Abstract

The scientific world is witnessing an unprecedented triumph of artificial neural network (ANN)- a computing system inspired by the biological neural network. With the enthralling quest for Internet of Everything (IoE), it is expected to have an unparalleled dominance of ANN in our day-to-day life. In recent times, memristor has come as an emerging candidate to realize ANN through emulating biological synapse and neuron behavior. Molybdenum disulfide (MoS2), one well-known two-dimensional (2D) transition metal dichalcogenides (TMDCs), has drawn interest for high speed, flexible, low power electronic devices since it has a tunable bandgap, reasonable carrier mobility, high Young's modulus, and large surface to volume ratio. Hence, in this work, 2D MoS2 based field effect transistor (FET) and memristor devices have been developed to evaluate the performance for advanced logic and neuromorphic computing applications. We probe the superior quality of 2D/high-? dielectric interfaces by fabricating MoS2 based FET transistors with different gate dielectrics. This low interface trap density of ~7x10^10 states/cm2-eV at the MoS2/Al2O3 interface establishes the case for van der Waals systems where the superior quality of 2D/high-? dielectric interfaces can produce high performance electronic and optoelectronic devices. This work also demonstrates Au/MoS2/Ag threshold switching memristor (TSM) device with low threshold voltage, sharp switching, high ON-OFF ratio and endurance. A leaky integration-and-firing (LIF) neuron is implemented with this TSM. It successfully emulates the key characteristics of a biological neuron. The LIF neuron is monolithically integrated with the MoS2 based synapse device to realize a single layer perceptron operation and Boolean logic gates. The Au/MoS2/Ag TSM device also imitates a nociceptor, the single device exhibits all the key features of nociceptors including threshold, relaxation, "no adaptation" and sensitization phenomena of allodynia and hyperalgesia. This work indicates applicability of this device in artificial intelligence systems-based neuromorphic hardware applications and artificial sensory alarm system.

Notes

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Graduation Date

2021

Semester

Fall

Advisor

Roy, Tania

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering

Format

application/pdf

Identifier

CFE0008823; DP0026102

URL

https://purls.library.ucf.edu/go/DP0026102

Language

English

Release Date

December 2021

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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