Title:  Adaptive Oxide based low-power memristors for Neuromorphic Computing

Committee: 

Dr. Doolittle, Advisor    

Dr. Hunt, Chair

Dr. Khan

Abstract: The objective of the proposed research is to design and develop intercalation based lithium niobite (LixNbO2) memtransistors that act as synaptic elements in neuromorphic architectures, achieving high energy efficiency, and co-localized processing and main memory, thus overcoming the memory wall problem. Brain-inspired neuromorphic computing is a crucial field in addressing the increased need for collection, analysis and decision making from high volumes of dynamic unstructured data generated globally, at low power consumption. Memristive devices have emerged a key enabling technology for developing such large scale neuromorphic computing platforms. Lithium niobite (LixNbO2) is an adaptive suboxide which has already shown promise in developing volatile and non-volatile memristors for highly scalable and low-power neuromorphic circuitry. This research aims to develop three-terminal LixNbO2 based memtransistors that will enable complex heterosynaptic functionality and real-time signal convolution in lithium niobite based low-power neuromorphic hardware platforms.