Electronic imitation of behavioral and psychological synaptic activities using TiOx/Al2O3-based memristor devices.

Seeking an effective electronic synapse to emulate biological synaptic behavior is fundamental for building brain-inspired computers. An emerging two-terminal memristor, in which the conductance can be gradually modulated by external electrical stimuli, is widely considered as the strongest competitor of the electronic synapse. Here, we show the capability of TiOx/Al2O3-based memristor devices to imitate synaptic behaviors. Along with analog resistive switching performances, the devices replicate the bio-synapse behaviors of potentiation/depression, short-term-plasticity, and long-term-potentiation, which show that TiOx/Al2O3-based memristors may be useful as electronic synapses. The essential memorizing capabilities of the brain are dependent on the connection strength between neurons, and the memory types change from short-term memory to long-term memory. In the TiOx/Al2O3-based electronic synaptic junction, the memorizing levels can change their state via a standard rehearsal process and also via newly introduced process called "impact of event" i.e. the impact of pulse amplitude, and the width of the input pulse. The devices show a short-term to long-term memory effect with the introduction of intermediate mezzanine memory. The experimental achievements using the TiOx/Al2O3 electronic synapses are finally psychologically modeled by considering the mezzanine level. It is highly recommended that similar phenomena should be investigated for other memristor systems to check the authenticity of this model.

[1]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

[2]  Shimeng Yu,et al.  An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation , 2011, IEEE Transactions on Electron Devices.

[3]  Giacomo Indiveri,et al.  Integration of nanoscale memristor synapses in neuromorphic computing architectures , 2013, Nanotechnology.

[4]  Dmitri B. Strukov,et al.  Donor‐Induced Performance Tuning of Amorphous SrTiO3 Memristive Nanodevices: Multistate Resistive Switching and Mechanical Tunability , 2015 .

[5]  T. Hasegawa,et al.  Learning Abilities Achieved by a Single Solid‐State Atomic Switch , 2010, Advanced materials.

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

[7]  M. Rozenberg,et al.  Mechanism for bipolar resistive switching in transition-metal oxides , 2010, 1001.0703.

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

[9]  Inah Lee,et al.  Differential contribution of NMDA receptors in hippocampal subregions to spatial working memory , 2002, Nature Neuroscience.

[10]  Wei Lu,et al.  Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics , 2015 .

[11]  L. Lauhon,et al.  Gate-tunable memristive phenomena mediated by grain boundaries in single-layer MoS2. , 2015, Nature nanotechnology.

[12]  Byoungil Lee,et al.  Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.

[13]  Qi Liu,et al.  Crystal that remembers: several ways to utilize nanocrystals in resistive switching memory , 2017 .

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

[15]  Byoung Hun Lee,et al.  Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device , 2013, Nanotechnology.

[16]  T. Bliss,et al.  A requirement for the immediate early gene Zif268 in the expression of late LTP and long-term memories , 2001, Nature Neuroscience.

[17]  B. Derrick,et al.  NMDA receptor antagonists sustain LTP and spatial memory: active processes mediate LTP decay , 2002, Nature Neuroscience.

[18]  Akrivi Katifori,et al.  Ontologies and the brain: Using spreading activation through ontologies to support personal interaction , 2010, Cognitive Systems Research.

[19]  Qi Liu,et al.  Occurrence of Resistive Switching and Threshold Switching in Atomic Layer Deposited Ultrathin (2 nm) Aluminium Oxide Crossbar Resistive Random Access Memory , 2015, IEEE Electron Device Letters.

[20]  John W. Backus,et al.  Can programming be liberated from the von Neumann style?: a functional style and its algebra of programs , 1978, CACM.

[21]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[22]  Don Monroe,et al.  A new type of mathematics? , 2014, CACM.

[23]  S. Royer,et al.  Conservation of total synaptic weight through balanced synaptic depression and potentiation , 2003, Nature.

[24]  Audrius V. Avizienis,et al.  Emergent Criticality in Complex Turing B‐Type Atomic Switch Networks , 2012, Advanced materials.

[25]  A. Bessonov,et al.  Layered memristive and memcapacitive switches for printable electronics. , 2015, Nature materials.

[26]  J. D. McGaugh,et al.  Inhibition of Activity-Dependent Arc Protein Expression in the Rat Hippocampus Impairs the Maintenance of Long-Term Potentiation and the Consolidation of Long-Term Memory , 2000, The Journal of Neuroscience.

[27]  Sung Min Kim,et al.  A stacked memory device on logic 3D technology for ultra-high-density data storage , 2011, Nanotechnology.

[28]  Qing Wan,et al.  Artificial synapse network on inorganic proton conductor for neuromorphic systems. , 2014, Nature communications.

[29]  P. Schulz,et al.  Long-term potentiation involves increases in the probability of neurotransmitter release. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

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

[31]  E. D’Angelo,et al.  Increased neurotransmitter release during long‐term potentiation at mossy fibre–granule cell synapses in rat cerebellum , 2004, The Journal of physiology.

[32]  Qi Liu,et al.  Evolution of conductive filament and its impact on reliability issues in oxide-electrolyte based resistive random access memory , 2015, Scientific Reports.

[33]  T. Hasegawa,et al.  Controlling the Synaptic Plasticity of a Cu2S Gap‐Type Atomic Switch , 2012 .

[34]  M. Lynch,et al.  Long-term potentiation and memory. , 2004, Physiological reviews.

[35]  X. Miao,et al.  Associative Learning with Temporal Contiguity in a Memristive Circuit for Large‐Scale Neuromorphic Networks , 2015 .

[36]  J. Yang,et al.  Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. , 2017, Nature materials.

[37]  Y. Liu,et al.  Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor , 2012 .

[38]  Bipin Rajendran,et al.  Novel synaptic memory device for neuromorphic computing , 2014, Scientific Reports.

[39]  Don Monroe,et al.  Neuromorphic computing gets ready for the (really) big time , 2014, CACM.

[40]  X. Miao,et al.  Ultrafast Synaptic Events in a Chalcogenide Memristor , 2013, Scientific Reports.

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

[42]  S. Ambrogio,et al.  Spike-timing dependent plasticity in a transistor-selected resistive switching memory , 2013, Nanotechnology.

[43]  L. Goux,et al.  On the Gradual Unipolar and Bipolar Resistive Switching of TiN\ HfO2\Pt Memory Systems , 2010 .

[44]  Carver A. Mead,et al.  Neuromorphic electronic systems , 1990, Proc. IEEE.