Brain inspired computing enabled by memristors have gained prominence over the years due to its nano-scale footprint, reduced complexity for implementing synapses and neurons. Several demonstrations show two-dimensional (2D) materials as a promising platform for realization of robust and energy-efficient memristive synapses. Ideally, a synapse should exhibit low cycle-to-cycle (C-C) and device-to-device (D-D) variability along with high maximum /minimum conductance (Gmax/Gmin) ratio, linearity and symmetry in weight update for obtaining high learning accuracy in neural networks (NNs). However, the demonstration of neuromorphic circuits using conventional materials systems has been limited by high C-C and D-D variability and non-linearity in the weight updates. In this study, we have realized robust memristive synapses using 2D molybdenum disulfide (MoS2) to address the concerns like high variability and non-linear weight update and asymmetry. We have utilized engineering techniques like electrode and stack engineering to realize ultra-low variability and linear weight update in MoS2 synapses. The ultra-low C-C and D-D variability in SET voltage, RESET power and weight update is demonstrated in Au/MoS2/Ti/Au synapses. Further, these synapses were integrated with MoS2 leaky-integrate and fire (LIF) neurons to realize AND, OR and NOT logic gates proving the viability of these synapses for in-memory computing. However, these MoS2 synapses suffer from low Gmax/Gmin ratio. We have employed stack engineering to increase Gmax/Gmin ratio while preserving low variability. In that regard, the active medium is modified to a heterogenous stack of MoS2/SiOx with Ti/Au bottom and top electrodes. We observe an increase in the Gmax/Gmin ratio from 2 to ~10. Further, electrode engineering is used to realize graphene/MoS2/SiOx/Ni to obtain linear weight update with identical pulses essential for online training of NNs. This work substantiates the necessity of engineering techniques to implement essential synaptic characteristics like ultra-low variability and linear and symmetric weight update.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Electrical and Computer Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Krishnaprasad Sharada, Adithi Pandrahal, "MoS2 based Memristive Synapses for Neuromorphic Computing" (2022). Electronic Theses and Dissertations, 2020-. 1239.