We use a multi-scale modeling to study the time-dependent dielectric breakdown of an amorphous (a-) HfO${}_2$ insulator in a metal–oxide–metal capacitor. We focus on the role played by electron injection in the creation of oxygen vacancies, which eventually form the percolation path responsible for dielectric breakdown. In this scenario, the electron transport through the dielectric occurs by multi-phonon trap assisted tunnelling (MPTAT) between O vacancies. Energy parameters characterizing the creation of oxygen vacancies and the MPTAT process are calculated using density functional theory employing a hybrid density functional. The results demonstrate that the formation of neutral O vacancies facilitated by electron injection into the oxide represents a crucial step in the degradation process dominating the kinetics at common breakdown fields. We further show the importance of the so-called “energetic correlation” effect, where pre-existing O vacancies locally increase the generation rate of additional vacancies accelerating the oxide degradation process. This model gives realistic breakdown times and Weibull slopes and provides a detailed insight into the mechanism of dielectric breakdown and atomistic and electronic structures of percolation paths in a-HfO${}_2$ . It offers a new understanding of degradation mechanisms in oxides used in the current MOSFET technology and can be useful for developing future resistive switching and neuromorphic nanodevices.

In this study, we present a copper nanowires (CuNWs) based spray deposition process for the fabrication of transparent heaters and thermo-acoustic loudspeakers. We developed a scalable and solution-based synthesis process for CuNWs, which allows to fabricate spray deposited transparent electrodes (TEs) that show performances comparable to indium tin oxide (ITO) based TEs, at much lower material and deposition costs. Without any post-processing, the CuNWs films exhibit a sheet resistance as low as 12.6 1/2 at a high transparency of 77 %. CuNW-based transparent heaters and thermo-acoustic loudspeakers are accurately characterized and modelled in both the thermal and the acoustic domain, showing performances aligned with the state-of-the art.

Memristor-based neuromorphic systems have been proposed as a promising alternative to von Neumann computing architectures, which are currently challenged by the ever-increasing computational power required by modern artificial intelligence (AI) algorithms. The design and optimization of memristive devices for specific AI applications is thus of paramount importance, but still extremely complex, as many different physical mechanisms and their interactions have to be accounted for, which are, in many cases, not fully understood. The high complexity of the physical mechanisms involved and their partial comprehension are currently hampering the development of memristive devices and preventing their optimization. In this work, we tackle the application-oriented optimization of Resistive Random-Access Memory (RRAM) devices using a multiscale modeling platform. The considered platform includes all the involved physical mechanisms (i.e., charge transport and trapping, and ion generation, diffusion, and recombination) and accounts for the 3D electric and temperature field in the device. Thanks to its multiscale nature, the modeling platform allows RRAM devices to be simulated and the microscopic physical mechanisms involved to be investigated, the device performance to be connected to the material’s microscopic properties and geometries, the device electrical characteristics to be predicted, the effect of the forming conditions (i.e., temperature, compliance current, and voltage stress) on the device’s performance and variability to be evaluated, the analog resistance switching to be optimized, and the device’s reliability and failure causes to be investigated. The discussion of the presented simulation results provides useful insights for supporting the application-oriented optimization of RRAM technology according to specific AI applications, for the implementation of either non-volatile memories, deep neural networks, or spiking neural networks.