We report the complementary resistive switching (CRS) behaviors in aluminum nitride (AlN)-based memory devices as the promising new material system for large-scale integration of passive crossbar arrays. By utilizing different electrodes (Cu, Pt, and TiN), CRS characteristics are demonstrated in both TiN/AlN/Cu/AlN/TiN electrochemical metallization cells and Pt/AlN/TiN/AlN/Pt ionic resistive switching systems. The instability of Pt/AlN/Cu/AlN/Pt based CRS is explained by the relatively small reset voltage caused by the thermal effects enhanced reset process in the corresponding bipolar resistive switching element. It is concluded that the prerequisite for reliable and stable CRS is that the reset voltage of the bipolar resistive switching element must be much larger than half of the set voltage.
Oxides-based resistive switching memory induced by oxygen ions migration is attractive for future nonvolatile memories. Numerous works had focused their attentions on the sandwiched oxide materials for depressing the characteristic variations, but the comprehensive studies of the dependence of electrodes on the migration behavior of oxygen ions are overshadowed. Here, we investigated the interaction of various metals (Ni, Co, Al, Ti, Zr, and Hf) with oxygen atoms at the metal/Ta2O5 interface under electric stress and explored the effect of top electrode on the characteristic variations of Ta2O5-based memory device. It is demonstrated that chemically inert electrodes (Ni and Co) lead to the scattering switching characteristics and destructive gas bubbles, while the highly chemically active metals (Hf and Zr) formed a thick and dense interfacial intermediate oxide layer at the metal/Ta2O5 interface, which also degraded the resistive switching behavior. The relatively chemically active metals (Al and Ti) can absorb oxygen ions from the Ta2O5 film and avoid forming the problematic interfacial layer, which is benefit to the formation of oxygen vacancies composed conduction filaments in Ta2O5 film thus exhibit the minimum variations of switching characteristics. The clarification of oxygen ions migration behavior at the interface can lead further optimization of resistive switching performance in Ta2O5-based memory device and guide the rule of electrode selection for other oxide-based resistive switching memories.
Traditional strategies for designing new materials with targeted property including methods such as trial and error, and experiences of domain experts, are time and cost consuming. In the present study, we propose a machine learning design system involving three features of machine learning modeling, compositional design and property prediction, which can accelerate the discovery of new materials. We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600-950 MPa and an electrical conductivity of 50.0% international annealed copper standard. There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions. Our results provide a new recipe to realize the property-oriented compositional design for highperformance complex alloys via machine learning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.