A Reliable All‐2D Materials Artificial Synapse for High Energy‐Efficient Neuromorphic Computing
Open accessAbstract
High‐performance artificial synaptic devices are indispensable for developing neuromorphic computing systems with high energy efficiency. However, the reliability and variability issues of existing devices such as nonlinear and asymmetric weight update are the major hurdles in their practical applications for energy‐efficient neuromorphic computing. Here, a two‐terminal floating‐gate memory (2TFGM) based artificial synapse built from all‐2D van der Waals materials is reported. The 2TFGM synaptic device exhibits excellent linear and symmetric weight update characteristics with high reliability and tunability. In particular, the high linearity and symmetric synaptic weight realized by simple programming with identical pulses can eliminate the additional latency and power consumption caused by the peripheral circuit design and achieve an ultralow energy consumption for the synapses in the neural network implementation. A large number of states up to ≈3000, high switching speed of 40 ns and low energy consumption of 18 fJ for a single pulse have been demonstrated experimentally. A high classification accuracy up to 97.7% (close to the software baseline of 98%) has been achieved in the Modified National Institute of Standards and Technology (MNIST) simulations based on the experimental data. These results demonstrate the potential of all‐2D 2TFGM for high‐speed and low‐power neuromorphic computing.
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Memristor crossbar array-based neural networks usually suffer from low accuracy because of non-ideal factors of memristors such as non-linearity and asymmetry, which prevent weights from being programmed to their targeted values.
“However, memristor crossbar array-based neural networks usually suffer from low accuracy because of the non-ideal factors of memristors such as non-linearity and asymmetry, which prevent weights from being programmed to their targeted values.”
It is hard to train the weights of a neural network in a memristor crossbar array.
“Despite the effectiveness of memristors as synapses in neural network systems, it is hard to train the weights of the neural network in a memristor crossbar array.”
Severe non-linear and asymmetric update properties prevent a memristor from being programmed to a targeted conductance state because even a single potentiation or depression pulse can induce a catastrophic conductance change.
“These prevent a memristor from being programmed to a targeted conductance state because even a single potentiation or depression pulse can induce a catastrophic conductance change (Dw).”
Two-terminal memristors show long retention and fast speed, but they usually undergo severe non-linear and asymmetric update properties.
“Two-terminal memristors show long retention and fast speed, but they usually undergo severe non-linear and asymmetric update properties.6,8,9,19,20”
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Park, S.-O., Park, T., Jeong, H., Hong, S., Seo, S., Kwon, Y., Lee, J., & Choi, S. (2023) · Nanoscale Horizons · doi:10.1039/d3nh00121k · Cited by 31 · see citing works
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- Authors
- Tang, J., He, C., Tang, J., Yue, K., Zhang, Q., Liu, Y., Wang, Q., Wang, S., Li, N., Shen, C., Zhao, Y., Liu, J., Yuan, J., Wei, Z., Li, J., Watanabe, K., Taniguchi, T., Shang, D., Wang, S., Yang, W., Yang, R., Shi, D., & Zhang, G.
- Affiliations
- Chinese Academy of Sciences (CN)
- Beijing Normal University (CN)
- University of Southern California (US)
- Tsinghua University (CN)
- RIKEN Center for Emergent Matter Science (JP)
- National Institute for Materials Science (JP)
- Corresponding author
- He, Congli
- Zhang, Guangyu
- ORCID
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2021-03-24
- Volume
- 31
- Issue
- 27
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W3163773863
- Cited by
- 163
- References
- 7
- Citations by year
- 2026: 24 · 2025: 47 · 2024: 35 · 2023: 25 · 2022: 24 · 2021: 8
- FWCI
- 6.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Advanced Functional Materials (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (61888102, 11834017, 61734001, 51901025, 61974081, 91964104)
- Ministry of Education, Culture, Sports, Science and Technology (JPMXP0112101001)
- Japan Society for the Promotion of Science (JP20H00354)
- Core Research for Evolutional Science and Technology (JPMJCR15F3)
- National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (61888102)
- 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
- artificial synapses, neuromorphic computing, two-dimensional materials, van der Waals materials, device reliability, energy-efficient neural networks, MNIST classification
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