Speech Title：Near-sensor and in-sensor computing for neuromorphic machine vision
Neuromorphic visual systems have considerable potential to emulate basic functions of the human visual system even beyond the visible light region. However, the complex circuitry of artificial visual systems based on conventional image sensors, memory and processing units presents serious challenges in terms of device integration and power consumption. Here we show simple two-terminal optoelectronic resistive random access memory (ORRAM) synaptic devices for an efficient neuromorphic visual system that exhibit non-volatile optical resistive switching and light-tunable synaptic behaviours. The ORRAM arrays enable image sensing and memory functions as well as neuromorphic visual pre-processing with an improved processing efficiency and image recognition rate in the subsequent processing tasks. The proof-of-concept device provides the potential to simplify the circuitry of a neuromorphic visual system and contribute to the development of applications in edge computing and the internet of things.
Dr. Yang Chai is an Associate Professor at the Hong Kong Polytechnic University, a member of The Hong Kong Young Academy of Sciences, Vice President of Physical Society of Hong Kong, IEEE Distinguished Lecturer of Electron Device Society, the Chair of IEEE ED/SSC Hong Kong Chapter (2017-2019), technical committee member of IEEE IEDM (2017-2018). He received RGC Early Career Award (2015), IOP Semiconductor Science and Technology Early Career Award in Research (2017), Faculty Award for Outstanding Achievement 2018/19 – Research and Scholarly Activities, ICON 2DMAT Young Scientist Award (2019), and Hong Kong Polytechnic University President’s Award – Research and Scholarly Activities 2019/2020,etc. He has published over 100 papers, including Nature Nanotechnology, Nature Electronics, Nature Communications, Science Advances, IEEE IEDM, etc.