OPTICAL NETWORKING & SENSING
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Optical Networking & Sensing
Eric C. 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.
Analog photonic information processing can be implemented with low chip area using wavelength-division multiplexed systems, which typically manipulate light using micro-ring resonators. Micro-rings are uniquely susceptible to thermal crosstalk, with negative system performance consequences if not addressed.
We report responsivity measurements of a multiterminal photodetection device in a commercial silicon photonics platform. The ratio of measured responsivities is found to track the relative terminal lengths. This can serve as a highly compact optoelectronic tap/diplexer. More importantly, complex biasing
Broadband analog signal processors utilizing silicon photonics have demonstrated a significant impact in numerous application spaces, offering unprecedented bandwidths, dynamic range, and tunability. In the past decade, microwave photonic techniques have been applied to neuromorphic processing, resulting
Over the last decade, silicon photonic neural networks have demonstrated the possibility of photonic-enabled machine learning at the edge. These systems enable low-latency ultra-wideband classifications, channel estimations, and many other signal characterization tasks within wireless environments. While
mmWave devices can broadcast multiple spatially-separated data streams simultaneously in order to increase data transfer rates. Data transfer can, however, be compromised by interference. Photonic blind interference cancellation systems offer a power-efficient means of mitigating interference, but previous
We demonstrate, for the first time, real-time blind source separation of interfering GHz transmitters using photonic weights controlled by an RF-System-on-Chip FPGA. This analog system achieves multi-antenna signal separation with millisecond execution latency.
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
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.
