April 1st, 2022

Can Li

Assistant Professor

The University of Hong Kong

Email:

canl@hku.hk

Personal webpage

http://canlab.hku.hk

Research interests

Neuromorphic computing, nanoelectronics devices, non-volatile memories, software-hardware co-optimization

Short bio

Dr. Can Li is currently an Assistant Professor at the Department of Electrical and Electronic Engineering of the University of Hong Kong, working on analog and neuromorphic computing accelerators based on post-CMOS emerging devices (e.g. memristors), for efficient machine/deep learning, network security, signal processing, etc. Before that, He spent two years at Hewlett Packard Labs in Palo Alto, California, and obtained his Ph.D. from University of Massachusetts, Amherst, and B.S./M.S. from Peking University. He is a recipient of the Early Career Award by HKSAR RGC and the Excellent Young Scientist Fund Award by NSFC.

Reasearch highlights

Can Li has made contributions to the in-memory computing technology based on non-volatile memory devices. At the device level, he fabricated and characterized different resistive switching or memristive devices with different material stacks, including Cu/SiOx/Pt, Pt/SiOx/Pt, Si/SiOx/Si, Ta/HfOx/Pt, etc. The potential of this type of device was also demonstrated by Can Li and colleagues’ work on three-dimensional (3D) stacking and integration (up to eight layers), and ultimate scaling down to 2 nm×2 nm. At the array level, he integrated memristors (2 µm×2 µm and 50 nm×50 nm) with silicon transistors from commercial foundries and demonstrated high-yield and good analog programming ability. At the circuit level, he designed and developed analog circuits for analog content addressable memory in a 6-transistor 2-memristor (6T2M) configuration. Can Li was closely involved in designing, taping out, and evaluating peripheral circuits for matrix multiplication accelerators. At the systems level, he showcased the memristor-based system in potential applications such as artificial intelligence, analog signal/image processing, pattern matching, solving optimization problems, hardware security, etc. Those studies have been documented in many high-profile publications, including Nature Electronics, Nature Machine Intelligence, Nature Nanotechnology, Nature Communications, Advanced Materials, IEDM, etc.