High-performance ferroelectric non-volatile memory based on La-doped BiFeO3 thin films

Ultralow Energy Consumption and Fast Neuromorphic Computing Based on La0.1Bi0.9FeO3 Ferroelectric Tunnel Junctions

Low-power and fast artificial neural network devices represent the direction in developing analogue neural networks. Here, an ultralow power consumption (0.8 fJ) and rapid (100 ns) La0.1Bi0.9FeO3/La0.7Sr0.3MnO3 ferroelectric tunnel junction artificial synapse has been developed to emulate the biological neural networks. The visual memory and forgetting functionalities have been emulated based on long-term potentiation and depression with good linearity. Moreover, with a single device, logical operations of "AND" and "OR" are implemented, and an artificial neural network was constructed with a recognition accuracy of 96%. Especially for noisy data sets, the recognition speed is faster after preprocessing by the device in the present work. This sets the stage for highly reliable and repeatable unsupervised learning.

Giant tunnel resistance effect in (SrTiO3)2/(BaTiO3)4/(CaTiO3)2 asymmetric superlattice with enhanced polarization

In this work, we report the effectively enhanced tunneling electroresistance effect in Au/(SrTiO3)2/(BaTiO3)4/(CaTiO3)2/Nb:SrTiO3 superlattice ferroelectric tunnel junction (FTJ). The stable polarization switching and enhanced ferroelectricity were achieved in the nanoscale thickness high-quality epitaxial superlattice. A high ON/OFF current ratio of more than 105 was obtained at room temperature, which is an order of magnitude larger than the BaTiO3 FTJ with the same structure. Nonvolatile resistance switching controlled by nonvolatile polarization switching was observed in the superlattice FTJ. Driven by increased polarization and intrinsic asymmetric ferroelectricity, a highly asymmetric depolarization field is generated compared with the Au/BaTiO3/Nb:SrTiO3 FTJ, resulting in an enhanced tunneling electroresistance effect. These results provide a potential way to construct FTJ memory devices by constructing asymmetric three-component ferroelectric superlattices.

High-performance ferroelectric nonvolatile memory based on Gd-and Ni-codoped BiFeO3 films

BiFeO₃ (BFO), Bi_0.92Gd_0.08FeO₃ (BGFO) and Bi_0.92Gd_0.08Fe_0.95Ni_0.05O₃ (BGFNO) films are epitaxially grown on 0.7 wt% Nb-SrTiO₃ (NSTO) substrates. The strong ferroelectric property in BGFNO film is confirmed by piezoresponse force microscopy (PFM) and polarization versus voltage (P-V) measurement. It is also found that the Au/BGFNO/NSTO devices possess a ferroelectric resistance switching (RS) effect. Gd- and Ni-codoped BiFeO₃ is found to strongly enhance the resistance on/off ratio. A resistance on/off ratio as large as 3 × 10⁶ is achieved with an applied pulse voltage of -8 V and +4 V. In addition, the devices exhibit excellent retention and anti-fatigue characteristics. The memristor behavior of Au/BGFNO/NSTO is attributed to the switching of polarization states, which modulate the width and height of the barrier at the BGFNO/NSTO interface. The excellent resistive switching properties in Au/BGFNO/NSTO devices indicate the promising application in nonvolatile memory.

ABO3 multiferroic perovskite materials for memristive memory and neuromorphic computing

The unique electron spin, transfer, polarization and magnetoelectric coupling characteristics of ABO₃ multiferroic perovskite materials make them promising candidates for application in multifunctional nanoelectronic devices. Reversible ferroelectric polarization, controllable defect concentration and domain wall movement originated from the ABO₃ multiferroic perovskite materials promotes its memristive effect, which further highlights data storage, information processing and neuromorphic computing in diverse artificial intelligence applications. In particular, ion doping, electrode selection, and interface modulation have been demonstrated in ABO₃-based memristive devices for ultrahigh data storage, ultrafast information processing, and efficient neuromorphic computing. These approaches presented today including controlling the dopant in the active layer, altering the oxygen vacancy distribution, modulating the diffusion depth of ions, and constructing the interface-dependent band structure were believed to be efficient methods for obtaining unique resistive switching (RS) behavior for various applications. In this review, internal physical dynamics, preparation technologies, and modulation methods are systemically examined as well as the progress, challenges, and possible solutions are proposed for next generation emerging ABO₃-based memristive application in artificial intelligence.