Emulating Artificial Synaptic Plasticity Characteristics from SiO2-Based Conductive Bridge Memories with Pt Nanoparticles.

Panagiotis Bousoulas,Konstantinos Moustakas,Charalampos Papakonstantinopoulos,Stavros Kitsios,Dimitris Tsoukalas,Georgios Ch Sirakoulis

doi:10.3390/mi12030306

Panagiotis Bousoulas, Konstantinos Moustakas + Show 4 more

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https://doi.org/10.3390/mi12030306

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Abstract

The quick growth of information technology has necessitated the need for developing novel electronic devices capable of performing novel neuromorphic computations with low power consumption and a high degree of accuracy. In order to achieve this goal, it is of vital importance to devise artificial neural networks with inherent capabilities of emulating various synaptic properties that play a key role in the learning procedures. Along these lines, we report here the direct impact of a dense layer of Pt nanoparticles that plays the role of the bottom electrode, on the manifestation of the bipolar switching effect within SiO2-based conductive bridge memories. Valuable insights regarding the influence of the thermal conductivity value of the bottom electrode on the conducting filament growth mechanism are provided through the application of a numerical model. The implementation of an intermediate switching transition slope during the SET transition permits the emulation of various artificial synaptic functionalities, such as short-term plasticity, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights toward the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior.

Highlights

The recent advances toward developing artificial neural networks and the perspective of carrying out in-memory calculations have increased the interest for emergent electronic synaptic elements that will be capable of performing multiple cognitive processes with low power consumption and high accuracy [1]
Even though several memory scenarios have been proposed toward that direction, a substantial amount of attention has been assembled around memristive devices, due to their minuscule dimensions, relatively small power supply requirements and most important their excellent compatibility with the conventional complementary metal-oxide semiconductor (CMOS) procedures [2,3]
We demonstrated that a broad range of artificial synaptic property characteristics can be emulated by SiO2 -based memristors, with Pt NPs as bottom electrode (BE), after the application of low-power pulses

Summary

Introduction

The recent advances toward developing artificial neural networks and the perspective of carrying out in-memory calculations have increased the interest for emergent electronic synaptic elements that will be capable of performing multiple cognitive processes with low power consumption and high accuracy [1]. The impressive characteristic of all the reported properties is that they cover a time constant between a few microseconds to several years. Under this light, we can argue that the synaptic properties are generally divided into two major categories: the short-term plasticity (STP) effects and the respective

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