Emergence of Artificial Intelligence in photonic integrated Circuits

The use of artificial intelligence in photonics integrated circuits is a challenge and it is important to learn about this subject for the future development of photonics chips.

Next generation photonic integrated circuits using machine learning design approaches

Webinar presentation by:

Odile Liboiron-Ladouceur received the B.Eng. degree in electrical engineering from McGill University, Montreal, Canada, in 1999, and the M.S. and Ph.D. degrees in electrical engineering from Columbia University, New York, USA, in 2003 and 2007, respectively. Prior to her graduate studies, she worked at Teradyne Inc. (1999-2000) in Boston, as an Applications Engineer, and then joined Texas Instruments Inc. (2000-2002) in Dallas and spent two years working in the fibre-optic business unit as a test and design engineer. She was a summer intern in the optical group at IBM T.J. Watson Research Center in 2006. She joined the Department of Electrical and Computer Engineering at McGill University in 2008 and is currently an Associate Professor and Canada Research Chair in Photonics Interconnect (Tier 2). She holds seven granted U.S. patents and co-authored over 90 peer-reviewed journal papers and more than 135 papers in conference proceedings. Her research interests include photonic integrated circuits for switch and computing and co-design of electrical/photonic integrated circuits. 

Dusan Gostimirovic is a postdoctoral researcher at McGill University in Montreal, Canada, in collaboration with the National Research Council Canada (NRC), researching modern machine learning methods for the design of next-generation photonic circuits and devices. He received his Ph.D. in Electrical and Computer Engineering from Carleton University in Ottawa, Canada, in 2021, where he worked on the design, fabrication, and experimental analysis of silicon photonic devices for high-bandwidth and low-power computing and communications applications. His current work is focused on the development of machine-learning-based methods for the accelerated design of highly robust, high-performance integrated photonic devices that are insensitive to fabrication-process-induced structural and performance variations.