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.

This paper presents a physics-based model for memristors with different active layer materials. The model predicts the effect of changing the active material on the electrical characteristics of the devices. It captures the essential characteristics of the memristor such as coupling between ion mobility and electron current in addition to the nonlinear effects of electric fields. The parameters in the model depend on material (metal-oxide) properties that have impact on the device behavior. These properties are activation energy, escape attempt frequency, hopping parameter and relative permittivity. In this work, the effect of each parameter is highlighted and explained. In addition, the physics-based Matlab model is used to analyze the electrical characteristics of simulated memristor device using the following oxide materials; ZnO, TiO2 and Ta2O5. The simulation results of the model are validated with experimental data reported in the literature. The value of this contribution is to enable the selection of suitable oxide materials for the target memristor using correlated mathematical models.

Bioinspired semiconductor-based devices with adaptive and dynamic properties will have many advantages over conventional static digital silicon-based technologies. The ability to compute, process, and retain information in parallel, without referencing other circuit elements, offers enhanced speed, storage density, energy efficiency, and functionality benefits. A novel crossbar microwire-based device consisting of Nb/NbO/Pt structure that exhibits neural synapse-like adaptive conductivity (i.e., synaptic plasticity) is presented. The neuromorphic memristive junction, formed at the interface between the Pt metal wire and the thermally annealed core-shell Nb-NbO wire, demonstrates 1000 times conductivity change with an effective continuum of resistance levels. The device can also be fully activated to display standard resistance switching between two states. In the subthreshold regime, the voltage flux applied through the ∼400 nm thick NbO junction is shown to have a linear relationship to the charge produced within the device. The conductance value G is a function of the total flux history applied. The linear flux-charge relationship is exploited to demonstrate the voltage-pulse invariance. This suggests that only the integrated flux produced during a voltage-pulse application determines the charge generated within the junction, regardless of the operational parameters like voltage amplitude and time interval. Variation in current onset voltage as a function of flux is discussed with reference to carrier extraction at the intrinsically doped metal-semiconductor interface. The observed flux invariance has implications in emerging neuromorphic semiconductor hardware. Enabling pulse stimuli to be designed to have equal flux through the device from nonvoltage sources such as light may increase functionality in bioinspired computing and applications.

A simulation-based analysis is conducted to study the set and reset times of TiO₂-based memristor device. This analysis uses nonlinear device model that captures the effects of large electric field inside memristor devices. Previous studies report strong asymmetry between reset and set times with reset time being several orders of magnitude higher than set time. The aim of this work was to investigate the effect of the device length and oxygen vacancies profile on the switching time. Our results show that a device model with a length of 10 nm and accurate parameters can result in more realistic device characteristics. Also, oxygen vacancies profile can be tuned to improve the reset time. MATLAB is used to simulate a 10-nm device, and the initial vacancy profile is chosen to look like inverted parabola. The results show reduced ratio between reset and set times from several orders of magnitude presented in the literature to not more than 1.5×. Thus, the proposed oxygen vacancies profile and quantitative results derived from it can be used to suggest a physical memristor device. In addition, the tuned parameters and model that matches actual device behavior can be factored in memristor fabrication.

Memristor has a potential to play a big role in the electronics industry as it provides small size, low cost and low power. However, the asymmetry between the ON and OFF switching times of the device hinders the adaption of the device in modern electronics systems. The contribution of this paper is to explore the relationship between the length of the memristor and the switching times. To achieve this the nonlinear model of oxygen vacancies is used. The model also includes coupling with electron transfer. The study shows that tuning the device length can affect the switching time significantly. This paper shows that having a device length of 10-nm gives switching ON and OFF times in the range of 4s - 13ns for applied voltage of 1V - 2.3V. In additon, the obtained OFF/ON switching time ratio is 3x compared to several order of magnitudes reported inliterature for device length of 50-nm. The proposed model is also used to study the effect of changing temperature, activation energy and frequency on memristor switching time.