Fe‐Doped Boehmite‐Based Memristor With Biocompatibility for Protonic Synapse
ABSTRACT The rapid development of memristors in implantable brain‐like applications urgently requires materials that possess both excellent durability and biocompatibility as the dielectrics. In this study, we employed biocompatible Fe‐doped boehmite material as the dielectric in a memristor, showcasing the preparation of Pt/Fe doped boehmite/ITO memristors, exhibiting proton synapse function. The device exhibits excellent endurance characteristics, retaining its high resistance state (HRS) and low resistance state (LRS) for 10 3 s and 10 3 cycles, respectively. Additionally, it is capable of simulating the functionality of synaptic including long‐term potentiation/depression (LTP/LTD) and plasticity that depends on spike rate/spike time. By harnessing the LTP/LTD functionality embedded in an artificial neural network, a recognition rate of 93.7% in handwritten digit recognition is achieved. To corroborate these findings, we conducted first‐principles calculations using density functional theory (DFT). The results confirm that the resistive switching mechanism observed in Fe‐doped boehmite memristors is closely linked to proton conduction within the resistive switching layer. This insight introduces a novel perspective for exploring the resistive switching mechanisms in proton‐conductive memristors and paves the way for future biocompatible electronic devices.
Authors
- Jianling Yue (ORCID: https://orcid.org/0000-0002-1089-6934)
- Chang Guo (ORCID: https://orcid.org/0000-0002-5833-8552)
- Jun Chen (ORCID: https://orcid.org/0000-0001-6402-101X)
- Haonan Wang (ORCID: https://orcid.org/0000-0003-1587-8628)
- Jialin Wang (ORCID: https://orcid.org/0000-0002-2839-553X)
- Yanting Li (ORCID: https://orcid.org/0000-0002-3948-4546)
- Linxi Xu
- Siqi Zhu
- Ze Wang
- Xu Zhao
- Ziqian Zhang
Institutions
- Central South University (CN)
- Ganzhou People's Hospital (CN)
- Henan Nonferrous Metals Geological Exploration Institute (CN)
- Powder Metallurgy Institute (BY)
Publication Details
- Journal
- Advanced Materials Technologies
- Published
- 2026-09-12
- DOI
- https://doi.org/10.1002/admt.71308
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Hunan Province