铁电性
神经形态工程学
材料科学
堆栈(抽象数据类型)
光电子学
极化(电化学)
电压
图层(电子)
纳米技术
电气工程
计算机科学
化学
工程类
电介质
物理化学
程序设计语言
机器学习
人工神经网络
作者
Tarek Ali,Ayse Sünbül,Konstantin Mertens,Ricardo Revello,Maximilian Lederer,David Lehninger,Franz Mȕller,Kati Kühnel,Matthias Rudolph,Sebastien Oehler,Raik Hoffmann,Katrin Zimmermann,Kati Biedermann,Philipp Schramm,M. Czernohorsky,Konrad Seidel,Thomas Kämpfe,Lukas M. Eng
出处
期刊:IEEE Silicon Nanoelectronics Workshop
日期:2021-06-13
卷期号:: 1-2
被引量:3
标识
DOI:10.1109/snw51795.2021.00032
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 Ion/Ioffratio. A FE thickness scaling shows a low voltage FTJ operation, further challenged by a diminishing trend in the maximum Ion/Ioff ratio due to the thickness dependence of the FE polarization, independent of the IL thickness. The maximum lon/loff ratio varies by tuning the IL type (Si02, Ah03) and thickness (1 nm, 2 nm), indicating a maximum at the Si02 (1 nm) IL condition. A stable endurance of 104 cycles is limited by the high field/cycles induced IL degradation, a stable FTJ at lOy 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.
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