We present a study of the 'snap-back' regime of resistive switching hysteresis in bipolar TaOx memristors, identifying power signatures in the electronic transport. Using a simple model based on the thermal and electric field acceleration of ionic mobilities, we provide evidence that the 'snap-back' transition represents a crossover from a coupled thermal and electric-field regime to a primarily thermal regime, and is dictated by the reconnection of a ruptured conducting filament. We discuss how these power signatures can be used to limit filament radius growth, which is important for operational properties such as power, speed, and retention.
In this paper, we present a fully-coupled electrical and thermal transport model for oxide memristors that solves simultaneously the time-dependent continuity equations for all relevant carriers, together with the time-dependent heat equation including Joule heating sources. The model captures all the important processes that drive memristive switching and is applicable to simulate switching behavior in a wide range of oxide memristors. The model is applied to simulate the ON switching in a 3D filamentary TaOx memristor. Simulation results show that, for uniform vacancy density in the OFF state, vacancies fill in the conduction filament till saturation, and then fill out a gap formed in the Ta electrode during ON switching; furthermore, ON-switching time strongly depends on applied voltage and the ON-to-OFF current ratio is sensitive to the filament vacancy density in the OFF state.
The locations of conductive regions in TaOx memristors are spatially mapped using a microbeam and Nanoimplanter by rastering an ion beam across each device while monitoring its resistance. Microbeam irradiation with 800 keV Si ions revealed multiple sensitive regions along the edges of the bottom electrode. The rest of the active device area was found to be insensitive to the ion beam. Nanoimplanter irradiation with 200 keV Si ions demonstrated the ability to more accurately map the size of a sensitive area with a beam spot size of 40 nm by 40 nm. Isolated single spot sensitive regions and a larger sensitive region that extends approximately 300 nm were observed.
This paper investigates the effects of high dose rate ionizing radiation and total ionizing dose (TID) on tantalum oxide (TaOx) memristors. Transient data were obtained during the pulsed exposures for dose rates ranging from approximately 5.0 ×107 rad(Si)/s to 4.7 ×108 rad(Si)/s and for pulse widths ranging from 50 ns to 50 μs. The cumulative dose in these tests did not appear to impact the observed dose rate response. Static dose rate upset tests were also performed at a dose rate of ~3.0 ×108 rad(Si)/s. This is the first dose rate study on any type of memristive memory technology. In addition to assessing the tolerance of TaOx memristors to high dose rate ionizing radiation, we also evaluated their susceptibility to TID. The data indicate that it is possible for the devices to switch from a high resistance off-state to a low resistance on-state in both dose rate and TID environments. The observed radiation-induced switching is dependent on the irradiation conditions and bias configuration. Furthermore, the dose rate or ionizing dose level at which a device switches resistance states varies from device to device; the enhanced susceptibility observed in some devices is still under investigation. As a result, numerical simulations are used to qualitatively capture the observed transient radiation response and provide insight into the physics of the induced current/voltages.
Resistive random access memory (ReRAM), or memristors, may be capable of significantly improve the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAMbased neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer some insight into the factors responsible for this variability.
Resistive random access memory (ReRAM), or memristors, may be capable of significantly improve the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAMbased neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer some insight into the factors responsible for this variability.