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Updated: May 24, 2025
08:07
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
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Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics
Muhammad Umair Khan1,2, Bilal Hassan3,4, Anas Alazzam5,6
1Center for Cyber-Physical Systems - System on Chip Lab, Khalifa University, Abu Dhabi, 127788, UAE. muhammad.khan@ku.ac.ae.
Microsystems & Nanoengineering
|February 28, 2025
Summary
This study integrates a ferrofluid triboelectric nanogenerator (TENG) with an iontronic fluidic memristive (IFM) device for self-powered neuromorphic computing. The system efficiently harvests energy, enabling autonomous operation and replicating synapse-like functions for advanced AI hardware.
Area of Science:
- Materials Science
- Neuroscience
- Electrical Engineering
Background:
- Ionic fluidic devices are crucial for self-powered neuromorphic computing.
- Replicating neuronal activity with artificial systems advances neuromorphic computing.
- Soft-matter fluidic devices dynamically adjust conductance via solution interface changes.
Purpose of the Study:
- To develop an integrated system for self-powered neuromorphic computing.
- To enhance energy harvesting for autonomous powering of iontronic fluidic memristive (IFM) devices.
- To demonstrate synapse-like learning functions in fluidic memristors.
Main Methods:
- Integration of a low-impedance IFM device with a high-impedance ferrofluid (FF) triboelectric nanogenerator (TENG).
- Incorporation of electromagnetic (EMG) signals to boost FF TENG energy harvesting.
- Development of voltage-controlled memristor and memcapacitor memory using PDMS structures with FF and PAA Na+ fluidic interface.
Main Results:
- The integrated FF TENG/EMG system enhances energy harvesting for autonomous IFM device powering.
- Confined ion interactions in the fluidic system induce ion transport hysteresis and memory effects.
- The IFM device successfully replicates diverse electric pulse patterns and demonstrates short-term (STM) and long-term (LTM) memory storage.
Conclusions:
- The developed system is highly suitable for neuromorphic computing applications.
- The fluidic memristor exhibits dynamic synapse-like features, promising for neural network hardware.
- The adaptable FF TENG/EMG device and iontronic fluidic materials enable advanced, self-powered neuromorphic devices.
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