Committee:

Dr. Shimeng Yu, ECE, Chair , Advisor

Dr. Asif Khan, ECE

Dr. Azad Naeemi, ECE

Dr. Alan Doolittle, ECE

Dr. Suman Datta, U of Notre Dame

Abstract: Neuromorphic computing has been proposed to accelerate the computation for deep neural networks (DNNs). The objective of this thesis work is to investigate the ferroelectric and metal-insulator phase transition devices for neuromorphic computing. We proposed and experimentally demonstrated the drain erase scheme in FeFET to enable the individual cell program/erase/inhibition for in-situ training in 3D NAND-like FeFET array. We also identify a new challenge of ferroelectric partial switching, namely “history effect” in minor loop dynamics for multi-level states analog in-memory computing. We experimentally validated the history effect on FeCap and FeFET. A phase-field model was constructed to understand the origin. Apart from using FeFET as synaptic devices, using metal-insulator phase transition device, as neuron was also explored experimentally. A NbOx metal-insulator phase transition threshold switch was integrated at the edge of the crossbar array as oscillation neuron. One promising application for FeFET+NbOx neuromorphic system is to implement quantum error correction (QEC) circuitry at 4K. Cryo-NeuroSim, a device-to-system modeling framework that calibrates data at cryogenic temperature was developed to benchmark the performance of the FeFET+NbOx neuromorphic system.