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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Two-terminal protonic devices with synaptic-like short-term depression and device memory.
Erik E Josberger1, Yingxin Deng, Wei Sun
1Department of Materials Science and Engineering, University of Washington, Seattle, WA, 98195-2120, USA; Department of Electrical Engineering, University of Washington, Seattle, WA, 98195-2120, USA.
Two-terminal protonic devices utilizing palladium hydride (PdHx) contacts and a Nafion channel demonstrate rapid 25 ms spiking and low-energy memory switching capabilities.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Device Engineering
Background:
- Protonic devices are crucial for neuromorphic computing and memory applications.
- Developing efficient and low-energy protonic switching mechanisms remains a key challenge.
- Palladium hydride (PdHx) and Nafion are promising materials for proton conduction.
Purpose of the Study:
- To investigate the performance of two-terminal protonic devices with PdHx contacts and a Nafion channel.
- To evaluate the device's capabilities in terms of spiking, short-term plasticity, and memory switching.
- To assess the energy efficiency of the observed switching phenomena.
Main Methods:
- Fabrication of two-terminal devices incorporating PdHx proton conducting contacts.
- Integration of a Nafion polymer as the proton conduction channel.
- Characterization of device electrical properties, including response time and switching behavior.
Main Results:
- The devices exhibited rapid spiking behavior with a response time of 25 milliseconds.
- Demonstrated short-term depression, a key characteristic for synaptic plasticity.
- Achieved low-energy memory switching, indicating potential for energy-efficient electronic applications.
Conclusions:
- Two-terminal protonic devices with PdHx contacts and Nafion channels show promise for neuromorphic computing.
- The observed 25 ms spiking and low-energy switching highlight the potential for efficient artificial synapses.
- Further research can explore optimization for advanced memory and computing architectures.
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