Interface Element Accumulation‐Induced Single Ferroelectric Domain for High‐Performance Neuromorphic Synapse
Abstract Ferroelectric (FE) synapses are promising for neuromorphic computing toward enhanced artificial intelligence systems. Nonetheless, there is a significant gap in understanding how to effectively tailor self‐polarization and its implications on synaptic device performance. Here, an approach using interfacial element accumulation is reported to tailor the self‐polarization states of BaTiO 3 (BTO)/La 0.67 Sr 0.33 MnO 3 (LSMO) FE heterostructure into a single domain state. This single domain configuration results are demonstrated in a gradient distribution of oxygen vacancies across the film thickness, yielding an extraordinary on/off ratio of 10 7 in Pt/BTO/LSMO FE diodes. This giant resistive switching enables the long‐term potentiation and long‐term depression synaptic functions of excellent linearity and symmetry (with a nonsymmetry factor as low as 0.1), leading to a supervised learning ability of the associated artificial neural network with a high pattern recognition accuracy of 95%. This work provides a simple design principle for FE single domain, which is substantial in enhancing the performance of FE synapses for neuromorphic computing.
Authors
- Weijin Hu (ORCID: https://orcid.org/0000-0001-5862-1481)
- Fan Xu (ORCID: https://orcid.org/0000-0003-3910-5398)
- Yizhuo Li (ORCID: https://orcid.org/0009-0004-8871-192X)
- Biaohong Huang
- Zhen Hong Fan (ORCID: https://orcid.org/0000-0002-1756-641X)
- Yun‐Long Tang (ORCID: https://orcid.org/0000-0001-6555-3296)
- Sajjad Ali (ORCID: https://orcid.org/0000-0002-0134-5217)
- M. Bououdina (ORCID: https://orcid.org/0000-0001-8770-7129)
- Yuxuan Jiang (ORCID: https://orcid.org/0000-0001-7971-8354)
- Teng Yang (ORCID: https://orcid.org/0000-0003-3773-7586)
- Han‐Chieh Wu (ORCID: https://orcid.org/0000-0003-2638-7883)
- Zhidong Zhang (ORCID: https://orcid.org/0000-0002-6203-0171)
- Yujia Wang (ORCID: https://orcid.org/0000-0001-8434-4801)
- Jiaqi Liu (ORCID: https://orcid.org/0000-0003-1963-003X)
- Xiaoqi Li (ORCID: https://orcid.org/0000-0001-6393-102X)
- Haoyue Deng
Institutions
- University of Science and Technology of China (CN)
- Prince Sultan University (SA)
- South China Normal University (CN)
- Chinese Academy of Sciences (CN)
- China Special Equipment Inspection and Research Institute (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-02-19
- DOI
- https://doi.org/10.1002/adfm.202423225
- Citations
- 12
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 8.77
Funders
- National Natural Science Foundation of China
- Chinese Academy of Sciences
- Youth Innovation Promotion Association of the Chinese Academy of Sciences
- Prince Sultan University
- National Key Research and Development Program of China
- Youth Innovation Promotion Association