Thermophoresis/diffusion as a plausible mechanism for unipolar resistive switching in metal–oxide–metal memristors

Abstract

We show that the SET operation of a unipolar memristor could be explained by thermophoresis, or the Soret effect, which is the diffusion of atoms, ions or vacancies in a steep temperature gradient. This mechanism explains the observed resistance switching via conducting channel formation and dissolution reported for TiO 2 and other metal-oxide-based unipolar resistance switches. Depending on the temperature profile in a device, dilute vacancies can preferentially diffuse radially inward toward higher temperatures caused by the Joule heating of an electronic current to essentially condense and form a conducting channel. The RESET operation occurs via radial diffusion of vacancies away from the channel when the temperature is elevated but the gradient is small.

Probing the Critical Region of Conductive Filament in Nanoscale HfO2 Resistive-Switching Device by Random Telegraph Signals

“…During the reset, defect movements from the CFR towards TE are dominant, resulting in a wider CFR and higher resistance. Our results also confirms that defects movement along the filament direction plays a dominant role in controlling the CFR modification, opposing to the cases of unipolar switching in which the defects move into and out of filament in the perpendicular direction in previous works [30]. Combined with previous results of physical characterization with c-AFM [14] and modeling [20] for the same devices, the results suggest that the filament has an hour-glass shape at HRS with the CFR located closer to the more oxygen-inert BE and the length of the constriction is less than 1 nm.…”

A comprehensive approach to decipher biological computation to achieve next generation high-performance exascale computing.

“…1748, we developed a theoretical model that fits memristive switching data in tantalum oxide-based devices with the use of only three physical fitting parameters: the critical activation temperature , thermal conductivity of the device electrodes , and the conductive filament radius . Importantly, the physical fitting parameters were found to all have quite reasonable ranges over our set of test devices: 8.5 nm 13.2 nm is consistent with the range of previous reports [93], 1300 K -1700 K is in close agreement with finite element thermal modeling [94,95] [83,86] and/or require inaccessible experimental instrumentation [65,96]. Filament temperature and surrounding thermal resistance -outside of finite element methods such as COMSOL simulations [94,95] -are not well known experimentally.…”

“…To accurately describe the resistive switching dynamics shown in Figure 27c, we have derived a quasi-3D model describing the time-evolution of memristive switching by predicting the change of a single state-variable dependent on three components of ionic flux: Fick diffusion, drift, and the less well known phenomenon of Soret diffusion [88,89]. Soret diffusion, also referred to as thermophoresis, is the movement of molecules along a temperature gradient and is commonly observed in liquid/molecular solutions [90], however, its role in solid oxides has recently been emphasized [55,89,91]. Using these components of ionic flux, we find that the resistive switching can be reproduced by modulating the radius of a Ta-rich conducting cylindrical filament surrounded by a matrix of insulating forms of tantalum oxide.…”

“…where D is the diffusion coefficient [89], represents a spatial derivative, S is the Soret coefficient defined as the ratio of the thermodiffusion coefficient, D T , to the normal diffusion coefficient, D [89], T is the temperature, E is the electric field, q is the ionic charge, and k B T is the thermal energy. In this geometry, the Fick and Soret diffusion terms will be directed through the radial surface area of the filament: .…”

Phenomenological modeling of memristive devices

“…For example, some models focus on specific aspects of the memristive behavior, e.g., static equation only [4], or a particular aspect of switching dynamics [13,19,21,43], and hence are incomplete. Others are derived assuming very simple physical models [6,18,48] and therefore could not accurately predict experimental behavior-see also comprehensive reviews of such models in Refs.…”