Two-dimensional materials have been widely investigated to implement memristive devices for data storage or neuromorphic computing applications because of their ultra-scaled thicknesses and clean interfaces. For example, resistance switching in hexagonal boron nitride (h-BN) has been demonstrated. This mechanism is most of the time attributed to the movement of metallic ions. It has however also been reported when h-BN is contacted with two inert electrodes such as graphene or Pt. We suggest here that the switching mechanism of the latter devices, which has not yet been clearly established, relies on locals change of the electronic structure of h-BN as caused by atomic defects, e.g., multi-vacancies. This class of intrinsic h-BN defects can create electrically controllable interlayer bridges. We use a combination of hybrid density functional theory and the Non-equilibrium Green’s function formalism to show that a single interlayer bridge resulting from the presence of a trivacancy in a graphene/h-BN/graphene stack leads to a switching voltage of ~5 V and a high-to-low resistance ratio >100. Both values lie within the reported experimental range and thus confirm the likelihood that intrinsic defects play a key role in the resistance switching of h-BN in contact with inert electrodes.

We review here how molecular dynamics and quantum transport can be combined to shed light on the performance of, for example, conductive bridging random access memories, and we show that electro-thermal effects play a critical role.

The encapsulation of single-layer 2D materials within hBN has been shown to improve the mobility of these compounds. Nevertheless, the interplay between the semiconductor channel and the surrounding dielectrics is not yet fully understood, especially their electron–phonon interactions. Therefore, here, we present an ab initio study of the coupled electrons and phonon transport properties of MoS${}_2$-hBN devices. The characteristics of two transistor configurations are compared to each other: one where hBN is treated as a perfectly insulating, non-vibrating layer and one where it is included in the ab initio domain as MoS${}_2$. In both cases, a reduction of the ON-state current by about 50% is observed as compared to the quasi-ballistic limit. Despite the similarity in the current magnitude, explicitly accounting for hBN leads to additional electron–phonon interactions at frequencies corresponding to the breathing mode of the MoS${}_2$-hBN system. Moreover, the presence of an hBN layer around the 2D semiconductor affects the Joule-induced temperature distribution within the transistor.

We propose a robust strategy for transforming the Hamiltonian of a metallic structure expressed in a nonorthogonal density-functional-theory (DFT) basis into a low-dimensional space compatible with electron-transport simulations. This mode-space approximation is applied locally to inhomogeneous material stacks including amorphous phases and interfaces. The contacts and periodic parts of the device are transformed into the subspace created, while the active inhomogeneous regions remain represented in real space. The various regions are connected to each other through mode-space–real-space hybrid blocks of the Hamiltonian and overlap matrices. This approach allows harnessing of the full flexibility of DFT combined with the power of the nonequilibrium Green’s function formalism at a moderate cost. In particular, for a realistic resistive random-access memory cell composed of 3390 atoms, a performance improvement by a factor of 136 compared with a pure real-space treatment is demonstrated, with an error of less than 2%.

We present a multiscale simulation framework to compute the current versus voltage (I-V) characteristics of metal-oxide-metal structures building the core of conductive bridging random-access memory (CBRAM) cells and to shed light on their resistance switching properties. The approach relies on a finite-element model whose input material parameters are extracted either from ab initio or from machine-learned empirical calculations. The applied techniques range from molecular dynamics and nudged elastic band to electronic and thermal quantum transport. Such an approach drastically reduces the number of fitting parameters needed and makes the resulting modeling environment more accurate than traditional ones. The developed computational framework is then applied to the investigation of an $\mathrm{Ag}/a$-${\mathrm{Si}\mathrm{O}}_2/\mathrm{Pt}$ CBRAM, reproducing experimental data very well. Moreover, the relevance of Joule heating is assessed by considering various cell geometries. It is found that self-heating manifests itself in devices with thin conductive filaments with few-nanometer diameters and at current concentrations in the tens-microampere range. With the proposed methodology it is now possible to explore the potential of not-yet fabricated memory cells and to reliably optimize their design.