The resistive switching (RS) characteristics of flexible films deposited on mica substrates have rarely been reported upon, especially flexible HfO₂ films. A novel flexible Au/HfO₂/Pt/mica resistive random access memory device was prepared by a sol-gel process, and a Au/HfO₂/Pt/Ti/SiO₂/Si (100) device was also prepared for comparison. The HfO₂ thin films were grown into the monoclinic phase by the proper annealing process at 700 °C, demonstrated by grazing-incidence X-ray diffraction patterns. The ratio of high/low resistance (off/on) reached 1000 and 50 for the two devices, respectively, being relatively stable for the former but not for the latter. The great difference in ratios for the two devices may have been caused by different concentrations of the oxygen defect obtained by the X-ray photoelectron spectroscopy spectra indicating composition and chemical state of the HfO₂ thin films. The conduction mechanism was dominated by Ohm’s law in the low resistance state, while in high resistance state, Ohmic conduction, space charge limited conduction (SCLC), and trap-filled SCLC conducted together.

Artificial neural networks built with optoelectronic synaptic devices have been proven to process visual information effectively. However, it takes great latency time and energy consumption, especially facing dynamic visual information, due to the separated optical sensor, memory, and process unit. Reservoir computing (RC) based on optoelectronic synaptic devices provides an in-sensor RC for processing temporal information efficiently. It achieves efficient computation by sensing and processing optical signals directly with optoelectronic synaptic devices. Optoelectronic synaptic devices shine in visual information processing, whose application in visual sensing and processing will provide a viable hardware solution for in-sensor computing. Therefore, the application of optoelectronic synaptic devices in reservoir computing has prompted increasing attention. Herein, for promoting the application of physical reservoir computing (PRC) with optoelectrical synapses in machine vision, synaptic plasticity will be introduced first in this work and then illustrate the basic functions of optoelectronic synapses as well as their application in reservoir computing further, and provide a perspective on PRC with optoelectronic synapses in the final.

(Pb_0.97La_0.02)(Zr_0.95Ti_0.05)O₃ (PLZT2/95/5) ceramics were successfully prepared via a solid-state reaction route. The dielectric properties were investigated in the temperature region of 26–650 °C. The dielectric diffuse anomaly in the dielectric relaxation was found in the high temperature region of 600–650 °C with increasing the measuring frequency, which was related to the dynamic thermal process of ionized oxygen vacancies generated in the high temperature. Two phase transition points were detected during heating, which were found to coexist from 150 to 200 °C. Electric field induced ferroelectric to antiferroelectric phase transition behavior of the (Pb_0.97La_0.02)(Zr_0.95Ti_0.05)O₃ ceramics was investigated in this work with an emphasis on energy storage properties. A recoverable energy-storage density of 0.83 J/cm³ and efficiency of 70% was obtained in (Pb_0.97La_0.02)(Zr_0.95Ti_0.05)O₃ ceramics at 55 kV/cm. Based on these results, (Pb_0.97La_0.02)(Zr_0.95Ti_0.05)O₃ ceramics with a large recoverable energy-storage density could be a potential candidate for the applications in high energy-storage density ceramic capacitors.

A negative electrocaloric effect (ECE) in B-site non-stoichiometric (Pb_0.97La_0.02)(Zr_0.95Ti_0.05)O₃ antiferroelectric ceramics synthesized by the conventional solid-state reaction method is investigated via indirect and direct measurements. Temperature-dependent P-E hysteresis loops were measured. A giant negative electrocaloric temperature change of about 12.45 K was achieved. The results indicate that the antiferroelectric ceramics with non-stoichiometry modification could be a promising candidate for the applications in cooling devices. IMPACT STATEMENT Giant negative electrocaloric effect (NECE) was achieved in B-site non-stoichiometric PLZT2/95/5 ceramics, the NECE parameters are comparable or even larger than most of the reported bulk materials, making it attractive for cooling devices application. GRAPHICAL ABSTRACT

Pulsed power systems require high-performance capacitors with high energy storage density. In this work, (1 − x)BaTiO3-xBi(Mg1/2Sn1/2)O3 ferroelectric ceramics were synthesized in a solid-state solution. The sample of x = 0.12 (0.88BT-0.12BMS) has excellent energy storage density, wide temperature, and wide frequency stability. The excellent energy density of 4.87 J/cm3 at 315 kV/cm and the energy efficiency of 72% at room temperature for 0.88BT-0.12BMS ceramics were achieved. Furthermore, the 0.88BT-0.12BMS ceramics demonstrated well temperature stabilities in the range of 20–100 °C and very good frequency stability in the range of 1–100 Hz. Through pulsed charging–discharging testing, the current density is calculated as 314.01 A/cm2, and the power density is 21.98 MW/cm3. Moreover, the oxygen vacancies' defects and ferroelectric domain mechanism for enhanced breakdown strength as well as high energy density were discussed. These findings broaden the horizon for lead-free dielectrics and show promising applications for pulse power capacitors.

Brain-like neuromorphic computing networks based on the human brain information processing model are gradually breaking down the memory barriers caused by traditional computing frameworks. The brain-like neural system consists of electronic synapses and neurons. The multiple ferroelectric polarization switching modulated by the external electric field is well suited to simulate artificial neural synaptic weights. Therefore, ferroelectric diodes' (FDs) synapses have great advantages in building highly reliable and energy-efficient artificial neural networks. In this paper, we demonstrate the FDs synapse, which is based on rare-earth metal-doped BaTiO₃ ferroelectric dielectric layer materials. This performs short-term and long-term synaptic plasticity behaviors by modulating synaptic weights using pulsed stimuli to polarize or flip ferroelectric films. In addition, convolutional neural networks were constructed on the MNIST dataset and the Fashion-MNIST dataset to check the feasibility of the device in simulating bio-visual recognition. The results expand the application of FDs' devices in the intersection of artificial intelligence and bioscience.