prototyping of new photonic integrated circuits, collaborating across teams to ensure that NEC’s hardware meets the demanding requirements of next-generation computation. His efforts help bring experimental technologies from the lab to production, improving reliability and scalability in optical systems.
We experimentally demonstrated the real-time operation of a photonic neuron with a self-connection, a prerequisite for integrated recurrent neural networks (RNNs). After studying two applications, we propose a photonics-assisted platform for time series prediction and classification.
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.
We demonstrate the experimental implementation of photonic neural network for fiber nonlinearity compensation over a 10,080 km trans-pacific transmission link. Q-factor improvement of 0.51 dB is achieved with only 0.06 dB lower than numerical simulations.
