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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Electrical and Computer Engineering

Year Degree Awarded


Month Degree Awarded


First Advisor

Jun Yao

Subject Categories

Electronic Devices and Semiconductor Manufacturing | Hardware Systems | Nanotechnology Fabrication


Neuromorphic systems built from memristors that emulate bioelectrical information processing in the brain may overcome the limitations of traditional computing architectures. However, functional emulation alone may still not attain all the merits of bio-computation, which uses action potentials of 50–120 mV at least ten times lower than signal amplitude in conventional electronics to achieve extraordinary power efficiency and effective functional integration. Reducing the functional voltage in memristors to this biological amplitude can thus advance neuromorphic engineering and bio-emulated integration. In this dissertation, we demonstrate a type of bio-voltage memristor whose operation voltage is as low as the biological amplitude (e.g., 50-120 mV). The device is made of silver active electrodes, together with dielectric protein nanowires harvested from microbe G. Sulfurreducens, which is considered the key factor for bio-voltage switching, possibly attributed to the protein nanowires catalyze metallization. With the advantage of low-voltage switching, we develop the parameter-matched artificial synapse and neurons for the wearable bio-electronic interface, as well as the comprehensive artificial sensory system harnessing the function of bio-signal fusion and dispersion. In addition, we also propose a new strategy to address the sneak-path issue by utilizing the bio-voltage memristor’s retention property. The unidirectional current flow in the bio-voltage memristor suppresses the sneak-path current, whereas the transient-retention window is exploited for bidirectional programming. This methodology was also extended to other technology-matured electrical components (e.g., diode) for high-efficient in-situ neuromorphic computing by studying diode’s reverse recovery.


Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Available for download on Saturday, February 01, 2025