Abstract

Neuromorphic visual systems emulating biological retina functionalities have enormous potential for in-sensor computing, with prospects of making artificial intelligence ubiquitous. Optical data sensing, processing and visual memory are fundamental requirements for artificial intelligence and robotics with autonomous navigation. Conventionally, imaging has been kept separate from the pattern recognition circuitry. Visual information is captured by an image sensor, stored by memory units, and eventually processed by the machine learning algorithm. Optoelectronic synapses hold the special potential of integrating these fields into a single layer, where a single device can record optical data, convert it into a conductance state and store it for learning and pattern recognition, similar to the optic nerve in human eye. Here we present optoelectronic synapse devices with multifunctional integration of all the processes required for real time object identification. First, an optoelectronic synapse was developed which operates in visible wavelengths of light. Then, ultraviolet-visible wavelength-sensitive MoS2 FET channel was integrated with infrared sensitive PtTe2 gate electrode to enable the device to sense, store, and process optical data for a wide range of the electromagnetic spectrum, while maintaining a low dark current. Both the devices exhibit optical stimulation-controlled short term and long term potentiation, electrically driven long term depression, synaptic weight update and all other necessary synaptic characteristics. An artificial neural network was developed using the extracted weight update parameters of the multi-wavelength optoelectronic synapse device, which can be trained to identify both single wavelength and mixed wavelength patterns. Our work establishes the platform of neuromorphic visual systems for pattern recognition and object identification.

Notes

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Graduation Date

2023

Semester

Spring

Advisor

Roy, Tania

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Physics

Degree Program

Physics

Identifier

CFE0009861; DP0028153

URL

https://purls.library.ucf.edu/go/DP0028153

Language

English

Release Date

November 2024

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

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