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Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
Published on: February 4, 2018
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Multilevel resistive random access memory achieved by MoO3/Hf/MoO3stack and its application in tunable high-pass
Sheng Li Fang1, Chuan Yu Han1, Wei Hua Liu1
1School of Microelectronics, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.
Nanotechnology
|June 11, 2021
Summary
Multilevel resistive random access memories (RRAMs) with four stable states were developed. These RRAMs enable tunable high-pass filters, showing potential for advanced neural networks and low-power electronics.
Area of Science:
- Materials Science
- Electrical Engineering
- Nanotechnology
Background:
- Resistive random access memories (RRAMs) are promising for next-generation electronic devices.
- Achieving multilevel resistance states is crucial for higher data storage density and complex functionalities.
- Tunable filters are essential components in various electronic systems.
Purpose of the Study:
- To develop and characterize multilevel RRAM devices with multiple stable resistance states.
- To investigate the resistive switching mechanism in the fabricated RRAM structure.
- To demonstrate the application of these multilevel RRAMs in creating tunable high-pass filters.
Main Methods:
- Fabrication of Pt/MoO3/Hf/MoO3/Pt stack for multilevel RRAM.
- Experimental characterization of device performance, including retention and memory window.
- Electrical analysis using current-voltage (I-V) measurements and fitting to conduction models (Poole-Frenkel, Ohmic).
- Simulation of device behavior and RRAM-based tunable high-pass filter (HPF).
Main Results:
- Achieved RRAM devices with four stable resistance states and good retention (>10^4 s).
- Demonstrated a large memory window (>10^4) for the multilevel RRAMs.
- Identified the switching mechanism as a combination of conductive filament formation and redox reactions.
- Conduction mechanisms identified as Poole-Frenkel emission (HRS) and Ohmic conduction (LRS).
- Successfully realized a tunable high-pass filter with configurable characteristics using the multilevel RRAMs.
Conclusions:
- The developed multilevel RRAMs exhibit excellent performance characteristics.
- The understanding of the resistive switching mechanism provides insights for further device optimization.
- The successful implementation of tunable HPFs highlights the potential of these RRAMs for applications in spiking neural networks and compact, low-power electronic filters.
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