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

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.

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

“…In Figure , parts b and d, the retention for both stacks is displayed. No significant fluctuations over a time span of 10 4 s were observed . However, the comparison between the reference and the MoS 2 -embedded samples clearly underlines that the latter exhibits fewer fluctuations, indicating a smaller variability.…”

“…The transmitted information of biological neurons is realized via small voltage pulses, the action potentials. The overall process and storage of these signals are arranged by the synapses . They establish a robust connection between the presynaptic and postsynaptic neurons.…”

“…No significant fluctuations over a time span of 10 4 s were observed. 34 However, the comparison between the reference and the MoS 2 -embedded samples clearly underlines that the latter exhibits fewer fluctuations, indicating a smaller variability. Additional device-to-device measurements for MoS 2 -embedded samples can be found in Figure S10.…”

“…The overall process and storage of these signals are arranged by the synapses. 34 They establish a robust connection between the presynaptic and postsynaptic neurons. In terms of material hardware, a neuronal synapse can be represented by a memristive device via the connection of a BE and TE.…”

An Artificial Synapse Based on CsPbI3 Thin Film

“…Therefore, the device can switch between high and low resistance states. The formation and decomposition of conductive filaments make it possible for pulse modulation (voltage, spike-number, frequency) to induce the synaptic behavior of devices [23][24][25][26][27][28]. Figure 4b,c illustrate that the artificial synapse has learning and memory behavior similar to neural synapses.…”