4.6 Article

Multilevel resistive random access memory achieved by MoO3/Hf/MoO3 stack and its application in tunable high-pass filter

期刊

NANOTECHNOLOGY
卷 32, 期 38, 页码 -

出版社

IOP PUBLISHING LTD
DOI: 10.1088/1361-6528/ac0ac4

关键词

tunable high-pass filter; multilevel RRAM; retention property; MoO3

资金

  1. National Natural Science Foundation of China [61704137, 61974026]
  2. Key R&D plan of Shaanxi Province [2020GY-021]
  3. Fundamental Research Funds for the Central Universities [xjh012020009]
  4. Opening Project of Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences

向作者/读者索取更多资源

This work achieved multilevel resistive random access memories (RRAMs) with multiple stable resistance states, attributing the resistive switching mechanism to conductive filament and redox reaction. By utilizing RRAM, a tunable high-pass filter with configurable filtering characteristics was realized, demonstrating high resolution and wide programming range.
In this work, the multilevel resistive random access memories (RRAMs) have been achieved by using the structure of Pt/MoO3/Hf/MoO3/Pt with four stable resistance states. The devices show good retention property of each state (>10(4 )s) and large memory window (>10(4)). The simulation and experimental study reveal that the resistive switching mechanism is ascribed to combination of the conductive filament in the stack of MoO3/Hf next to the top electrode and redox reaction at the interface of Hf/MoO3 next to bottom electrode. The fitting results of current-voltage characteristics under low sweep voltage indicate that the conduction of HRSs is dominated by the Poole-Frenkel emission and that of LRS is governed by the Ohmic conduction. Based on the RRAM, the tunable high-pass filter (HPF) with configurable filtering characteristics has been realized. The gain-frequency characteristics of the programmable HPF show that the filter has high resolution and wide programming range, demonstrating the viability of the multilevel RRAMs for future spiking neural network and shrinking the programmable filters with low power consumption.

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