A High‐Performance Memristor Device and Its Filter Circuit Application

With the onset of the era of big data, memristors have been extensively studied for applications in nonvolatile neuromorphic computing due to their fast switching speed and low switching power ability and the complementary metal–oxide–semiconductor (CMOS) compatibility. In particular, their adjustable cutoff frequency for filtering applications is a significant advantage. However, for circuit applications, memristors are still in their early simulated stage. Herein, Ag/HfO2/graphene oxide quantum dot (GOQD)/Pt structure memristor devices are fabricated, where GOQDs are used as a stability boost, with biosynapse simulation achieved and numerical recognition performed at an accuracy of 90.91%. A circuit‐based filter is designed based on the memristor and STM32 microcontroller chip controlling, in which low‐pass, high‐pass, and band‐pass filter circuits are all realized. By changing the output signal of the control circuit of the resistance value of the memristor, the cutoff frequency of the filter is successfully adjusted. This work, therefore, paves a new way to obtain filter circuit applications of memristors for electronic systems; the overall performance of the GOQD‐inserted memristor is further optimized and its function in filtering realized, creating opportunities for further application of the memristor in information processing.

[1]  G. A. Campbell,et al.  Physical theory of the electric wave-filter , 1922 .

[2]  L. Chua.  Memristor-The missing circuit element , 1971 .

[3]  E. Fortune,et al.  Short-term synaptic plasticity as a temporal filter , 2001, Trends in Neurosciences.

[4]  Andrew G. Glen,et al.  APPL , 2001 .

[5]  W. Regehr,et al.  Short-term synaptic plasticity. , 2002, Annual review of physiology.

[6]  L. Abbott,et al.  Synaptic computation , 2004, Nature.

[7]  R. Waser,et al.  Nanoionics-based resistive switching memories. , 2007, Nature materials.

[8]  N. Xu,et al.  Bipolar switching behavior in TiN/ZnO/Pt resistive nonvolatile memory with fast switching and long retention , 2008 .

[9]  Leon O. Chua,et al.  Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors , 2009, Proceedings of the IEEE.

[10]  Massimiliano Di Ventra,et al.  Memristive circuits simulate memcapacitors and meminductors , 2009, 0910.1583.

[11]  S. Roche,et al.  Damaging graphene with ozone treatment: a chemically tunable metal-insulator transition. , 2010, ACS nano.

[12]  Yidong Xia,et al.  Effects of the electroforming polarity on bipolar resistive switching characteristics of SrTiO3−δ films , 2010 .

[13]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[14]  Ru Huang,et al.  Unipolar $\hbox{TaO}_{x}$-Based Resistive Change Memory Realized With Electrode Engineering , 2010, IEEE Electron Device Letters.

[15]  Massimiliano Di Ventra,et al.  Practical Approach to Programmable Analog Circuits With Memristors , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[16]  R. Ruoff,et al.  Graphene oxide thin films for flexible nonvolatile memory applications. , 2010, Nano letters.

[17]  K. Novoselov,et al.  From one electron to one hole: quasiparticle counting in graphene quantum dots determined by electrochemical and plasma etching. , 2010, Small.

[18]  B. Cho,et al.  Organic Resistive Memory Devices: Performance Enhancement, Integration, and Advanced Architectures , 2011 .

[19]  S. Schumacher.  Photophysics of graphene quantum dots: Insights from electronic structure calculations , 2011 .

[20]  T. Hasegawa,et al.  Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. , 2011, Nature materials.

[21]  Massimiliano Di Ventra,et al.  Memory materials: a unifying description , 2011 .

[22]  K. Müllen,et al.  Bottom-up fabrication of photoluminescent graphene quantum dots with uniform morphology. , 2011, Journal of the American Chemical Society.

[23]  Hui‐Ming Cheng,et al.  Synthesis and upconversion luminescence of N-doped graphene quantum dots , 2012 .

[24]  Leon O. Chua,et al.  A Voltage Mode Memristor Bridge Synaptic Circuit with Memristor Emulators , 2012, Sensors.

[25]  J Joshua Yang,et al.  Memristive devices for computing. , 2013, Nature nanotechnology.

[26]  Heeyoung Jeon,et al.  Detection of oxygen ion drift in Pt/Al2O3/TiO2/Pt RRAM using interface-free single-layer graphene electrodes , 2014 .

[27]  S. Menzel,et al.  Realization of Boolean Logic Functionality Using Redox‐Based Memristive Devices , 2015 .

[28]  Jinlan Wang,et al.  Electronic and Optical Properties of Graphene Quantum Dots: The Role of Many-Body Effects , 2015 .

[29]  M. Gao,et al.  Structural Phase Transition Effect on Resistive Switching Behavior of MoS2 -Polyvinylpyrrolidone Nanocomposites Films for Flexible Memory Devices. , 2016, Small.

[30]  A. Pradhan,et al.  Resistive switching behavior of reduced graphene oxide memory cells for low power nonvolatile device application , 2016, Scientific Reports.

[31]  Ali Khiat,et al.  Real-time encoding and compression of neuronal spikes by metal-oxide memristors , 2016, Nature Communications.

[32]  Jianhui Zhao,et al.  Highly improved performance in Zr0.5Hf0.5O2 films inserted with graphene oxide quantum dots layer for resistive switching non-volatile memory , 2017 .

[33]  P. Luciano,et al.  A Hardware Implementation of a Brain Inspired Filter for Image Processing , 2017, IEEE Transactions on Nuclear Science.

[34]  Byung-Gook Park,et al.  Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system , 2017, Nanotechnology.

[35]  Shinhyun Choi,et al.  SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations , 2018, Nature Materials.

[36]  Peng Lin,et al.  Fully memristive neural networks for pattern classification with unsupervised learning , 2018 .

[37]  Nagarajan Raghavan,et al.  Recommended Methods to Study Resistive Switching Devices , 2018, Advanced Electronic Materials.

[38]  P. Zhou,et al.  Self‐Assembled Networked PbS Distribution Quantum Dots for Resistive Switching and Artificial Synapse Performance Boost of Memristors , 2018, Advanced materials.

[39]  Jianhui Zhao,et al.  Robust Ag/ZrO2/WS2/Pt Memristor for Neuromorphic Computing. , 2019, ACS applied materials & interfaces.

[40]  Jianhui Zhao,et al.  Vacancy-Induced Synaptic Behavior in 2D WS2 Nanosheet-Based Memristor for Low-Power Neuromorphic Computing. , 2019, Small.

[41]  Thirumalai Venkatesan,et al.  Ferroic tunnel junctions and their application in neuromorphic networks , 2020 .

[42]  Han Zhang,et al.  Current status and prospects of memristors based on novel 2D materials , 2020 .