Polarity-dependent ferroelectric modulations in two-dimensional hybrid perovskite heterojunction transistors

Enlong Li, Weixin He, Ruixue Wang, Chi Zhang, Hongmiao Zhou, Yu Liu, Yijia Yuan, Kian Ping Loh (), Junhao Chu and Wenwu Li ()
Additional contact information
Enlong Li: Fudan University
Weixin He: National University of Singapore
Ruixue Wang: Fudan University
Chi Zhang: Fudan University
Hongmiao Zhou: Fudan University
Yu Liu: Fudan University
Yijia Yuan: National University of Singapore
Kian Ping Loh: National University of Singapore
Junhao Chu: Fudan University
Wenwu Li: Fudan University

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract The non-volatile spontaneous ferroelectric polarization field serves as a cornerstone for applying ferroelectric materials in electronic devices, yet it is frequently mitigated by charge trapping at defect sites. Achieving an effective transition between ferroelectric polarization and charge trapping is challenging due to the inherent opposition of the two mechanisms and the uncontrollable charge trapping types in ferroelectric materials. Here, we realized a polarity-dependent ferroelectric transition in two-dimensional ferroelectric heterojunction transistor by integrating a hybrid organic-inorganic ferroelectric layer embedded with electron trapping sites. Through theoretical calculations and experimental validation, we demonstrate a ferroelectric manifestation and elimination mechanism based on the polarity of the semiconductor layer. The electron-majority n-type semiconductor exhibits charge trapping behavior, while the electron-minority p-type transistor exhibits the ferroelectric control mechanism. Leveraging the mechanism transition, our bipolar heterojunction transistor enables synergistic heterogeneous control of non-volatile memory and volatile synaptic weight modulation within a single bipolar ferroelectric transistor. Based on the experimentally extracted parameters from the transistors, the device-informed simulation achieves a recognition accuracy of 92.9% and a 20.7-fold improvement in training efficiency of the transfer learning network.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-64387-x Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64387-x

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-64387-x

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().