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

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.

[1]  T. Tseng,et al.  ZnO2/ZnO bilayer switching film for making fully transparent analog memristor devices , 2019, APL Materials.

[2]  Sumio Hosaka,et al.  Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO2 Memristive Spiking-Neuron , 2018, Scientific Reports.

[3]  Chang Hwan Choi,et al.  Influence of Oxygen Vacancies in ALD HfO 2-x Thin Films on Non-Volatile Resistive Switching Phenomena with a Ti/HfO 2-x /Pt Structure , 2018 .

[4]  W. Lew,et al.  Oxide-based RRAM materials for neuromorphic computing , 2018, Journal of Materials Science.

[5]  Tuning Resistive, Capacitive, and Synaptic Properties of Forming Free TiO2‐x‐Based RRAM Devices by Embedded Pt and Ta Nanocrystals , 2018 .

[6]  Shimeng Yu,et al.  Neuro-Inspired Computing With Emerging Nonvolatile Memorys , 2018, Proceedings of the IEEE.

[7]  Neuro-Inspired Computing With Emerging Nonvolatile Memory , 2018 .

[8]  H.-S. Philip Wong,et al.  Device and circuit optimization of RRAM for neuromorphic computing , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[9]  I. Valov.  Interfacial interactions and their impact on redox-based resistive switching memories (ReRAMs) , 2017 .

[10]  Jae-Joon Kim,et al.  Input Voltage Mapping Optimized for Resistive Memory-Based Deep Neural Network Hardware , 2017, IEEE Electron Device Letters.

[11]  H. Hwang,et al.  Improved Conductance Linearity and Conductance Ratio of 1T2R Synapse Device for Neuromorphic Systems , 2017, IEEE Electron Device Letters.

[12]  Shimeng Yu,et al.  Improving Analog Switching in HfOx-Based Resistive Memory With a Thermal Enhanced Layer , 2017, IEEE Electron Device Letters.

[13]  P. Bousoulas,et al.  Low-Power Forming Free TiO2–x/HfO2–y/TiO2–x-Trilayer RRAM Devices Exhibiting Synaptic Property Characteristics , 2017, IEEE Transactions on Electron Devices.

[14]  Eby G. Friedman,et al.  Synaptic Characteristics of Ag/AgInSbTe/Ta-Based Memristor for Pattern Recognition Applications , 2017, IEEE Transactions on Electron Devices.

[15]  Di Wu,et al.  Synaptic Plasticity and Learning Behaviors Mimicked in Single Inorganic Synapses of Pt/HfOx/ZnOx/TiN Memristive System , 2017, Nanoscale Research Letters.

[16]  T. Tseng,et al.  Status and Prospects of ZnO-Based Resistive Switching Memory Devices , 2016, Nanoscale Research Letters.

[17]  I-Ting Wang,et al.  3D Ta/TaOx/TiO2/Ti synaptic array and linearity tuning of weight update for hardware neural network applications , 2016, Nanotechnology.

[18]  H. Hwang,et al.  Improved Synaptic Behavior Under Identical Pulses Using AlOx/HfO2 Bilayer RRAM Array for Neuromorphic Systems , 2016, IEEE Electron Device Letters.

[19]  E. Vianello,et al.  On the Origin of Low-Resistance State Retention Failure in HfO2-Based RRAM and Impact of Doping/Alloying , 2015, IEEE Transactions on Electron Devices.

[20]  I. Iatsunskyi,et al.  Structural and XPS characterization of ALD Al2O3 coated porous silicon , 2015 .

[21]  Yahong Xie,et al.  Controlled direct growth of Al2O3-doped HfO2 films on graphene by H2O-based atomic layer deposition. , 2015, Physical chemistry chemical physics : PCCP.

[22]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[23]  R. Degraeve,et al.  Tailoring switching and endurance / retention reliability characteristics of HfO2 / Hf RRAM with Ti, Al, Si dopants , 2014, 2014 Symposium on VLSI Technology (VLSI-Technology): Digest of Technical Papers.

[24]  A. Gloskovskii,et al.  Engineering of the chemical reactivity of the Ti/HfO₂ interface for RRAM: experiment and theory. , 2014, ACS applied materials & interfaces.

[25]  A. Zenkevich,et al.  Multilevel resistive switching in ternary HfxAl1-xOy oxide with graded Al depth profile , 2013 .

[26]  Shimeng Yu,et al.  A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation , 2013, Advanced materials.

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

[28]  Shimeng Yu,et al.  Metal–Oxide RRAM , 2012, Proceedings of the IEEE.

[29]  T. Bliss,et al.  Long-term potentiation and long-term depression: a clinical perspective , 2011, Clinics.

[30]  R. Dittmann,et al.  Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges , 2009, Advanced materials.

[31]  M. Perego,et al.  XPS and IPE analysis of HfO2 band alignment with high-mobility semiconductors , 2008 .

[32]  R. Zucker,et al.  Long-lasting potentiation and depression without presynaptic activity. , 1996, Journal of neurophysiology.

[33]  D. Amit.  Modelling Brain Function: The World of Attractor Neural Networks , 1989 .