This chapter 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 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.

In this chapter, the geometric scaling effect is investigated on the unipolar switching behavior of nano-thick Pd(TE)/Hf (capping)/HfO₂/Pd(BE) metal-insulator-metal memristive devices. The electrical I–V characteristics of such device are studied as a function of the active area and thickness of each of the metal-oxide film and the capping layer. The device active area is shown to play a critical role in dictating the magnitude of the threshold turn-on voltage (V _ON). For (Hf-7 nm/HfO₂-10 nm) stack particularly, it is found that the average V _ON decreases from 4.7 to 2.8 V when the active area increases from 50 × 50 to 200 × 200 µm². Beyond this size, the threshold V _ON saturates and the device active area have a minimal effect on V _ON. Also, the switching ON voltage increases when the capping layer thickness increases as it adds to the total active film of the device. For the fabricated devices, decreasing the oxide thickness from 10-nm to 5-nm reduces the average turn-on voltage by 21-to-27% for 50 × 50-to-400 × 400 µm² active area range, demonstrating an improvement in the threshold power consumption of the memristor. These findings can be used to guide the design and fabrication of memristors for an improved RRAM and memristive-based applications.

Bio-inspired 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. This has implications in emerging neuromorphic semiconductor hardware.

Memristors are one of the emerging technologies that can potentially replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. Over the past few years, research and development mostly focused on revolutionizing the metal-oxide materials, which are used as core components of the popular metal-insulator-metal (MIM) memristors owing to their highly recognized resistive switching behavior. This chapter outlines the recent advancements and characteristics of such memristive devices, with a special focus on (i) their established resistive switching mechanisms and (ii) the key challenges associated with their fabrication processes including the impeding criteria of material adaptation for the electrode, capping, and insulator component layers. Potential applications and an outlook into the future development of metal-oxide memristive devices are also outlined.

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 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.

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.

This paper proposes novel secret key generation techniques using memristor devices. The approach depends on using the initial profile of a memristor as a master key. In addition, session keys are generated using the master key and other specified parameters. In contrast to existing memristor-based security approaches, the proposed development is cost effective and power efficient since the operation can be achieved with a single device rather than a crossbar structure. An algorithm is suggested and demonstrated using physics based Matlab model. It is shown that the generated keys can have dynamic size which provides perfect security. Moreover, the proposed encryption and decryption technique using the memristor based generated keys outperforms Triple Data Encryption Standard (3DES) and Advanced Encryption Standard (AES) in terms of processing time. This paper is enriched by providing characterization results of a fabricated microscale Al/TiO₂/Al memristor prototype in order to prove the concept of the proposed approach and study the impacts of process variations. The work proposed in this paper is a milestone towards System On Chip (SOC) memristor based security.

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.