High-Speed Switching and Giant Electroresistance in an Epitaxial Hf0.5Zr0.5O2-Based Ferroelectric Tunnel Junction Memristor

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.

Physics, Structures, and Applications of Fluorite-Structured Ferroelectric Tunnel Junctions

The interest in ferroelectric tunnel junctions (FTJ) has been revitalized by the discovery of ferroelectricity in fluorite-structured oxides such as HfO2 and ZrO2 . In terms of thickness scaling, CMOS compatibility, and 3D integration, these fluorite-structured FTJs provide a number of benefits over conventional perovskite-based FTJs. Here, recent developments involving all FTJ devices with fluorite structures are examined. The transport mechanism of fluorite-structured FTJs is explored and contrasted with perovskite-based FTJs and other 2-terminal resistive switching devices starting with the operation principle and essential parameters of the tunneling electroresistance effect. The applications of FTJs, such as neuromorphic devices, logic-in-memory, and physically unclonable function, are then discussed, along with several structural approaches to fluorite-structure FTJs. Finally, the materials and device integration difficulties related to fluorite-structure FTJ devices are reviewed. The purpose of this review is to outline the theories, physics, fabrication processes, applications, and current difficulties associated with fluorite-structure FTJs while also describing potential future possibilities for optimization.

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.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Revival of Ferroelectric Memories Based on Emerging Fluorite-Structured Ferroelectrics

Over the last few decades, the research on ferroelectric memories has been limited due to their dimensional scalability and incompatibility with complementary metal-oxide-semiconductor (CMOS) technology. The discovery of ferroelectricity in fluorite-structured oxides revived interest in the research on ferroelectric memories, by inducing nanoscale nonvolatility in state-of-the-art gate insulators by minute doping and thermal treatment. The potential of this approach has been demonstrated by the fabrication of sub-30 nm electronic devices. Nonetheless, to realize practical applications, various technical limitations, such as insufficient reliability including endurance, retention, and imprint, as well as large device-to-device-variation, require urgent solutions. Furthermore, such limitations should be considered based on targeting devices as well as applications. Various types of ferroelectric memories including ferroelectric random-access-memory, ferroelectric field-effect-transistor, and ferroelectric tunnel junction should be considered for classical nonvolatile memories as well as emerging neuromorphic computing and processing-in-memory. Therefore, from the viewpoint of materials science, this review covers the recent research focusing on ferroelectric memories from the history of conventional approaches to future prospects.

HfO2-based ferroelectric thin film and memory device applications in the post-Moore era: A review

The rapid development of 5G, big data, and Internet of Things (IoT) technologies is urgently required for novel non-volatile memory devices with low power consumption, fast read/write speed, and high reliability, which are crucial for high-performance computing. Ferroelectric memory has undergone extensive investigation as a viable alternative for commercial applications since the post-Moore era. However, conventional perovskite-structure ferroelectrics (e.g., PbZr x Ti1- x O3) encounter severe limitations for high-density integration owing to the size effect of ferroelectricity and incompatibility with complementary metal-oxide-semiconductor technology. Since 2011, the ferroelectric field has been primarily focused on HfO2-based ferroelectric thin films owing to their exceptional scalability. Several reviews discussing the control of ferroelectricity and device applications exist. It is believed that a comprehensive understanding of mechanisms based on industrial requirements and concerns is necessary, such as the wake-up effect and fatigue mechanism. These mechanisms reflect the atomic structures of the materials as well as the device physics. Herein, a review focusing on phase stability and domain structure is presented. In addition, the recent progress in related ferroelectric memory devices and their challenges is briefly discussed.

Wurtzite and fluorite ferroelectric materials for electronic memory

Ferroelectric materials, the charge equivalent of magnets, have been the subject of continued research interest since their discovery more than 100 years ago. The spontaneous electric polarization in these crystals, which is non-volatile and programmable, is appealing for a range of information technologies. However, while magnets have found their way into various types of modern information technology hardware, applications of ferroelectric materials that use their ferroelectric properties are still limited. Recent advances in ferroelectric materials with wurtzite and fluorite structure have renewed enthusiasm and offered new opportunities for their deployment in commercial-scale devices in microelectronics hardware. This Review focuses on the most recent and emerging wurtzite-structured ferroelectric materials and emphasizes their applications in memory and storage-based microelectronic hardware. Relevant comparisons with existing fluorite-structured ferroelectric materials are made and a detailed outlook on ferroelectric materials and devices applications is provided.

High-Speed Nanoscale Ferroelectric Tunnel Junction for Multilevel Memory and Neural Network Computing

Ferroelectric tunnel junction (FTJ) is one promising candidate for next-generation nonvolatile data storage and neural network computing systems. In this work, the high-performance 50 nm-diameter Au/Ti/PbZr_0.52Ti_0.48O₃ (∼3 nm, (111)-oriented)/Nb:SrTiO₃ (Nb: 0.7 wt %) FTJs are achieved to demonstrate the scaling down capability of FTJ. As a nonvolatile memory, the FTJ shows eight distinct resistance states (3 bits) with a large ON/OFF ratio (>10³), and these states can be switched at a fast speed of 10 ns. Intriguingly, the long-term potentiation/depression and spike timing-dependent plasticity, that is, fundamental functions of biological synapses, can be emulated in the nanoscale FTJ-based artificial synapse. A convolutional neural network (CNN) simulation is then carried out based on the experimental results, and a high recognition accuracy of ∼93.8% on fashion product images is obtained, which is very close to the result of ∼94.4% by a floating-point-based CNN software. In particular, the FTJ-based CNN simulation also exhibits robustness to input image noises. These results indicate the great potential of FTJ for high-density information storage and neural network computing.