Silicon photonic manufacturing has opened possibilities for new concepts in optical information science. Neural networks have proliferated throughout machine learning applications, but electronic implementations are reaching performance limits. Neuromorphic photonics is the pursuit of a bridge between photonic physics and neuromorphic applications. This talk addresses fundamentals and current status: what makes a device neuromorphic; what applications are promising?

Alex Tait is an assistant professor of electrical and computer engineering at Queen's University, Kingston, ON, Canada. He was a NRC postdoctoral fellow in the Quantum Nanophotonics and Faint Photonics Group at the National Institute of Standards and Technology, Boulder, CO, USA. He received his PhD in the Lightwave Communications Research Laboratory, Department of Electrical Engineering, Princeton University, Princeton, NJ, USA under the direction of Paul Prucnal. His research interests include silicon photonics, neuromorphic engineering, and superconducting optoelectronics. Dr. Tait is a recipient of the National Science Foundation (NSF) Graduate Research Fellowship (GRFP) and is a member of the IEEE Photonics Society and the Optical Society of America (OSA). He is the recipient of the Award for Excellence from the Princeton School of Engineering and Applied Science, the Best Student Paper Award from the 2016 IEEE Summer Topicals Meeting Series, and the Class of 1883 Writing Prize from the Princeton Department of English. He has authored 15 refereed journal papers, (co)filed 8 provisional patents, created 7 open-source software packages, and contributed to the textbook Neuromorphic Photonics.