Spatial Confinement Effects of Embedded Nanocrystals on Multibit and Synaptic Properties of Forming Free SiO<sub>2</sub>-Based Conductive Bridge Random Access Memory

ACS Appl. Electron. Mater.

Tsigkourakos

et al. 2021

Self Cite

The growth of flexible memory devices with intrinsic neuromorphic properties is attracting tremendous attention due to the wide spread of wearable electronics. The fabrication of robust artificial synaptic structures that will have the ability to remain operational after various strain loads and perform artificial synaptic weight modulation procedures is considered the cornerstone for the development of wearable neuromorphic computing systems. Along these lines, a low-temperature Ag/SiO 2 /TiN memristive device is demonstrated on a polyethylene naphthalate substrate decorated with Pt nanoparticles (NPs) with a 5 nm average diameter. The memory devices are completely forming-free, operate at a low voltage of 500 mV, and exhibit great stability after bending numerous times under a record high mechanical strain of 4.16% without any severe degradation of the pulse endurance and retention capabilities. Concrete pieces of evidence regarding the crack suppression effect induced by the layer of Pt NPs on the robust memory performance are also provided. Moreover, a thorough analysis of the neuromorphic properties of our prototypes is presented, including the implementation of short-term plasticity effects, such as paired-pulsed facilitation and paired-pulse depression, which are of great importance for the rehearsal procedures of neurons during the learning function as well as for the linear distribution of the conductance pattern during synaptic potentiation and depression tasks. Additionally, Hebbian spike-time-dependent-plasticity responses were also recorded.

Section: Experimental Characteristicsmentioning

confidence: 99%

Section: Experimental Characteristicsmentioning

confidence: 99%

Section: Experimental Characteristicsmentioning

confidence: 99%

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Highly Flexible Artificial Synapses from SiO₂-Based Conductive Bridge Memristors and Pt Nanoparticles through a Crack Suppression Technique

Papakonstantinopoulos

ACS Appl. Electron. Mater.

Tsigkourakos

et al. 2021

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“…The SiO 2 thin film was found also in the amorphous state [ 15 ], which is also associated with the degree of porosity of a metal oxide thin film [ 16 ]. The sample operates under the bipolar switching mode and exhibits a quite big switching ratio (~10 6 ), which is also beneficial for multibit storing capabilities [ 17 ]. The transition from the high-resistance state (HRS) to the low-resistance state (LRS) takes place at about V SET ~ 220 mV while the opposite transition manifests at V RESET ~ −50 mV.…”

Section: Resultsmentioning

confidence: 99%

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

Papakonstantinopoulos

Kitsios

et al. 2021

Micromachines

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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.

“…Due to this effect there is an enhanced spatial confinement compared to the reference sample, where Ag nanofilaments are not confined within the 2D material lattice. 28 Both Ag/SiO 2 /SiO 2 /TiN and Ag/SiO 2 /MoS 2 /SiO 2 / TiN stacks exhibit distinct levels of conductance. Regarding the reference sample three quantum conductance states were extracted (Figure 5a), whereas for the MoS 2 -embedded sample seven different states have been recorded at a 20 mV step size (Figure 5b).…”

Section: Experimental Characteristicsmentioning

confidence: 98%

Demonstration of Enhanced Switching Variability and Conductance Quantization Properties in a SiO₂ Conducting Bridge Resistive Memory with Embedded Two-Dimensional MoS₂ Material

Kitsios

ACS Appl. Electron. Mater.

Spithouris

et al. 2022

Self Cite

In this work, we explore the resistive switching behavior of a thin layer of SiO 2 with embedded two-dimensional (2D) molybdenum disulfide, MoS 2 , in a conductive bridge random access memory (CBRAM) configuration. The proposed device exhibits enhanced conductance quantization behavior, reduced variability due to the suppression of the stochastic filament formation process, and synaptic properties. The device operates under the bipolar switching mode without the application of any electroforming procedure; eight different quantized conductance states were captured during direct current (DC) operation and 10 quantized states were recorded under pulse measurements. On top of that, both improved endurance and retention properties as well as linearity of the synaptic potentiation and depression procedures were attained; the underlying origins of these effects are attributed to the control of the Ag ion diffusion barrier through the existence of the atomic sieve of MoS 2 . Our work paves the way for the development of robust memristive elements for the implementation of stable resistive switching and neuromorphic functionalities.