Impact of the Ferroelectric and Interface Layer Optimization in an MFIS HZO based Ferroelectric Tunnel Junction for Neuromorphic based Synaptic Storage

被引:6
|
作者
Ali, Tarek [1 ]
Suenbuel, Ayse [1 ]
Mertens, Konstantin [1 ]
Revello, Ricardo [1 ]
Lederer, Maximilian [1 ]
Lehninger, David [1 ]
Mueller, Franz [1 ]
Kuehnel, Kati [1 ]
Rudolph, Matthias [1 ]
Oehler, Sebastien [1 ]
Hoffmann, Raik [1 ]
Zimmermann, Katrin [1 ]
Biedermann, Kati [1 ]
Schramm, Philipp [1 ]
Czernohorsky, Malte [1 ]
Seidel, Konrad [1 ]
Kaempfe, Thomas [1 ]
Eng, Lukas M. [2 ]
机构
[1] Fraunhofer IPMS Ctr Nanoelect Technol, Bartlake Str 5, D-01109 Dresden, Germany
[2] Tech Univ Dresden, Inst Angew Phys, Nothnitzer Str 61, D-01187 Dresden, Germany
关键词
Ferroelectric; HZO; MFIS; FTJ; synaptic device;
D O I
10.1109/SNW51795.2021.00032
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The stack structure tuning of the ferroelectric tunnel junction (FTJ) devices is reported based on the ferroelectric (FE) layer thickness and interface layer (IL) type/thickness optimization to maximize the FTJ I-on/I-off ratio. A FE thickness scaling shows a low voltage FTJ operation, further challenged by a diminishing trend in the maximum I-on/I-off ratio due to the thickness dependence of the FE polarization, independent of the IL thickness. The maximum I-on/I-off ratio varies by tuning the IL type (SiO2, Al2O3) and thickness (1 nm, 2 nm), indicating a maximum at the SiO2 (1 nm) IL condition. A stable endurance of 10(4) cycles is limited by the high field/cycles induced IL degradation, a stable FTJ at 10y extrapolated retention time is shown. The FTJ synaptic device operation is reported with insight on the stack structure tuning impact on the synaptic LTP/LTD nonlinearity and maximum dynamic range.
引用
收藏
页码:61 / 62
页数:2
相关论文
共 50 条
  • [21] A computational study of AlScN-based ferroelectric tunnel junction
    Yang, Ning
    Cheng, Guoting
    Guo, Jing
    SOLID-STATE ELECTRONICS, 2025, 223
  • [22] Carbon Nanotube-Based Flexible Ferroelectric Synaptic Transistors for Neuromorphic Computing
    Xia, Fan
    Xia, Tian
    Xiang, Li
    Ding, Sujuan
    Li, Shuo
    Yin, Yucheng
    Xi, Meiqi
    Jin, Chuanhong
    Liang, Xuelei
    Hu, Youfan
    ACS APPLIED MATERIALS & INTERFACES, 2022, : 30124 - 30132
  • [23] High Performance and Self-rectifying Hafnia-based Ferroelectric Tunnel Junction for Neuromorphic Computing and TCAM Applications
    Goh, Youngin
    Hwang, Junghyeon
    Kim, Minki
    Jung, Minhyun
    Lim, Sehee
    Jung, Seong-Ook
    Jeon, Sanghun
    2021 IEEE INTERNATIONAL ELECTRON DEVICES MEETING (IEDM), 2021,
  • [24] A Ferroelectric Tunnel Junction-based Integrate-and-Fire Neuron
    Gibertini, P.
    Fehlings, L.
    Lancaster, S.
    Duong, Q. T.
    Mikolajick, T.
    Dubourdieu, C.
    Slesazeck, S.
    Covi, E.
    Deshpande, V
    2022 29TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (IEEE ICECS 2022), 2022,
  • [25] Highly Stable Artificial Synapses Based on Ferroelectric Tunnel Junctions for Neuromorphic Computing Applications
    Song, Sungmun
    Ham, Woori
    Park, Gyuil
    Kho, Wonwoo
    Kim, Jisoo
    Hwang, Hyunjoo
    Kim, Hyo-Bae
    Song, Hyunsun
    Ahn, Ji-Hoon
    Ahn, Seung-Eon
    ADVANCED MATERIALS TECHNOLOGIES, 2022, 7 (07)
  • [26] Effect of Al Concentration on Ferroelectric Properties in HfAlOx-Based Ferroelectric Tunnel Junction Devices for Neuroinspired Applications
    Kim, Jihyung
    Kim, Dahye
    Min, Kyung Kyu
    Kraatz, Matthias
    Han, Taeyoung
    Kim, Sungjun
    ADVANCED INTELLIGENT SYSTEMS, 2023, 5 (08)
  • [27] Bismuth-based ferroelectric memristive device induced by interface barrier for neuromorphic computing
    Chen, Zhi-Long
    Xiao, Yang
    Zheng, Yang-Fan
    Jiang, Yan-Ping
    Liu, Qiu-Xiang
    Tang, Xin-Gui
    MATERIALS TODAY ELECTRONICS, 2024, 8
  • [28] Theoretical Study of Bilayer Composite Barrier Based Ferroelectric Tunnel Junction Memory
    Duan, Huali
    Fang, Wenxiao
    Liu, Leitao
    Chen, Wenchao
    2020 IEEE MTT-S INTERNATIONAL CONFERENCE ON NUMERICAL ELECTROMAGNETIC AND MULTIPHYSICS MODELING AND OPTIMIZATION (NEMO 2020), 2020,
  • [29] Characterizing HfO2-Based Ferroelectric Tunnel Junction in Cryogenic Temperature
    Hur, Jae
    Park, Chinsung
    Choe, Gihun
    Ravindran, Prasanna Venkatesan
    Khan, Asif Islam
    Yu, Shimeng
    IEEE TRANSACTIONS ON ELECTRON DEVICES, 2022, 69 (10) : 5948 - 5951
  • [30] FerroCoin: Ferroelectric Tunnel Junction-Based True Random Number Generator
    Chatterjee, Swetaki
    Rangarajan, Nikhil
    Patnaik, Satwik
    Rajasekharan, Dinesh
    Sinanoglu, Ozgur
    Chauhan, Yogesh Singh
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 2023, 11 (02) : 541 - 547