# Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory
## 4 Discussion  
Memristors have an extremely high potential for revolutionizing both the memory and AI fields. In fact, memristors have been proposed for implementing high-density, high-speed, and low-power NVMs, and crossbar memory arrays supporting fast and efficient in-memory vector-matrix multiplication for DNN acceleration, and are finding applications in the development of SNNs, enabling the realization of artificial synapses exhibiting an STDP learning mechanism and biologically plausible artificial neurons (i.e., in accordance with the Hodgkin-Huxley model). Figure 10: Simulated distributions of oxygen ions (blue) and vacancies (red) in TiN/5 nm HfO\({}_{\text{y}}\)/TiO\({}_{\text{y}}\)/TIN RRAM stacks: (**a**) after forming; (**b**) after the reset operation with slow and anisotropic oxygen diffusion, showing an efficient reset process; (**c**) after the reset operation with excessively fast and anisotropic oxygen diffusion, showing an inefficient reset process with the formation of a dielectric barrier near the bottom electrode; (**d**) after the reset operation with slow and isotropic oxygen diffusion (with significant motion in the radial direction), showing an inefficient reset process due to the excessive motion of the oxygen ions away from the CF. As illustrated in Table 1, each of the memristor applications poses different constraints for the performance metrics, demanding devices with rather different characteristics. However, the design and optimization of memristors is still difficult and, most of the time, impractical, due to the limited knowledge on the resistance switching phenomena, their interplay, and the effects of the device materials and geometry on such phenomena. In this context, the described physical multiscale modeling and simulation provide an extremely powerful tool for the application-oriented optimization of RRAM-based memristors. As shown in Section 3, the presented multiscale modeling platform allows the device properties, such as its macroscopic electrical properties, its resistance switching dynamics, and its microscopic properties (i.e., the oxygen ions and vacancies distribution), to be simultaneously extracted, and how such properties are affected by the device materials, geometry, and forming conditions to be investigated. Despite a few differences between the simulations and the experimental results, mainly evidenced in Figures 4 and 6, the platform proved capable of capturing the trends and fundamental relationships between the OxRAM devices' microscopic properties and their behavior. The discrepancies shown by the simulations can be ascribed to several effects not considered by the platform, such as the oxide thickness and area variations due to the fabrication process tolerances, and process-dependent interface effects between the oxide and the electrodes. The platform can therefore be further improved by including the said effects. Moreover, it could be extended for simulating other memory devices, such as CBRAMs (i.e., including the chemical reaction phenomena at the device interfaces) and Phase Change Memories (PCMs) (i.e., accounting for the phase change in sub-regions of the device). Nevertheless, the information obtained through the presented multiscale simulation can be effectively used to determine the best combination of OxRAM materials, geometry, forming conditions, and pulse schemes for the desired application, dramatically reducing the time required for its marked deployment. The application of memristors to NVMs is less demanding performance-wise [26]. Noticeably, for binary NVM applications, the conductance update linearity and symmetry are not required, greatly relaxing both the device and pulse scheme design. Even in this simple case, device optimization must account for its microscopic properties in order to satisfy the state retention and endurance constraints.