Improving linearity by introducing Al in HfO2 as a memristor synapse device

被引：106

作者：

Chandrasekaran, Sridhar ^{[1
]}

Simanjuntak, Firman Mangasa ^{[2
]}

Saminathan, R. ^{[3
]}

Panda, Debashis ^{[4
]}

Tseng, Tseung-Yuen ^{[5
,6
]}

机构：

[1] Natl Chiao Tung Univ, Dept Elect Engn & Comp Sci, Hsinchu 30010, Taiwan

[2] Tohoku Univ, Adv Inst Mat Res, WPI, Sendai, Miyagi 9808577, Japan

[3] Natl Tsing Hua Univ, Dept Chem Engn, Hsinchu 30010, Taiwan

[4] Natl Inst Sci & Technol, Elect Engn & Phys Dept, Berhampur 761008, Orissa, India

[5] Natl Chiao Tung Univ, Dept Elect Engn, Hsinchu 30010, Taiwan

[6] Natl Chiao Tung Univ, Inst Elect, Hsinchu 30010, Taiwan

来源：

NANOTECHNOLOGY | 2019年 / 30卷 / 44期

关键词：

artificial synaptic; neuromorphic; resistive switching; memristor; RESISTIVE MEMORY; OXIDE; RRAM; POTENTIATION; DEPRESSION; FILMS;

D O I：

10.1088/1361-6528/ab3480

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Artificial synapse having good linearity is crucial to achieve an efficient learning process in neuromorphic computing. It is found that the synaptic linearity can be enhanced by engineering the doping region across the switching layer. The nonlinearity of potentiation and depression of the pure device is 36% and 91%, respectively; meanwhile, the nonlinearity after doping can be suppressed to be 22% (potentiation) and 60% (depression). Henceforth, the learning accuracy of the doped device is 91% with only 13 iterations; meanwhile, the pure device is 78%. A detailed conduction mechanism to understand this phenomenon is proposed.

引用

页数：9

共 33 条

[1] Amit D. J., 1989, MODELING BRAIN FUNCT, DOI DOI 10.1017/CBO9780511623257
[2] Long-term potentiation and long-term depression: a clinical perspective
Bliss, Timothy V. P.
Cooke, Sam F.
[J]. CLINICS, 2011, 66 : 3 - 17
[3] Tuning Resistive, Capacitive, and Synaptic Properties of Forming Free TiO2-x-Based RRAM Devices by Embedded Pt and Ta Nanocrystals
Bousoulas, Panagiotis
Karageorgiou, Ismini
Aslanidis, Vaggelis
Giannakopoulos, Kostas
Tsoukalas, Dimitris
[J]. PHYSICA STATUS SOLIDI A-APPLICATIONS AND MATERIALS SCIENCE, 2018, 215 (03):
[4] Low-Power Forming Free TiO2-x/HfO2-y/TiO2-x-Trilayer RRAM Devices Exhibiting Synaptic Property Characteristics
Bousoulas, Panagiotis
Michelakaki, Irini
Skotadis, Evangelos
Tsigkourakos, Menelaos
Tsoukalas, Dimitris
[J]. IEEE TRANSACTIONS ON ELECTRON DEVICES, 2017, 64 (08) : 3151 - 3158
[5] Engineering of the Chemical Reactivity of the Ti/HfO2 Interface for RRAM: Experiment and Theory.
Calka, Pauline
Sowinska, Malgorzata
Bertaud, Thomas
Walczyk, Damian
Dabrowski, Jarek
Zaumseil, Peter
Walczyk, Christian
Gloskovskii, Andrei
Cartoixa, Xavier
Sune, Jordi
Schroeder, Thomas
[J]. ACS APPLIED MATERIALS & INTERFACES, 2014, 6 (07) : 5056 - 5060
[6] Chen Y., 2014, J. Nanomater, V86, P1
[7] Implementing ceramic materials into neuromorphic memory devices through oxide-based memristors
Rutherford, Bethany X.
[J]. AMERICAN CERAMIC SOCIETY BULLETIN, 2021, 100 (06): : 48 - 48
[8] Structural and XPS characterization of ALD Al2O3 coated porous silicon
Iatsunskyi, Igor
Kempinski, Mateusz
Jancelewicz, Mariusz
Zaleski, Karol
Jurga, Stefan
Smyntyna, Valentyn
[J]. VACUUM, 2015, 113 : 52 - 58
[9] Input Voltage Mapping Optimized for Resistive Memory-Based Deep Neural Network Hardware
Kim, Taesu
Kim, Hyungjun
Kim, Jinseok
Kim, Jae-Joon
[J]. IEEE ELECTRON DEVICE LETTERS, 2017, 38 (09) : 1228 - 1231
[10] Multilevel resistive switching in ternary HfxAl1-xOy oxide with graded Al depth profile
Markeev, A.
Chouprik, A.
Egorov, K.
Lebedinskii, Yu.
Zenkevich, A.
Orlov, O.
[J]. MICROELECTRONIC ENGINEERING, 2013, 109 : 342 - 345

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