ABO3 multiferroic perovskite materials for memristive memory and neuromorphic computing

Flame Spray Pyrolysis Synthesis of Vo-Rich Nano-SrTiO3-x

Engineering of oxygen vacancies (Vo) in nanomaterials allows diligent control of their physicochemical properties. SrTiO3 possesses the typical ABO3 structure and has attracted considerable attention among the titanates due to its chemical stability and its high conduction band energy. This has resulted in its extensive use in photocatalytic energy-related processes, among others. Herein, we introduce the use of Flame Spray Pyrolysis (FSP); an industrial and scalable process to produce Vo-rich SrTiO3 perovskites. We present two types of Anoxic Flame Spray Pyrolysis (A-FSP) technologies using CH4 gas as a reducing source: Radial A-FSP (RA-FSP); and Axial A-FSP (AA-FSP). These are used for the control engineering of oxygen vacancies in the SrTiO3-x nanolattice. Based on X-ray photoelectron spectroscopy, Raman and thermogravimetry-differential thermal analysis, we discuss the role and the amount of the Vos in the so-produced nano-SrTiO3-x, correlating the properties of the nanolattice and energy-band structure of the SrTiO3-x. The present work further corroborates the versatility of FSP as a synthetic process and the potential future application of this process to engineer photocatalysts with oxygen vacancies in quantities that can be measured in kilograms.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

Design of high polarization low switching barrier hybrid improper ferroelectric perovskite oxide superlattices

Hybrid improper ferroelectricity is a useful tool to design ABO3/A'BO3 polar superlattices from non polar building blocks. In this study, we have designed high polarization-low switching barrier hybrid improper ferroelectric superlattices with efficient polarization, and polarization-magnetization switching properties above room temperature, using density functional theory and ab initio molecular dynamics simulations. Superlattices with a chemical formula of (AAlO3)m/(A'AlO3)n, where m/n = 1/1, 1/3, 3/1, 1/5 and 5/1, A, A' = Lanthanide and Y cations are considered to outline the design principles behind polarization switching and (LaFeO3)3/(CeFeO3)1 is investigated for polarization-magnetization switching. We find that the unconventional switching paths via out-of-phase rotation QR- (a0a0c-) and tilt precession QTP always yield lower switching barrier compared to those via in-phase rotation QR+ (a0a0c+) and tilt QT (a-a-c0) of BO6 octahedra. Results from ab initio molecular dynamics simulations estimate the temperature at which the lowest energy barrier can be overcome. It is possible to tune the polarization switching barrier by tuning the tolerance factor, A,A' cation radius mismatch and super lattice periodicity. For switching via QR-, the switching barrier varies exponentially with rotation angle, indicating how high switching barrier is expected for systems, away from cubic symmetry. We provide a recipe to overcome such a bottleneck by tuning superlattice periodicity. Finally, we have proposed the multiferroic device application concept through a proposed polarization-temperature hysteresis loop and magnetization switching.

Comprehensive analysis of novel cubic HgCrO3 perovskite: a first principles, structural, thermodynamic, and magnetic properties study for spintronic applications

The main goal of modern manufacturing is to create products that are affordable, eco-friendly, and energy-efficient. With a focus on HgCrO3, this study sought to discover molecules that meet these requirements. The structural, electrical, thermodynamic, and transport properties of the material were investigated using Wien2K, a full-potential, linearized augmented plane wave program (FP LAPW). Utilizing the generalised gradient approximation (GGA) and lattice constants that have previously produced excellent theoretical and practical findings, structural optimization was carried out. Calculated HgCrO3 magnetic characteristics show that the Cr and Hg atoms are the main contributors to magnetism. Over a temperature range of 0-1200 K and a pressure range of 0-196 GPa, thermodynamic characteristics were evaluated. The thermoelectric properties of HgCrO3 were evaluated using the Boltzmann transport method provided by the BoltzTrap program. This analysis revealed that at room temperature, the figures of merit (ZT) values for HgCrO3 were nearly equal to one. A ZT value close to one indicates that a material has excellent thermoelectric properties and can efficiently convert heat into electricity or vice versa. This investigation highlights the promising thermoelectric capabilities of HgCrO3, which could contribute to more sustainable and energy-efficient technologies in the future.

