In this paper, the resistive switching characteristics in a Ti/HfO₂: Al/Pt sandwiched structure are investigated for gradual conductance tuning inherent functions. The variation in conductance of the device under different amplitudes and voltage pulse widths is studied. At the same time, it was found that the variation in switching parameters in resistive random-access memory (RRAM) under impulse response is impacted by the initial conductance states. The device was brought to a preset resistance value range by energizing a single voltage amplitude pulse with a different number of periodicities. This is an efficient and simple programming algorithm to simulate the strength change observed in biological synapses. It exhibited an on/off of about 100, an endurance of over 500 cycles, and a lifetime (at 85 °C) of around 10⁵ s. This multi-level switching two-terminal device can be used for neuromorphic applications to simulate the gradual potentiation (increasing conductance) and inhibition (decreasing conductance) in an artificial synapse.

The resistive memory has become one of the most promising new memory types because of its excellent performance, and HfO₂ resistive material has attracted extensive attention. The conduction mechanism based on oxygen vacancy is widely recognized in the research of new nonvolatile memory. An RRAM electrothermal coupling model based on the oxygen vacancy conduction mechanism was constructed using COMSOL. The resistance process of the device is simulated by solving the coefficient partial differential equation, and the distribution of oxygen vacancy concentration, temperature, electric field, electric potential and other parameters in the dielectric layer at different voltages are obtained. The effects of temperature, dielectric layer thickness, top electrode thermal conductivity and conductive wire size on the resistance characteristics of the device are studied. It has guiding significance to further study the RRAM mechanism.

In this paper, a novel ferroelectric-based electrostatic doping (Fe-ED) nanosheet tunneling field-effect transistor (TFET) is proposed and analyzed using technology computer-aided design (TCAD) Sentaurus simulation software. By inserting a ferroelectric film into the polarity gate, the electrons and holes are induced in an intrinsic silicon film to create the p-source and the n-drain regions, respectively. Device performance is largely independent of the chemical doping profile, potentially freeing it from issues related to abrupt junctions, dopant variability, and solid solubility. An improved ON-state current and I_ON/I_OFF ratio have been demonstrated in a 3D-calibrated simulation, and the Fe-ED NSTFET’s on-state current has increased significantly. According to our study, Fe-ED can be used in versatile reconfigurable nanoscale transistors as well as highly integrated circuits as an effective doping strategy.

This paper mainly studies the hardware implementation of a fully connected neural network based on the 1T1R (one-transistor-one-resistor) array and its application in handwritten digital image recognition. The 1T1R arrays are prepared by connecting the memristor and nMOSFET in series, and a single-layer and a double-layer fully connected neural network are established. The recognition accuracy of 8 × 8 handwritten digital images reaches 95.19%. By randomly replacing the devices with failed devices, it is found that the stuck-off devices have little effect on the accuracy of the network, but the stuck-on devices will cause a sharp reduction of accuracy. By using the measured conductivity adjustment range and precision data of the memristor, the relationship between the recognition accuracy of the network and the number of hidden neurons is simulated. The simulation results match the experimental results. Compared with the neural network based on the precision of 32-bit floating point, the difference is lower than 1%.