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
来源
2021 SILICON NANOELECTRONICS WORKSHOP (SNW) | 2021年
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 6 条
[1]  
Ali T., 2018, TED, P3769
[2]  
Boscke T.S., 2011, APPL PHYS LETT
[3]   Back-End-of-Line Compatible Low-Temperature Furnace Anneal for Ferroelectric Hafnium Zirconium Oxide Formation [J].
Lehninger, David ;
Olivo, Ricardo ;
Ali, Tarek ;
Lederer, Maximilian ;
Kaempfe, Thomas ;
Mart, Clemens ;
Biedermann, Kati ;
Kuehnel, Kati ;
Roy, Lisa ;
Kalkani, Mahsa ;
Seidel, Konrad .
PHYSICA STATUS SOLIDI A-APPLICATIONS AND MATERIALS SCIENCE, 2020, 217 (08)
[4]   Hafnia-Based Double-Layer Ferroelectric Tunnel Junctions as Artificial Synapses for Neuromorphic Computing [J].
Max, Benjamin ;
Hoffmann, Michael ;
Mulaosmanovic, Halid ;
Slesazeck, Stefan ;
Mikolajick, Thomas .
ACS APPLIED ELECTRONIC MATERIALS, 2020, 2 (12) :4023-4033
[5]  
Mulasmanovic H., 2020, TED, P3466
[6]  
Rya H., 2019, SCI REP, V9, P20383