Hybridization state transition-driven carbon quantum dot (CQD)-based resistive switches for bionic synapses

As an emerging carbon-based material, carbon quantum dots (CQDs) have shown unstoppable prospects in the field of bionic electronics with their outstanding optoelectronic properties and unique biocompatible characteristics. In this study, a novel CQD-based memristor is proposed for neuromorphic computing. Unlike the models that rely on the formation and rupturing of conductive filaments, it is speculated that the resistance switching mechanism of CQD-based memristors is due to the conductive path caused by the hybridization state transition of the sp² carbon domain and sp³ carbon domain induced by the reversible electric field. This avoids the drawback of uncontrollable nucleation sites leading to the random formation of conductive filaments in resistive switching. Importantly, it illustrates that the coefficient of variation (C_V) of the threshold voltage can be as low as -1.551% and 0.083%, which confirms the remarkable uniform switching characteristics. Interestingly, the Pavlov's dog reflection as an important biological behavior can be demonstrated by the samples. Finally, the accuracy recognition rate of MNIST handwriting can reach up to 96.7%, which is very close to the ideal number (97.8%). A carbon-based memristor based on a new mechanism presented provides new possibilities for the improvement of brain-like computing.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

BaTiO3/NixZn1−xFe2O4 (x = 0, 0.5, 1) Composites Synthesized by Thermal Decomposition: Magnetic, Dielectric and Ferroelectric Properties

To investigate the influence of spinel structure and sintering temperature on the functional properties of BaTiO3/NixZn1−xFe2O4 (x = 0, 0.5, 1), NiFe2O4, ZnFe2O4, and Ni0.5Zn0.5Fe2O4 were in situ prepared by thermal decomposition onto BaTiO3 surface from acetylacetonate precursors. As-prepared powders were additionally sintered at 1150 °C and 1300 °C. X-ray powder diffraction (XRPD) and scanning electron microscopy (SEM) coupled with electron dispersive spectroscopy (EDS) were used for the detailed examination of phase composition and morphology. The magnetic, dielectric, and ferroelectric properties were investigated. The optimal phase composition in the BaTiO3/NiFe2O4 composite, sintered at 1150 °C, resulted in a wide frequency range stability. Additionally, particular phase composition indicates favorable properties such as low conductivity and ideal-like hysteresis loop behavior. The favorable properties of BaTiO3/NiFe2O4 make this particular composite an ideal material choice for further studies on applications of multi-ferroic devices.

Ferroelectric Polarized in Transistor Channel Polarity Modulation for Reward-Modulated Spike-Time-Dependent Plasticity Application

Reward signals reflect the developmental tendency of reinforcement learning (RL) agents. Reward-modulated spike-time-dependent plasticity (R-STDP) is an efficient and concise information processing feature in RL. However, the physical construction of R-STDP normally demands complex circuit design engineering, resulting in large power consumption and large area. In this work, we studied the role of ferroelectric polarization in the modulation of carbon nanotube transistor channel polarity. Furthermore, we applied a modulating channel method to construct a 2T synaptic component by spin-coating technology. Based on the nonvolatility of ferroelectric polarization, the synaptic component constructed has the characteristics of reconfigurable polarity. One channel could be modulated to n-type and the other to p-type. One modulated channel was used to perform the STDP function when the reward signal arrived, and the other modulated channel was used to perform the anti-STDP function when the punishment signal arrived. Finally, R-STDP learning rules are implemented on hardware. This work provides a strategy for hardware construction of RL.

