Ferroelectric Field-Effect-Transistor Integrated with Ferroelectrics Heterostructureopen access
- Authors
- Baek, Sungpyo; Yoo, Hyun Ho; Ju, Jae Hyeok; Sriboriboon, Panithan; Singh, Prashant; Niu, Jingjie; Park, Jin-Hong; Shin, Changhwan; Kim, Yunseok; Lee, Sungjoo
- Issue Date
- 7월-2022
- Publisher
- WILEY
- Keywords
- ferroelectric semiconductors; ferroelectronics; van der Waals ferroelectric heterostructures
- Citation
- ADVANCED SCIENCE, v.9, no.21
- Indexed
- SCIE
SCOPUS
- Journal Title
- ADVANCED SCIENCE
- Volume
- 9
- Number
- 21
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/142161
- DOI
- 10.1002/advs.202200566
- ISSN
- 2198-3844
- Abstract
- To address the demands of emerging data-centric computing applications, ferroelectric field-effect transistors (Fe-FETs) are considered the forefront of semiconductor electronics owing to their energy and area efficiency and merged logic-memory functionalities. Herein, the fabrication and application of an Fe-FET, which is integrated with a van der Waals ferroelectrics heterostructure (CuInP2S6/alpha-In2Se3), is reported. Leveraging enhanced polarization originating from the dipole coupling of CIPS and alpha-In2Se3, the fabricated Fe-FET exhibits a large memory window of 14.5 V at V-GS = +/- 10 V, reaching a memory window to sweep range of approximate to 72%. Piezoelectric force microscopy measurements confirm the enhanced polarization-induced wider hysteresis loop of the double-stacked ferroelectrics compared to single ferroelectric layers. The Landau-Khalatnikov theory is extended to analyze the ferroelectric characteristics of a ferroelectric heterostructure, providing detailed explanations of the hysteresis behaviors and enhanced memory window formation. The fabricated Fe-FET shows nonvolatile memory characteristics, with a high on/off current ratio of over 10(6), long retention time (>10(4) s), and stable cyclic endurance (>10(4) cycles). Furthermore, the applicability of the ferroelectrics heterostructure is investigated for artificial synapses and for hardware neural networks through training and inference simulation. These results provide a promising pathway for exploring low-dimensional ferroelectronics.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.