Memristors, as next-generation memory devices, have garnered significant academic interest. Among them, TiO₂/TiO_2-x based memristors have particularly attracted substantial scholarly attention. Research on the activation and stability of TiO2 based memristor devices through process parameters is essential. Here, to determine the impact of process parameters on the activation of TiO₂/TiO_2-x based memristor devices, we fabricated the memristor devices using a sputtering system andconducted annealing at 400℃. Additionally, to analyze the electrical characteristics of the devices, we measured the I-V curves and C-V curves. Also, we examined TiO₂/TiO_2-x based memristor devices surface using SEM. Consequently, it was observed that the devices subjected to annealing exhibited improved hysteresis curves in the I-V characteristics, a reduced bandgap, and changes in resistance compared to the non-annealed devices. The retention test results further demonstrated that the set/reset characteristics of the devices were stable, confirming their potential applicability as memory devices.

The report reviews recent research efforts in demonstrating a computing system whose operation principle mimics the dynamics of biological neurons. The temporal variation of the membrane potential of neurons is one of the key features that contribute to the information processing in the brain. We first summarize the neuron models that explain the experimentally observed change in the membrane potential. The function of ion channels is briefly introduced to understand such change from the molecular viewpoint. Dedicated circuits that can simulate the neuronal dynamics have been developed to reproduce the charging and discharging dynamics of neurons depending on the input ionic current from presynaptic neurons. Key elements include volatile memristors that can undergo volatile resistance switching depending on the voltage bias. This behavior called the threshold switching has been utilized to reproduce the spikes observed in the biological neurons. Various types of threshold switch have been applied in a different configuration in the hardware demonstration of neurons. Recent studies revealed that the memristor-based circuits could provide energy and space efficient options for the demonstration of neurons using the innate physical properties of materials compared to the options demonstrated with the conventional complementary metal-oxidesemiconductors (CMOS).

This paper presents an electrical feature analysis of hysteresis curves in memristor differential and intergral control circuit. After making macro model of the memristor device, electric characteristics of the model such as time analysis, frequency dependent DC I-V curves were performed by PSPICE simulation. Also, we made a circuit of memristor-capacitor based on nano-wired memristor device and analyzed the simulated PSPICE results. Finally, we proposed a memristor based differential or integral control circuit, analyzed hysteresis curve characteristic in the control circuit.