A Programmable Nonvolatile Schottky Diode Based on van der Waals Ferroelectric Junction

Programmable and nonvolatile Schottky junctions are highly desirable for next-generation electronic and neuromorphic systems. However, conventional metal-semiconductor and even van der Waals (vdW) Schottky diodes often suffer from fixed rectifying behaviors or limited tunability. Here, we report a programmable nonvolatile ferroelectric Schottky diode based on a vdW heterojunction between semimetallic 1T'-MoTe<sub>2</sub> and ferroelectric α-In<sub>2</sub>Se<sub>3</sub>. The diode exhibits near-ideal performance, including a rectification ratio exceeding 10<sup>4</sup>, a leakage current down to 1 pA, and an ideality factor as low as 1.38. By switching ferroelectric polarization, the Schottky barrier can be modulated in a programmable manner, enabling reversible, nonvolatile, and multilevel rectification states. The device demonstrates polarization-dependent photoresponse and transient integrate-and-leak dynamics, closely resembling biological spiking neurons. A spiking neural network is implemented based on this behavior, achieving image recognition accuracy up to 98.4%. This work establishes programmable ferroelectric Schottky diodes as promising candidates for low-power memory, reconfigurable logic, and neuromorphic vision.

Authors

Institutions

Publication Details

Journal
Nano Letters
Published
2025-06-24
DOI
https://doi.org/10.1021/acs.nanolett.5c02646
Citations
6
Primary Topic
2D Materials and Applications
Type
article
Field-Weighted Citation Impact
3.11

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Programmable Nonvolatile Schottky Diode Based on van der Waals Ferroelectric Junction

Xin He, Liang Zou, Zhongyi Wang, Zongwen Li et al.
6 citations
Nano Letters
2D Materials and Applications
3.11
article

A Programmable Nonvolatile Schottky Diode Based on van der Waals Ferroelectric Junction

Xin He, Liang Zou, Zhongyi Wang, Zongwen Li, Zheng‐Dong Luo, Bin Yu, Fei Xue, Zhenhua Wu, Genquan Han, Shangui Lan, Dongxing Zheng, Pu Feng, Xixiang Zhang, Yang Xu, Haoran Xu, Yinchang Ma, Yan Liu, Tao Wang, Wenyu Chen, Baoyu Wang, Lijun Xu
article en
6 citations

Abstract

Programmable and nonvolatile Schottky junctions are highly desirable for next-generation electronic and neuromorphic systems. However, conventional metal-semiconductor and even van der Waals (vdW) Schottky diodes often suffer from fixed rectifying behaviors or limited tunability. Here, we report a programmable nonvolatile ferroelectric Schottky diode based on a vdW heterojunction between semimetallic 1T'-MoTe<sub>2</sub> and ferroelectric α-In<sub>2</sub>Se<sub>3</sub>. The diode exhibits near-ideal performance, including a rectification ratio exceeding 10<sup>4</sup>, a leakage current down to 1 pA, and an ideality factor as low as 1.38. By switching ferroelectric polarization, the Schottky barrier can be modulated in a programmable manner, enabling reversible, nonvolatile, and multilevel rectification states. The device demonstrates polarization-dependent photoresponse and transient integrate-and-leak dynamics, closely resembling biological spiking neurons. A spiking neural network is implemented based on this behavior, achieving image recognition accuracy up to 98.4%. This work establishes programmable ferroelectric Schottky diodes as promising candidates for low-power memory, reconfigurable logic, and neuromorphic vision.

Nano LettersVol. 25(26)
Xidian University (CN), Chinese Academy of Sciences (CN), Collaborative Innovation Center of Quantum Matter (CN), King Abdullah University of Science and Technology (SA), Zhejiang University (CN)
National Natural Science Foundation of China, King Abdullah University of Science and Technology, China Scholarship Council, Natural Science Foundation of Ningbo, Natural Science Foundation of Zhejiang Province
Affordable and clean energy
Openalex Percentile: Top 8%
2D Materials and Applications
3.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.