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

Micromachines

Dimitris Tsoukalas¹  Stavros Kitsios¹  Charalampos Papakonstantinopoulos¹  Konstantinos Moustakas¹  Panagiotis Bousoulas¹  Georgios Ch. Sirakoulis² 

[1] Department of Applied Physics, National Technical University of Athens, Iroon Polytechniou 9 Zografou, 15780 Athens, Greece;Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece;

关键词: conducting filament;    diffusivity;    nanoparticles;    synapses;    plasticity;    conductance;   

DOI  :  10.3390/mi12030306

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown