Oxide dendrite transistors gated with polyvinyl alcohol/chitosan hybrid electrolyte for spatiotemporal integration
Inspired by the brain computing mode, significant efforts have been dedicated to developing neuromorphic devices to realize energy-efficient neuromorphic systems. Specifically, a neuron can deal with spatiotemporal input patterns with dendrites, meaningful for multi-modal responses of our perceptual system. Here, a multi-gate blended polyvinyl alcohol/chitosan based electrolyte gated oxide dendrite transistor was fabricated. With extremely strong interfacial protonic/electronic coupling, the proposed dendrite transistors exhibit rich neuromorphic functions under heterosynaptic mechanism. Spatiotemporal dendritic integration and linear/nonlinear dendritic algorithms were demonstrated, showing its versatility in complex bionic neuronal processes. Logic operations were successfully implemented, including “OR”, “AND”, “NOR”, “NAND”. Capacitively coupled multi-terminal oxide dendrite transistors are also proposed for data classification and information decoding. Finally, neural addition operation was realized on a single dendrite transistor. These results indicate that the present neuromorphic devices could be building blocks for neuromorphic systems, offering an interesting strategy toward efficient and compact neuromorphic architectures. • A multi-gate PVA/CS based electrolyte gated oxide dendrite transistor is fabricated. • The dendrite transistor exhibits rich neuromorphic functions under heterosynaptic mechanism. • Spatiotemporal dendritic integration and dendritic algorithms are demonstrated. • The dendrite transistor is proposed for data classification and information decoding. • Neural addition operation was realized on a single dendrite transistor.
Authors
- Wei Sheng Wang
- Hui Xiao (ORCID: https://orcid.org/0000-0001-8357-2917)
- Li Zhu (ORCID: https://orcid.org/0000-0003-1583-2670)
- You Jie Huang
- Jia Kang Di (ORCID: https://orcid.org/0009-0000-0752-4753)
- Xin Huang (ORCID: https://orcid.org/0000-0002-7071-8873)
Institutions
- Ningbo University (CN)
- Chinese Academy of Sciences (CN)
- Ningbo Institute of Industrial Technology (CN)
Publication Details
- Journal
- Journal of Alloys and Compounds
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1016/j.jallcom.2024.177938
- Citations
- 5
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.04