Loading…

Structural Engineering of H 0.5 Z 0.5 O 2 ‐Based Ferroelectric Tunneling Junction for Fast‐Speed and Low‐Power Artificial Synapses

Advanced synaptic devices capable of neuromorphic data processing are widely studied as the building block in the next‐generation computing architecture for artificial intelligence applications. Due to its fast speed, low power, and excellent complementary metal‐oxide‐semiconductor (CMOS) compatibil...

Full description

Saved in:
Bibliographic Details
Published in:Advanced electronic materials 2023-05, Vol.9 (5)
Main Authors: Cao, Yuanyuan, Liu, Yilun, Yang, Yafen, Li, Qingxuan, Zhang, Tianbao, Ji, Li, Zhu, Hao, Chen, Lin, Sun, Qingqing, Zhang, David Wei
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Advanced synaptic devices capable of neuromorphic data processing are widely studied as the building block in the next‐generation computing architecture for artificial intelligence applications. Due to its fast speed, low power, and excellent complementary metal‐oxide‐semiconductor (CMOS) compatibility, Zr‐doped HfO 2 (HZO)‐based ferroelectric tunnel junction (FTJ) are promising candidates as a new type of non‐volatile memory for neuromorphic device applications. Here, an experimental approach is reported to enhance the tunneling efficiency and the electrical performance by engineering the dielectric stack of the FTJ device. By sandwiching the HZO ferroelectric layer with ZrO 2 and Al 2 O 3 layers, the FTJ tunneling current is greatly increased with lowered barrier, larger remnant polarization (P r ), and tunneling electrical resistance ratio as well as suppressed leakage current have been achieved. The optimized FTJ devices are further implemented emulating synaptic functions with demonstrated short/long‐term synaptic plasticity and spike‐timing‐dependent plasticity behaviors. Such engineering in HZO‐based FTJ devices can be promising and instructive for the realization of future ultra‐low‐power and CMOS‐compatible neuromorphic devices and systems.
ISSN:2199-160X
2199-160X
DOI:10.1002/aelm.202201247