A bi-functional three-terminal memristor applicable as an artificial synapse and neuron
Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synaptic and neuronal functions; however, such memristors...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/173458 |
| _version_ | 1824456168476508160 |
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| author | Liu, Lingli Dananjaya, Putu Andhita Ang, Calvin Ching Ian Koh, Eng Kang Lim, Gerard Joseph Poh, Han Yin Chee, Mun Yin Lee, Calvin Xiu Xian Lew, Wen Siang |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Liu, Lingli Dananjaya, Putu Andhita Ang, Calvin Ching Ian Koh, Eng Kang Lim, Gerard Joseph Poh, Han Yin Chee, Mun Yin Lee, Calvin Xiu Xian Lew, Wen Siang |
| author_sort | Liu, Lingli |
| collection | NTU |
| description | Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synaptic and neuronal functions; however, such memristors face asynchronous programming/reading operation issues. Here, a three-terminal memristor (3TM) based on oxygen ion migration is developed to function as both a synapse and a neuron. We demonstrate short-term plasticity such as pair-pulse facilitation and high-pass dynamic filtering in our devices. Additionally, a 'learning-forgetting-relearning' behavior is successfully mimicked, with lower power required for the relearning process than the first learning. Furthermore, by leveraging the short-term dynamics, the leaky-integrate-and-fire neuronal model is emulated by the 3TM without adopting an external capacitor to obtain the leakage property. The proposed bi-functional 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of an SNN. |
| first_indexed | 2025-02-19T03:49:49Z |
| format | Journal Article |
| id | ntu-10356/173458 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:49:49Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1734582024-02-06T07:16:43Z A bi-functional three-terminal memristor applicable as an artificial synapse and neuron Liu, Lingli Dananjaya, Putu Andhita Ang, Calvin Ching Ian Koh, Eng Kang Lim, Gerard Joseph Poh, Han Yin Chee, Mun Yin Lee, Calvin Xiu Xian Lew, Wen Siang School of Physical and Mathematical Sciences Physics Artificial Synapse Memristor Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synaptic and neuronal functions; however, such memristors face asynchronous programming/reading operation issues. Here, a three-terminal memristor (3TM) based on oxygen ion migration is developed to function as both a synapse and a neuron. We demonstrate short-term plasticity such as pair-pulse facilitation and high-pass dynamic filtering in our devices. Additionally, a 'learning-forgetting-relearning' behavior is successfully mimicked, with lower power required for the relearning process than the first learning. Furthermore, by leveraging the short-term dynamics, the leaky-integrate-and-fire neuronal model is emulated by the 3TM without adopting an external capacitor to obtain the leakage property. The proposed bi-functional 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of an SNN. Agency for Science, Technology and Research (A*STAR) This work was supported by a RIE2020 ASTAR AME IAF-ICP Grant (No. I1801E0030). 2024-02-05T07:10:19Z 2024-02-05T07:10:19Z 2023 Journal Article Liu, L., Dananjaya, P. A., Ang, C. C. I., Koh, E. K., Lim, G. J., Poh, H. Y., Chee, M. Y., Lee, C. X. X. & Lew, W. S. (2023). A bi-functional three-terminal memristor applicable as an artificial synapse and neuron. Nanoscale, 15(42), 17076-17084. https://dx.doi.org/10.1039/d3nr02780e 2040-3364 https://hdl.handle.net/10356/173458 10.1039/d3nr02780e 37847400 2-s2.0-85175243696 42 15 17076 17084 en I1801E0030 Nanoscale © 2023 The Authors. All rights reserved. |
| spellingShingle | Physics Artificial Synapse Memristor Liu, Lingli Dananjaya, Putu Andhita Ang, Calvin Ching Ian Koh, Eng Kang Lim, Gerard Joseph Poh, Han Yin Chee, Mun Yin Lee, Calvin Xiu Xian Lew, Wen Siang A bi-functional three-terminal memristor applicable as an artificial synapse and neuron |
| title | A bi-functional three-terminal memristor applicable as an artificial synapse and neuron |
| title_full | A bi-functional three-terminal memristor applicable as an artificial synapse and neuron |
| title_fullStr | A bi-functional three-terminal memristor applicable as an artificial synapse and neuron |
| title_full_unstemmed | A bi-functional three-terminal memristor applicable as an artificial synapse and neuron |
| title_short | A bi-functional three-terminal memristor applicable as an artificial synapse and neuron |
| title_sort | bi functional three terminal memristor applicable as an artificial synapse and neuron |
| topic | Physics Artificial Synapse Memristor |
| url | https://hdl.handle.net/10356/173458 |
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