Yang Chai

Phase Change and Memory


The Hong Kong Polytechnic University, Hong Kong, China

Email: ychai@polyu.edu.hk



   Dr. Yang Chai is an Assistant Dean of Faculty of Applied Science and Textile of the Hong Kong Polytechnic University, Vice President of Physical Society of Hong Kong, a member of The Hong Kong Young Academy of Sciences, an IEEE Distinguished Lecturer since 2016, and was the Chair of IEEE ED/SSC Hong Kong Chapter (2017-2019). He is a recipient of RGC Early Career Award in 2014, the Semiconductor Science and Technology Early Career Research Award in 2017, PolyU FAST Faculty Award in Research and Scholar Activities in 2018/2019, Young Scientist Award of ICON-2DMAT in 2019, PolyU President’s Award in Research and Scholar Activities in 2019/2020, NR45 Young Innovators Award in 2021, and Young Scientist of World Laureate Forum in 2021. His current research interest mainly focuses on emerging electronic devices.



Abstract for Presentation

Optoelectronic memories for neuromorphic machine vision


    The number of nodes typically used in sensory networks is growing rapidly, leading to large amounts of redundant data being exchanged between sensory terminals and computing units. To efficiently process such large amounts of data, and decrease power consumption, it is necessary to develop approaches to computing that operate close to or inside sensory networks, and that can reduce the redundant data movement between sensing and processing units. 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. We examine the concept of near-sensor and in-sensor computing, in which computation tasks are moved partly to the sensory terminals. We classify functions into low-level and high-level processing, and discuss implementation of near-sensor and in-sensor computing for different physical sensing systems. We also show the in-sensor visual adaptation of accurate image perception.




[1] Nature Electronics, 2022, 5, 84-91

[2] Nature, 2022, 602, 364
[3] Nature Electronics, 2020, 3, 664-671.
[4] Nature, 2020, 579, 32-33.
[5] Nature Nanotechnology, 2019, 14, 776-782