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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.
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