Thesis
Biomaterial based resistive switching random access memory for artificial neural network
Washington State University
Master of Science (MS), Washington State University
05/2021
DOI:
https://doi.org/10.7273/000000077
Handle:
https://hdl.handle.net/2376/124594
Abstract
Resistive random access memory (RRAM) based on a monosaccharide natural
biomaterial – fructose is developed. Fructose thin film was synthesized using a simple solution
process and acts as a resistive switching material sandwiched in between Cu bottom electrode
and Al or Ag top electrode for comparison. Both devices demonstrated highly reproducible
nonvolatile bipolar resistive switching behaviors and with on/off ratio for Al electrode
demonstrates an order of magnitude (~106
) larger than Ag electrode (~105
). Similarly, the
voltages of forming, set and memory window are also larger for Al electrode than Ag electrode
but the reset voltages for both electrodes are comparable. Dominant conduction mechanisms of
fructose films with Al and Ag top electrodes were revealed by linear fitting of the currentvoltage characteristics, in which at high resistance state both type of electrodes reveals space
charge limited conduction while at low resistance state (LRS), the governing mechanism is
Ohm’s law. In addition, at LRS, Ag electrode also controls by trap-fill limited conduction when
it is approaching to reset voltage. The reasons of these observations are elaborated. Some synaptic and non-ideal behaviors of fructose-based RRAM were also characterized and
investigated. All these results demonstrated that fructose-based RRAM is desirable for
nonvolatile memory and artificial neural network in biocompatible and “green” electronics.
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Details
- Title
- Biomaterial based resistive switching random access memory for artificial neural network
- Creators
- Yuan Xing
- Contributors
- FENG ZHAO (Degree Supervisor) - Washington State University, Engineering and Computer Science (VANC), School ofXINGHUI ZHAO (Committee Member) - Washington State University, Engineering and Computer Science (VANC), School ofTUTKU KARACOLAK (Committee Member) - Washington State University, Engineering and Computer Science (VANC), School of
- Awarding Institution
- Washington State University
- Academic Unit
- Engineering and Computer Science (VANC), School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Format
- pdf
- Number of pages
- 67
- Identifiers
- 99900588363701842
- Language
- English
- Resource Type
- Thesis