Avalanche criticality in LaAlO[Formula: see text] and the effect of aspect ratio

Ferroic domain dynamics, as a function of external stimuli, can be collectively described as scale-invariant avalanches characterised by a critical exponent that are sensitive to the complexity of the domain microstructure. The understanding and manipulation of these avalanches lies at the heart of developing novel applications such as neuromorphic computing. Here we combine in situ heating optical observations and mean-field analysis to investigate the collective domain behaviour in pure-ferroelastic lanthanum aluminate (LaAlO[Formula: see text]) as a function of aspect ratio, the ratio of sample length to width, where the movement of the domains is predominantly driven by thermal stresses via thermal expansion/contraction during heat cycling. Our observations demonstrate that the aspect ratio induces (1) distinctive domain microstructures at room temperature, (2) a deviation of dynamical behaviour at high temperatures and (3) critical exponent mixing in the higher aspect ratio samples that accompanies this behaviour. While the critical exponents of each aspect ratio fall within mean-field predicted values, we highlight the effect that the aspect ratio has in inducing exponent mixing. Hence, furthering our understanding towards tuning and controlling avalanches which is crucial for fundamental and applied research.

Actual origin and precise control of asymmetrical hysteresis in an individual CH3NH3PbI3 micro/nanowire for optical memory and logic operation

Although CH₃NH₃PbI₃ can present an excellent photoresponse to visible light, its application in solar cells and photodetectors is seriously hindered due to hysteresis behaviour. Moreover, for its origin, there exist different opinions. Herein, we demonstrate a route to realize precise control for the electrical transport of a single CH₃NH₃PbI₃ micro/nanowire by constructing a two-terminal device with asymmetric Ag and C electrodes, and its hysteresis can be clearly identified as a synergistic effect of the redox reaction at the interface of the Ag electrode and the injection and ejection of holes in the interfacial traps of the C electrode rather than its bulk effect. The device can show superior bias amplitude and illumination intensity dependence of hysteresis loops with typical bipolar resistive switching features. Thus, an excellent multilevel nonvolatile optical memory can be effectively realized by the modulation of the illumination and bias, and moreover a logic OR gate operation can be successfully implemented with voltage and illumination as input signals as well. This work clearly reveals and provides a new insight of hysteresis origin that can be attributed to a synergistic effect of two asymmetrical electrode interfaces, and therefore precisely controlling its electrical transport to realize an outstanding application potential in multifunctional devices integrated with optical nonvolatile memory and logic OR gate operation.

Post-silicon nano-electronic device and its application in brain-inspired chips

As information technology is moving toward the era of big data, the traditional Von-Neumann architecture shows limitations in performance. The field of computing has already struggled with the latency and bandwidth required to access memory (“the memory wall”) and energy dissipation (“the power wall”). These challenging issues, such as “the memory bottleneck,” call for significant research investments to develop a new architecture for the next generation of computing systems. Brain-inspired computing is a new computing architecture providing a method of high energy efficiency and high real-time performance for artificial intelligence computing. Brain-inspired neural network system is based on neuron and synapse. The memristive device has been proposed as an artificial synapse for creating neuromorphic computer applications. In this study, post-silicon nano-electronic device and its application in brain-inspired chips are surveyed. First, we introduce the development of neural networks and review the current typical brain-inspired chips, including brain-inspired chips dominated by analog circuit and brain-inspired chips of the full-digital circuit, leading to the design of brain-inspired chips based on post-silicon nano-electronic device. Then, through the analysis of N kinds of post-silicon nano-electronic devices, the research progress of constructing brain-inspired chips using post-silicon nano-electronic device is expounded. Lastly, the future of building brain-inspired chips based on post-silicon nano-electronic device has been prospected.

Applications of biomemristors in next generation wearable electronics

With the rapid development of mobile internet and artificial intelligence, wearable electronic devices have a great market prospect. In particular, information storage and processing of real-time collected data are an indispensable part of wearable electronic devices. Biomaterial-based memristive systems are suitable for storage and processing of the obtained information in wearable electronics due to the accompanying merits, i.e. sustainability, lightweight, degradability, low power consumption, flexibility and biocompatibility. So far, many biomaterial-based flexible and wearable memristive devices were prepared by spin coating or other technologies on a flexible substrate at room temperature. However, mechanical deformation caused by mechanical mismatch between devices and soft tissues leads to the instability of device performance. From the current research and practical application, the device will face great challenges when adapting to different working environments. In fact, some interesting studies have been performed to address the above issues while they were not intensively highlighted and overviewed. Herein, the progress in wearable biomemristive devices is reviewed, and the outlook and perspectives are provided in consideration of the existing challenges during the development of wearable biomemristive systems.

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.