A two-SBT-memristor-based chaotic circuit was proposed. The stability of the equilibrium point was studied by theoretical analysis. The close dependence of the circuit dynamic characteristics on its initial conditions and circuit parameters was investigated by utilizing Lyapunov exponents spectra, bifurcation diagrams, phase diagrams, and Poincaré maps. The analysis showed that the circuit system had complex dynamic behaviors, such as stable points, period, chaos, limit cycles, and so on. In particular, the chaotic circuit produced the multistability phenomenon, such as coexisting attractors and coexisting periods.

Implementing memory using nonvolatile, low power, and nano-structure memristors has elicited widespread interest. In this paper, the SPICE model of Sr0.95Ba0.05TiO3 (SBT)-memristor was established and the corresponding characteristic was analyzed. Based on an SBT-memristor, the process of writing, reading, and rewriting of the binary and multi-value memory circuit was analyzed. Moreover, we verified the SBT-memristor-based 4 × 4 crossbar binary and multi-value memory circuits through comprehensive simulations, and analyzed the sneak-path current and memory density. Finally, we apply the 8 × 8 crossbar multi-value memory circuits to the images memory.

This paper presents a new physical [Formula: see text] (SBT) memristor-based chaotic circuit. The equilibrium point and the stability of the chaotic circuit are analyzed theoretically. This circuit system exhibits multiple dynamics such as stable point, periodic cycle and chaos by means of Lyapunov exponents spectra, bifurcation diagrams, Poincaré maps and phase portraits, when the initial state or the circuit parameter changes. Specially, the circuit system exhibits coexisting multi-dynamics. This study provides insightful guidance for the design and analysis of physical memristor-based circuits.

The synapse of human brain neurons is not only the transmission channel of information, but also the basic unit of human brain learning and information storing. The artificial synapse is constructed based on the Sr0.97Ba0.03TiO3 – x (SBT) memristor, which realizes the short-term and long-term plasticity of the synapse. The experiential learning and non-associative learning behavior in accordance with human cognitive rules are realized by using the SBT-memristor-based synapse. The process of synaptic habituation and sensitization is analyzed. This study provides insightful guidance for realization of artificial synapse and the development of artificial neural network.