Multimode Synaptic Operation of a HfAlO_x-Based Memristor as a Metaplastic Device for Neuromorphic Applications

Abstract

Metaplasticity is one of the synaptic functions in the brain, known as the plasticity of synapse plasticity. It allows some specific functions of the brain, such as continual learning, rapid homeostasis, and synaptic tag-and-capture functions. Here, we propose an electrically triggerable HfAlO x -based metaplastic device that exhibits reversible multimode synaptic behaviors. The origin of the synaptic characteristic change is the change in the serial and parallel resistance components of the memristive system, accompanied by unbalanced oxygen flux during the set and reset switching, triggered by the priming activity. Four distinguishable potentiation and depression characteristics (two stable modes and two transition modes) are suggested, which can be explained using a plausible schematic model. A hardware-based adaptive learning system for artificial neural networks is proposed as a potential application of the metaplastic device.

HfO2-based resistive switching memory devices for neuromorphic computing

“…In turn, in case of the emulation of the dynamics of individual network units, like single neuron or synapse with its own dynamics, the superimposition principle does not hold any more and use of volatile RRAMs for each network units provides a scaling advantage compared to the use of capacitors. In particular, volatile devices can be used to implement biological properties of individual synapses, like paired pulse facilitation and depression [57,62,145], metaplasticity [146] or short-to-long term memory [145] transition in case the same RRAM device shows both volatile to nonvolatile retention [92,93,147]. These functions, which cannot be efficiently implemented in conventional CMOS technology, have not been demonstrated neither with large statistics nor at array level, yet.…”

Memristive Explainable Artificial Intelligence Hardware

“…[15,16] The MVM function is known to be achievable using memristor crossbar arrays with multilevel resistance state programmable characteristics. [17][18][19][20][21] In addition, the signal integration function can be implemented using the conductance integration characteristics. [22][23][24] While Figure 1.…”

A review of memristor: material and structure design, device performance, applications and prospects

“… Memristor: RS materials, structure design, performances, and applications. Reproduced with permission from [ 15 , 16 ] copyright 2021, Wiley, [ 17 ] copyright 2021, American Chemical Society, [ 18 ] copyright 2021, Springer Nature, [ 19 ] copyright 2021, American Chemical Society, [ 20 ] copyright 2018, Royal Society of Chemistry, [ 21 ] copyright 2022, Elsevier, [ 22 ] copyright 2020, Springer Nature, [ 23 ] copyright 2020, American Chemical Society, [ 24 ] copyright 2019, Wiley, [ 25 ] copyright 2022, American Chemical Society, [ 26 ] copyright 2022, Wiley. …”