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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Emulation of synaptic functions with low voltage organic memtransistor for hardware oriented neuromorphic computing
Srikrishna Sagar1, Kannan Udaya Mohanan2, Seongjae Cho2
1School of Physics, Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM), Vithura, Trivandrum, Kerala, 695551, India.
Scientific Reports
|March 10, 2022
Summary
Novel organic memtransistors (memTs) demonstrate efficient pattern recognition for artificial neural networks. These devices exhibit excellent synaptic functions and high accuracy, paving the way for hardware-based neural network implementation.
Area of Science:
- Materials Science
- Neuroscience
- Electrical Engineering
Background:
- Artificial neural networks (ANNs) require efficient hardware for complex computations.
- Organic electronics offer a promising platform for neuromorphic computing due to their flexibility and low-cost fabrication.
- Developing artificial synapses with robust synaptic functions is crucial for advancing ANNs.
Purpose of the Study:
- To demonstrate synaptic functions and pattern recognition using novel organic memtransistors (memTs).
- To investigate the performance of memTs based on a redox-gating mechanism for neuromorphic applications.
- To explore the potential of these memTs in hardware-oriented neural network implementations.
Main Methods:
- Fabrication of solution-processed organic memtransistors using conjugated polymer thin-film and redox-active solid electrolyte.
- Characterization of memT device performance, including gate voltage, subthreshold swing, and ON/OFF current ratio.
- Demonstration of non-volatile resistive switching (RS) properties and multiple conducting states.
- Implementation of neural network simulations for pattern recognition using the developed memTs.
Main Results:
- MemTs operated effectively at low gate voltages (< -1.5 V) with a high ON/OFF current ratio (> 10^8) and low subthreshold swing (< 120 mV/dec).
- Observed non-volatile resistive switching with a high ON/OFF ratio (10^5) and over 500 distinct conducting states.
- Achieved high training and testing accuracy (> 90%) in pattern recognition simulations using a simple neural network model.
- Demonstrated various synaptic functions essential for neuromorphic computing.
Conclusions:
- The developed organic memTs exhibit excellent performance and synaptic functionalities for artificial neural networks.
- The unconventional redox-gating mechanism enables high-performance neuromorphic computing applications.
- This approach offers a promising pathway for fabricating high-performance artificial synapses and their arrays for hardware-oriented neural networks.
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