Large-scale photonic-electronic integration for next generation neuromorphic computing systems

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Neuromorphic computing has gained huge momentum in the last decade thanks to the emergence of novel machine learning algorithms such as deep learning. Artificial neural networks are at the forefront of this revolution and their efficient hardware implementation poses significant challenges that impact on many fields of science and technology. The major problem posed by neural network computing is the handling of large matrix multiplications resulting from the parallel flux of information between densely connected layers of neurons. Photonics offers unique advantages for such demanding task as parallel operation is intrinsic to optical systems. In recent years, a number of schemes based on free-space optical setups have been proposed which have successfully implemented densely connected neural networks with hundreds of thousands of neurons. Such schemes largely rely on spatial-light modulators (SLM) to simultaneously tune millions of neuron interconnects. However, the main drawbacks of commercial SLMs are speed and integrability. An extraordinary opportunity for both high-bandwidth and integrability comes from the recent development of high-speed, high-brightness micro-light emitting diode (uLED) arrays integrated with complementary metal-oxide semiconductor (CMOS) drive electronics. In particular, gallium nitride uLED arrays with GHz-order modulation bandwidths, sub-micron pixel pitches, and large pixel counts have been demonstrated within the past few years by our group. As a result, such combined uLED-on-CMOS arrays offer integrated, reconfigurable, all-optical control eliminating the need of additional electronic tuning elements and external optical sources. In this project, the limits in terms of number of elements and bandwidth will be explored for uLED-on-COMs arrays. Moreover, this system will be combined with optical interconnectivity schemes and advanced learning algorithms to build integrated photonic neural networks. The student will gain expertise in massively parallel drive electronics (LED array drivers) and PIC design, delivering flexible photonic-electronic integration on-a-chip. Furthermore, the student will implement photonic neural network computing with immediate applicability to complex tasks like all-optical signal regeneration and processing, optical pattern recognition and smart sensing. This will require gaining expertise on free-space optical setups and advanced learning algorithms. The student will be part of a larger research group with the opportunity to work with others in a collegiate and enthusiastic team. Research findings will be published in high impact journals with the opportunity to present at international conferences.