Analog HfxZr1-xO2 Memristors with Tunable Linearity for Implementation in a Self-Organizing Map Neural Network

Quanzhou,Zhu1; Biyi,Jiang1; Jun,Lan1; Zeyu,Hou1; Yida,Dong1; Zhongrui,Wang2; Xuewei,Feng3; Mei,Shen1; Hongyu,Yu1; Kai,Chen1; Jiamin,Li1; Longyang,Lin1; Feichi,Zhou1; Yida,Li1

2023-12-27

10.1002/aelm.202300508

Advanced Electronic Materials   影响因子和分区

2199-160X

Doped-metal oxide-based memristors, with the potential for improved switching performance and capability for multi-bit information storage, are attractive candidates in the implementation of artificial neural network (ANN) hardware systems. However, performance and process considerations such as switching behavior and complementary-metal-oxide-semiconductor (CMOS) process compatibility remain a challenge. This study shows that amorphous Zr-doped HfO2 (HZO) memristors fabricated via a co-sputtering approach improve the switching performance by providing a controllable knob to modulate defects in the switching layer. At the same time, it satisfies the CMOS process compatibility requirements for industry adoption. HZO memristors with optimized stoichiometry exhibit 30% reduced switching voltages and 50% faster switching as compared to control HfO2 memristors. Concurrently, this study shows that high linearity analog states tuning is achievable via a programming scheme that utilizes voltage pulses with increasing amplitudes. This study further shows via simulation evaluation that HZO memristors implemented in a self-organizing-map (SOM) network for Fashion MNIST database classification, achieve an accuracy of 92% with short training cycles. The results thus pave a potential pathway for further development of CMOS process compatible HZO memristors for use in future storage and computing applications.

hafnium oxide resistive switching memristor SOM

SCI ; EI

英语

第一 ; 通讯

Shenzhen Fundamental Research Program["JCYJ20220530115014032","JCYJ20220530115204009"] ; Young Innovative Talent Project Research Program[2021KQNCX077] ; Zhujiang Young Talent Program[2021QN02X362] ; Guangdong Provincial Department of Education Innovation Team Program[2021KCXTD012] ; Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation[pdjh2022b0455] ; null[62174074] ; null[52273246] ; null[62274081]

Science & Technology - Other Topics ; Materials Science ; Physics

Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied

WOS:001136272800001

Wiley

20235215295132

Classification (of information) ; CMOS integrated circuits ; Conformal mapping ; Memristors ; MOS devices ; Oxide semiconductors ; Self organizing maps ; Zirconium compounds

Semiconducting Materials:712.1 ; Semiconductor Devices and Integrated Circuits:714.2 ; Information Theory and Signal Processing:716.1 ; Chemical Products Generally:804 ; Information Sources and Analysis:903.1

人工提交

1.School of Microelectronics Southern University of Science and Technology Shenzhen 518055, China
2.Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong, SAR 999077, China
3.Shanghai Jiao Tong University Shanghai 200240, China

Quanzhou,Zhu,Biyi,Jiang,Jun,Lan,et al. Analog HfxZr1-xO2 Memristors with Tunable Linearity for Implementation in a Self-Organizing Map Neural Network[J]. Advanced Electronic Materials,2023,10(4):1-11.

Quanzhou,Zhu.,Biyi,Jiang.,Jun,Lan.,Zeyu,Hou.,Yida,Dong.,...&Yida,Li.(2023).Analog HfxZr1-xO2 Memristors with Tunable Linearity for Implementation in a Self-Organizing Map Neural Network.Advanced Electronic Materials,10(4),1-11.

Quanzhou,Zhu,et al."Analog HfxZr1-xO2 Memristors with Tunable Linearity for Implementation in a Self-Organizing Map Neural Network".Advanced Electronic Materials 10.4(2023):1-11.