Giant Ferroelectric Resistance Switching Controlled by a Modulatory Terminal for Low‐Power Neuromorphic In‐Memory Computing
Open accessAbstract
Ferroelectrics have been demonstrated as excellent building blocks for high‐performance nonvolatile memories, including memristors, which play critical roles in the hardware implementation of artificial synapses and in‐memory computing. Here, it is reported that the emerging van der Waals ferroelectric α‐In 2 Se 3 can be used to successfully implement heterosynaptic plasticity (a fundamental but rarely emulated synaptic form) and achieve a resistance‐switching ratio of heterosynaptic memristors above 10 3, which is two orders of magnitude larger than that in other similar devices. The polarization change of ferroelectric α‐In 2 Se 3 channel is responsible for the resistance switching at various paired terminals. The third terminal of α‐In 2 Se 3 memristors exhibits nonvolatile control over channel current at a picoampere level, endowing the devices with picojoule read‐energy consumption to emulate the associative heterosynaptic learning. The simulation proves that both supervised and unsupervised learning manners can be implemented in α‐In 2 Se 3 neutral networks with high image recognition accuracy. Moreover, these heterosynaptic devices can naturally realize Boolean logic without an additional circuit component. The results suggest that van der Waals ferroelectrics hold great potential for applications in complex, energy‐efficient, brain‐inspired computing systems and logic‐in‐memory computers.
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Most studies claiming memristive ANNs only measured figures-of-merit of one or a few devices and simulated ANN accuracy via software, so the connection between fabricated memristors and the ANN is relatively weak.
“However, most of those studies in fact only measured the figures-of-merit of one/few devices and simulated the accuracy of an ANN via software62–67 in such type of studies the connection between the memristors fabricated and the ANN is relatively weak.”
From Hardware implementation of memristor-based artificial neural networks (2024) · cites this paper as [65]
Some articles focused on fabrication and device-level characterization of one/few memristors also present figures-of-merit generated by a software-based training ANN process to claim that their devices exhibit potential for neuromorphic applications.
“some articles focused on the fabrication and device-level characterization of one/few memristors62–70,186,187, also present some of the figures-of-merit generated by a software-based training ANN process (similar to the ones in Fig. 14) in order to claim that their devices exhibit potential for neuromorphic applications.”
From Hardware implementation of memristor-based artificial neural networks (2024) · cites this paper as [65]
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Non-volatile memory and artificial synapse based on 2D α-In2Se3(2 H) ferroelectric
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- Authors
- Xue, F., He, X., Wang, Z., Retamal, J. R. D., Chai, Z., Jing, L., Zhang, C., Fang, H., Chai, Y., Jiang, T., Zhang, W., Alshareef, H. N., Ji, Z., Li, L., He, J., & Zhang, X.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- Shanghai Jiao Tong University (CN)
- Liverpool John Moores University (GB)
- Loughborough University (GB)
- Hong Kong Polytechnic University (HK)
- Chinese Academy of Sciences (CN)
- UNSW Sydney (AU)
- City University of Hong Kong (HK)
- Corresponding author
- Ji, Zhigang
- Li, Lain‐Jong
- He, Jr‐Hau
- Zhang, Xixiang
- ORCID
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2021-04-15
- Volume
- 33
- Issue
- 21
- Pages
- e2008709-e2008709
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W3134836631
- Cited by
- 130
- References
- 55
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 25 · 2023: 33 · 2022: 26 · 2021: 7
- FWCI
- 6.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Advanced Materials (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Liverpool John Moores University (submittedVersion)
- Figshare (submittedVersion, cc-by-nc-nd)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
- Funders
- King Abdullah University of Science and Technology
- City University of Hong Kong
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Physical Sciences › Materials Science › Materials Chemistry › 2D Materials and Applications
- Keywords
- α-In2Se3, van der Waals ferroelectrics, heterosynaptic plasticity, memristors, in-memory computing, neuromorphic computing, artificial synapses, associative learning, Boolean logic, low-power neural networks, image recognition, non-volatile memory
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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