This paper describes the synthesis of micro-Thick hafnium-oxide (60 μm) memristors using a low-cost sol-gel drop-coating technique, and emphasizes on key parameters to be considered in the development of the microscale technology. The impact of electrode material on the device I-V electrical behavior is screened with the use of different metals, with different work-functions and oxygen affinities such as aluminum (Al), copper (Cu), titanium (Ti) and palladium (Pd) under a symmetric metal-insulator-metal arrangement. The type of metal contact was found to play a vital role in the switching behavior of the devices, highlighting a potential chemical interplay between the drop-coating solution and the electrode surface during the fabrication process. The effect of the sol-gel drying temperature on the I-V curve characteristics is also investigated with similar devices made at 60 °C and 80 °C. For example, the ROFF/RON ratio of Al/Al-Type memristors was found to increase by approximately nine folds when the drying temperature was increased from 60 °C to 80 °C. As depicted from the changes observed in the maximum flowing current within an I-V curve, the drying temperature was generally found to mainly affect the internal resistance of the devices, allowing external control over the ROFF/RON ratio.

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.

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.

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.