Research Focus

• Neuromorphic computing for on-chip learning: integration of emerging nanoelectronic devices, non-conventional circuits and architectures, and neural-inspired learning algorithms in order to achieve learning on-a-chip with high energy efficiency and high speed.
• Design for resilience: modeling, simulation, characterization, and resilient design solutions. The goal is to significantly improve the design quality, efficiency, and predictability of nanoscale digital and AMS circuits, with guaranteed tool-to-hardware matching.
• Predictive Technology Model (PTM): accurate, customizable, and predictive model files for future transistor and intereconnect technologies. These predictive model files are compatible with standard circuit simulators and scalable with a wide range of process variations. Currently PTM provides model files for bulk CMOS until the 22nm node, FinFET (double gate) device down to the 7nm node, and carbon nanotube device.
• Reconfigurable design: programmable design of AMS circuits to achieve a new platform for fast emulation, prototyping, and design reconfiguration. Such techniques will further enable resilent design of AMS circuits at scaled technology nodes.