Switching Dynamics of Ag-Based Filamentary Volatile Resistive Switching Devices—Part I: Experimental Characterization

Abstract

Volatile resistive switching random access memory (RRAM) devices are drawing attention in both storage and computing applications due to their high ON-/OFF-ratio, fast switching speed, low leakage, and scalability. However, these devices are relatively new and the physical switching mechanisms are still under investigation. A thorough understanding and modeling of the physical dynamics underlying filament formation and self-dissolution are of utmost importance in view of future integration of volatile devices in neuromorphic systems and in memory arrays. To assess the physical mechanisms and develop appropriate models, though, the electrical properties of the device have to be characterized. In this article, we present an extensive study of Ag/SiO x -based volatile RRAM devices. Important parameters, such as switching time, switching voltage, and retention time are investigated as a function of the stimulation conditions. A physical explanation is provided and the applicability of the device in neuromorphic systems is discussed.

Effect of the Threshold Kinetics on the Filament Relaxation Behavior of Ag‐Based Diffusive Memristors

“…In the experiments, the filament's strength was modified qualitatively by varying the programming current through a transistor. [50,51] On the other hand, the dependence of t set on the external bias has been shown in multiple publications and, yet, lack to make connections with the t r . [52][53][54] For a wide utilization of v-ECM devices in the emerging field of neuromorphic computation [55][56][57][58] understanding and control of the switching kinetics is of utmost importance.…”

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

“…The programming pulses are disclosed in Figure for the reference (red) and MoS 2 -embedded (blue) devices, respectively. The delay times t SET and t RESET are defined as the time between the initiation of the pulsed stimuli until the switching of the device and vice versa . More precisely, the results exhibited in Figure indicate that a delay time of t SET = 39.9 ns (Figure a) is necessary to observe a substantial increase of current and a delay of t RESET = 230 ns (Figure b) is required to minimize the value of the setting current values.…”

“…The delay times t SET and t RESET are defined as the time between the initiation of the pulsed stimuli until the switching of the device and vice versa. 33 More precisely, the results exhibited in Figure 6 indicate that a delay time of t SET = 39.9 ns (Figure 6a) is necessary to observe a substantial increase of current and a delay of t RESET = 230 ns (Figure 6b) is required to minimize the value of the setting current values. On the other hand, for the MoS 2 -embedded sample (Figure 6c) t SET = 53.6 ns and t RESET = 254.7 ns (Figure 6d), which are slightly higher compared to the reference sample.…”

Ferroelectric-based synapses and neurons for neuromorphic computing

“…The latter approach also results in a very high on/off ratio of 10 7 [39], which is uncommon for ferroelectric devices. The exploitation of the migration of silver ions is also an element of novelty for ferroelectric devices, whereas it was already reported in some filamentary oxide-based resistive switching devices [45,46] used, as an example, for a retina-inspired artificial vision system [47].…”