Ferroelectric Charged Domain-Wall Synapse for Neuromorphic Computing
Inspired by brain neural networks, integrated memory-computing devices are critical to meet the demands of big data and artificial intelligence. This work explores the quasi-continuous modulation of ferroelectric charged domain walls' conductance, which is confined in a topological quad-domain, allowing the charged domain walls to serve as neural synapses. The device mimics synaptic plasticity (long-term potentiation and depression) and shows paired impulse facilitation. In a designed ferroelectric domain-wall neural network, we demonstrate multiplicative, accumulation-additive operations between the input image and the stored response matrix, capable of image processing functions, including triclassification with 100% accuracy. In the neural network simulation, the MINST database and the Cifar-10 database achieve 98.7% and 95.1% recognition rates. The sub-nanosecond polarization switching and the ultrathin (3-5 nm) charged domain walls position them as a promising platform for advancing ultrafast and scalable synaptic devices for low-power (potentially reduced to 0.2 aJ with sub-nanosecond pulse durations) neuromorphic computing systems.
Authors
- Ying Fu (ORCID: https://orcid.org/0000-0002-8196-6493)
- Qinghua Liang (ORCID: https://orcid.org/0000-0002-1550-758X)
- Ji Ma (ORCID: https://orcid.org/0000-0002-1308-3226)
- Yuanyuan Fan (ORCID: https://orcid.org/0009-0001-1265-8825)
- Wael Ben Taazayet (ORCID: https://orcid.org/0000-0002-1996-8770)
- Houbing Huang (ORCID: https://orcid.org/0000-0002-8006-3495)
- Congli He (ORCID: https://orcid.org/0000-0002-7834-2719)
- Jiafang Li (ORCID: https://orcid.org/0000-0002-9658-8261)
- Xiaoming Shi (ORCID: https://orcid.org/0000-0001-7332-6835)
- Ce‐Wen Nan (ORCID: https://orcid.org/0000-0002-3261-4053)
- Liang Chen (ORCID: https://orcid.org/0000-0002-8893-3782)
- Yu Wang (ORCID: https://orcid.org/0000-0001-7288-9150)
- Yiming Liu (ORCID: https://orcid.org/0000-0002-8766-9318)
- Jing Wang
- Huayu Yang (ORCID: https://orcid.org/0009-0003-8564-9713)
Institutions
- Kunming University of Science and Technology (CN)
- Beijing Institute of Technology (CN)
- Beijing Normal University (CN)
- Zhuhai People's Hospital (CN)
- Division of Materials Science and Engineering (AU)
- Time Domain (United States) (US)
- Multidisciplinary Software Systems Research (United States) (US)
- Zhuhai Institute of Advanced Technology (CN)
- PRG S&Tech (South Korea)
- State Key Laboratory of New Ceramics and Fine Processing
- University of Science and Technology Beijing (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Nano Letters
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1021/acs.nanolett.5c02081
- Citations
- 6
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 4.38
Funders
- National Natural Science Foundation of China
- Beijing Municipal Natural Science Foundation
- National Key Research and Development Program of China