Alkali Ion‐Incorporated HfO <sub>2</sub> Dielectrics for Reconfigurable Neuromorphic Computing
Abstract Neuromorphic computing system requires precise control over synaptic retention characteristics to emulate both short‐term and long‐term memory functions of biological neural networks. Here, a solution‐processed approach is presented for the fabrication of alkali metal cation‐embedded hafnium dioxide (HfO 2 ) dielectrics that promote tunable synaptic behavior in transistor‐based electronics. By incorporating sodium fluoride (NaF) into electrochemically exfoliated hafnium disulfide (HfS 2 ) nanosheets, followed by thermal oxidation, a uniform, ultrathin (≈8 nm) HfO 2 film is produced with embedded mobile alkali ions on a wafer‐scale substrate. With a semiconducting indium gallium zinc oxide layer, the resulting transistors demonstrate distinct retention characteristics, with pristine HfO 2 ‐based devices exhibiting short‐term switching behavior and NaF‐incorporated HfO 2 (NaF‐HfO 2 )‐based devices demonstrating long‐term retention exceeding 30 s with tunable synaptic plasticity. It successfully demonstrates the practical application of these ion‐engineered dielectrics in 1‐transistor‐1‐synapse memory arrays, synaptic comparators, and reconfigurable AND/OR logic gates that mimic brain‐like information processing. The solution‐processable approach expands the range of dielectric modification, providing a scalable pathway for next‐generation neuromorphic computing technologies with controllable synaptic functionalities.
Authors
- Dong Gue Roe (ORCID: https://orcid.org/0000-0002-2889-3379)
- Seonkwon Kim (ORCID: https://orcid.org/0000-0001-6382-2018)
- Cheol Kwak (ORCID: https://orcid.org/0000-0002-1987-2111)
- Jeong Ho Cho (ORCID: https://orcid.org/0000-0002-1030-9920)
- Seung Jun Ki (ORCID: https://orcid.org/0000-0001-5031-6036)
- Joohoon Kang (ORCID: https://orcid.org/0000-0002-6578-2547)
- Se‐Jin Kim (ORCID: https://orcid.org/0009-0005-8375-5955)
- Jihyun Kim (ORCID: https://orcid.org/0000-0001-8493-2881)
Institutions
- Yonsei University (KR)
- University of Pennsylvania (US)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-11-13
- DOI
- https://doi.org/10.1002/adfm.202521002
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- Ministry of Trade, Industry and Energy
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea