This paper presents a physics-based mathematical model for anionic memristor devices. The model utilizes Poisson-Boltzmann equation to account for temperature effect on device potential at equilibrium and comprehends material effect on device behaviors. A detailed MATLAB based algorithm is developed to clarify and simplify the simulation environment. Moreover, the provided model is used to simulate and predict the effect of oxide thickness, material type, and operating temperatures on the electrical characteristics of the device. The parameters of the proposed model are tuned and validated using experimental data of a fabricated wire based NbO_1-x junction. This device is attractive for neuromorphic and computing applications, where multilevel state is desirable. The value of this contribution is to provide a framework intended to simulate anionic memristor devices using correlated mathematical models. In addition, the model can be used to explore device materials and predict its performance.

A rewritable micro-Thick low power memristor device is presented. The memristor is fabricated based on a novel structure that is composed of Al/TiO2/Cu. The device switches at 3X lower voltage compared to the existing bipolar memristor state of the art at the microscale. The fabricated device exhibits unique I-V characteristic among similar devices when operated without prior electroforming. Thus, a new secure communication technique is proposed based on the existence of third trust party (TTP). As each host has unique memristor device, messages can be encrypted and decrypted securely using memristor based keys. In addition, Scyther analysis are provided to prove the security of the proposed technique. This work takes advantage of the inherited variation of memristor device electric characteristics to strengthen security algorithm in cryptographic application.