Speaker: Yiyang Li, University of Michigan (UMich)
Program Description:
The rapid increase in data generation and processing necessitates ever greater computational power, especially for energy-intensive processes like machine learning. At the same time, Moore’s Law is reaching its physical limits, so further advances in computing require unconventional architectures, devices, and materials. One such approach is in-memory computing, which combines memory and computation onto a single element. In-memory computing is expected to be at least 100 times more efficient than optimized digital computers, but this technology has been limited by the poor performance of the memory-compute element.
In this talk, we present the use of solid-state electrochemical cells as on-chip memory-compute elements. These cells contain a solid electrolyte sandwiched by two mixed electronic and ionic conducting electrodes. Electrochemical ion intercalation drives ion migration between the two electrodes, as in a battery. However, instead of storing energy, intercalation provides nonvolatile analogue information storage. We show how electrochemical memory cells enable low-energy, deterministic switching with hundreds analogue information states, a significant advance over other memory-compute technologies. We further show that cells based on oxygen vacancies can meet the material requirements for CMOS integration at the size and densities necessary for in-memory computing.
Bio: Yiyang Li is an assistant professor of Materials Science and Engineering at the University of Michigan. His research lies at the intersection of electrochemistry and electronics, applied towards energy storage and computing. Yiyang received his PhD at Stanford University (2016) and was trained as a postdoc at SLAC’s Stanford Institute for Materials and Energy Science (2016-17) and a Harry Truman Fellow at Sandia National Laboratories (2017-20)
