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Title: Ultrafast photonic neuromorphic processing and nonlinearity mitigation in long-haul transmissions by nonlinear optical signal processing
Authors: Tian, Yue
Advisors: Prucnal, Paul R
Contributors: Electrical Engineering Department
Keywords: Neuromorphic processing
Nonlinear optics
Optical communications
Optical signal processing
Subjects: Electrical engineering
Issue Date: 2014
Publisher: Princeton, NJ : Princeton University
Abstract: As digital electronics and integrated circuits have been dramatically progressed in the past decades, it is increasingly difficult to keep up with Moore's Law due to the physical limitations of electronics. Recently optical devices are receiving an increased interest as a promising alternative in various areas, including ultrafast signal processing, computing and control systems, because of the low latency and vast bandwidth enabled by nonlinear optical signal processing. The emerging nonlinear material and devices with higher efficiency and smaller footprint have paved the way for on-chip integration and brought nonlinear optical signal processing to practical deployment. Meanwhile, as an outcome of the marriage of the neuro-ethology drawn from biological neurons with sophisticated modern engineering techniques, neuromorphic processing not only helps in studying biological neural circuits, but also opens up a wide range of applications such as adaptive control, learning, perception, motion control, sensory processing and autonomous robots. Among all the mathematical models drawn from nervous systems, the leaky-integrate-and-fire (LIF) neuron model is the most fundamental and widely used model of biological neurons in theoretical neuroscience for studying complex computation in nervous systems. Its spiking coding and processing mechanism is both computationally efficient and scalable, adopting the best features of both analog and digital computing. Therefore mimicking spike processing with photonics can result in bandwidths that are billions of times higher than biological neurons and substantially faster than electronics. By utilizing a number of nonlinear effects in semiconductor optoelectronic devices and nonlinear fibers, fully functioning photonic neuron prototypes are demonstrated with capability to process optical spikes at the picosecond level. Based on bench-top prototypes, two lightwave neuromorphic circuits are presented as well. Furthermore, one of the most powerful capabilities of neurons is their ability to learn, which takes place by strengthening synaptic connections in response to spiking activity. To mimic this process, an optical spiking time dependent plasticity (STDP) device is invented by using nonlinear properties in semiconductor components. With the optical STDP device, for the first time the supervised learning of a photonic neuron is demonstrated, potentially laying the foundation for learning at speeds that are a billion times faster than those of biological neurons. In addition, the application of nonlinear optical signal processing in telecommunication is studied as well. The huge transmission capacity and ultra-long distance in coherent long-haul transmission systems requires ultrafast signal processing to overcome the linear and nonlinear impairments that occur during transmission. However it is extremely hard for electronics to accomplish real-time processing at such high speeds. With the help of nonlinear optical signal processing, a phase-sensitive boosting (PSB) scheme is proposed using all-optical phase conjugation to mitigate nonlinear impairments and greatly extend the system reach in coherent long-haul optical transmission systems.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Electrical Engineering

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