Non-volatile memory (NVM) will play a very important role in the next-generation digital technologies, including the Internet of things. The metal-oxide memristors, especially based on HfO2, have been favored by lots of researchers because of its simple structure, high integration, fast operation speed, low power consumption, and high compatibility with advanced (complementary metal oxide silicon) CMOS technologies. In this paper, a 20-level stable resistance states Al-doped HfO2-based memristor is presented. Its cycles endurance, data retention time, and resistance ratio are larger than 103, > 104 s, and > 10, respectively.

A self-compliance resistive random access memory (RRAM) achieved through thermal annealing of a Pt/HfO_x/Ti structure. The electrical characteristic measurements show that the forming voltage of the device annealing at 500 °C decreased, and the switching ratio and uniformity improved. Tests on the device’s cycling endurance and data retention characteristics found that the device had over 1000 erase/write endurance and over 10⁵ s of lifetime (85 °C). The switching mechanisms of the devices before and after annealing were also discussed.

Both nonvolatile and volatile characteristics are realized in the same Pt/HfO ₂ /Ti memristor by specific electrical operations. The resistance states of nonvolatile memory hardly change for over 10 ⁵ s, whereas the low resistance state of volatile memory is unstable, and degenerates gradually into the high resistance state in hundreds of seconds. The volatile device was forming-free and realized only in clockwise voltage sweeping. Compared to the nonvolatile device, the operating current of volatile device was lower and showed obviously rectification characteristics. Emulations of synaptic plasticity including short-term plasticity, long-term plasticity, and spike-time-dependent-plasticity were achieved. Finally, the resistive switching mechanism of the volatile device was analyzed and assumed to be the trapping and detrapping of electrons by traps near the interface of Pt/HfO ₂ .

The optical absorption coefficient of graphene will change after injecting carriers. Based on this principle, a high-speed double-layer graphene electro-optic modulator with a ring resonator structure was designed in this paper. From the numerical simulations, we designed a modulator. Its optical bandwidth is larger than 130 GHz, the switching energy is 0.358 fJ per bit, and the driven voltage is less than 1.2 V. At the same time, the footprint of the proposed modulator is less than 10 microns squared, which makes the process compatible with the Complementary Metal Oxide Semiconductors (CMOS) process. This will provide the possibility for the on-chip integration of the photoelectric device.

GaN-based high electron mobility transistors offer high carrier density combined with high electron mobility and often require operation at high frequencies, voltages, and temperatures. The device may be under high temperature and high voltage at the same time in actual operation. In this work, the impact of separate off-state stresses, separate high-temperature stresses, and off-state stresses at high temperatures on AlGaN/GaN high electron mobility transistors (HEMTs) grown on Si substrates was investigated. The output current and gate leakage of the device degenerated to different degrees under either isolated off-state or high-temperature stress. The threshold voltage of the device only exhibited obvious negative drift under the action of high-temperature and off-state stresses. The parameter at high temperature (or room temperature) before stress application was the reference. We found that there was no significant difference in the degradation rate of drain current and transconductance peak when the same off-state stress was applied to the device at different temperatures. It was concluded that, under the high-temperature off-state electric field pressure, there were two degradation mechanisms: one was the inverse piezoelectric polarization mechanism only related to the electric field, and the other was the degradation mechanism of the simultaneous action of temperature and electric field.

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%.