A ferroelectric memristor

Data encryption/decryption and medical image reconstruction based on a sustainable biomemristor designed logic gate circuit

Memristors are considered one of the most promising new-generation memory technologies due to their high integration density, fast read/write speeds, and ultra-low power consumption. Natural biomaterials have attracted interest in integrated circuits and electronics because of their environmental friendliness, sustainability, low cost, and excellent biocompatibility. In this study, a sustainable biomemristor with Ag/mugwort:PVDF/ITO structure was prepared using spin-coating and magnetron sputtering methods, which exhibited excellent durability, significant resistance switching (RS) behavior and unidirectional conduction properties when three metals were used as top electrode. By studying the conductivity mechanism of the device, a charge conduction model was established by the combination of F-N tunneling, redox, and complexation reaction. Finally, the novel logic gate circuits were constructed using the as-prepared memristor, and further memristor based encryption circuit using 3-8 decoder was innovatively designed, which can realize uniform rule encryption and decryption of medical information for data and medical images. Therefore, this work realizes the integration of memristor with traditional electronic technology and expands the applications of sustainable biomemristors in digital circuits, data encryption, and medical image security.

Multistate Ferroelectric Diodes with High Electroresistance Based on van der Waals Heterostructures

Some van der Waals (vdW) materials exhibit ferroelectricity, making them promising for novel nonvolatile memories (NVMs) such as ferroelectric diodes (FeDs). CuInP2S6 (CIPS) is a well-known vdW ferroelectric that has been integrated with graphene for memory devices. Here we demonstrate FeDs with self-rectifying, hysteretic current-voltage characteristics based on vertical heterostructures of 10 nm thick CIPS and graphene. By using vdW indium-cobalt top electrodes and graphene bottom electrodes, we achieve a high electroresistance (on- and off-state resistance ratios) of ∼106, an on-state rectification ratio of 2500 for read/write voltages of 2 V/0.5 V, and a maximum output current density of 100 A/cm2. These metrics compare favorably with state-of-the-art FeDs. Piezoresponse force microscopy measurements show that stabilization of intermediate net polarization states in CIPS leads to stable multibit data retention at room temperature. The combination of two-terminal design, multibit memory, and low-power operation in CIPS-based FeDs is potentially interesting for compute-in-memory and neuromorphic computing applications.

Synchronization of Delayed Memristor-Based Neural Networks via Pinning Control With Local Information

In this article, a novel pinning control method, only requiring information from partial nodes, is developed to synchronize drive-response memristor-based neural networks (MNNs) with time delay. An improved mathematical model of MNNs is established to describe the dynamic behaviors of MNNs accurately. In the existing literature, pinning controllers for synchronization of drive-response systems were designed based on information of all nodes, but in some specific situations, the control gains may be very large and challenging to realize in practice. To overcome this problem, a novel pinning control policy is developed to achieve synchronization of delayed MNNs, which depends only on local information of MNNs, for reducing communication and calculation burdens. Furthermore, sufficient conditions for synchronization of delayed MNNs are provided. Finally, numerical simulation and comparative experiments are conducted to verify the effectiveness and superiority of the proposed pinning control method.

All-Inorganic CsPbBr3 Perovskite Planar-Type Memristors as Optoelectronic Synapses

Mimicking fundamental synaptic working principles with memristors contributes an essential step toward constructing brain-inspired, high-efficiency neuromorphic systems that surpass von Neumann system computers. Here, an electroforming-free planar-type memristor based on a CsPbBr3 single crystal is proposed and exhibits excellent resistive switching (RS) behaviors including stable endurance, ultralow power consumption, and fast switching speed. Furthermore, an optically tunable RS performance is demonstrated by manipulating irradiation intensity and wavelength. Optical analysis techniques such as steady-state photoluminescence and time-resolved photoluminescence are employed to investigate the distribution of Br ions and vacancies before and after quantitative polarization, describing migration dynamic processes to elucidate the RS mechanism. Importantly, a CsPbBr3 single crystal, as the optoelectronic synapse, shows unique potential to emulate photoenhanced synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation/depression, spike-timing-dependent plasticity, spike-voltage-dependent plasticity, and learning-forgetting-relearning process with ultralow per synapse event energy consumption. A classical Pavlov's dog experiment is simulated with a combination of optical and electrical stimulation. Finally, pattern recognition with simulated artificial neural networks based on our synapse reached an accuracy of 93.11%. The special strategy and superior RS characteristics of optoelectronic synapses provide a pathway toward high-performance, energy-efficient neuromorphic electronics.

Adapting magnetoresistive memory devices for accurate and on-chip-training-free in-memory computing

Memristors have emerged as promising devices for enabling efficient multiply-accumulate (MAC) operations in crossbar arrays, crucial for analog in-memory computing (AiMC). However, variations in memristors and associated circuits can affect the accuracy of analog computing. Typically, this is mitigated by on-chip training, which is challenging for memristors with limited endurance. We present a hardware-software codesign using magnetic tunnel junction (MTJ)-based AiMC off-chip calibration that achieves software accuracy without costly on-chip training. Hardware-wise, MTJ devices exhibit ultralow cycle-to-cycle variations, as experimentally evaluated over 1 million mass-produced devices. Software-wise, leveraging this, we propose an off-chip training method to adjust deep neural network parameters, achieving accurate AiMC inference. We validate this approach with MAC operations, showing improved transfer curve linearity and reduced errors. By emulating large-scale neural network models, our codesigned MTJ-based AiMC closely matches software baseline accuracy and outperforms existing off-chip training methods, highlighting MTJ's potential in AI tasks.

Advances on MXene-Based Memristors for Neuromorphic Computing: A Review on Synthesis, Mechanisms, and Future Directions

Neuromorphic computing seeks to replicate the capabilities of parallel processing, progressive learning, and inference while retaining low power consumption by drawing inspiration from the human brain. By further overcoming the constraints imposed by the traditional von Neumann architecture, this innovative approach has the potential to revolutionize modern computing systems. Memristors have emerged as a solution to implement neuromorphic computing in hardware, with research based on developing functional materials for resistive switching performance enhancement. Recently, two-dimensional MXenes, a family of transition metal carbides, nitrides, and carbonitrides, have begun to be integrated into these devices to achieve synaptic emulation. MXene-based memristors have already demonstrated diverse neuromorphic characteristics while enhancing the stability and reducing power consumption. The possibility of changing the physicochemical properties through modifications of the surface terminations, bandgap, interlayer spacing, and oxidation for each existing MXene makes them very promising. Here, recent advancements in MXene synthesis, device fabrication, and characterization of MXene-based neuromorphic artificial synapses are discussed. Then, we focus on understanding the resistive switching mechanisms and how they connect with theoretical and experimental data, along with the innovations made during the fabrication process. Additionally, we provide an in-depth review of the neuromorphic performance, making a connection with the resistive switching mechanism, along with a compendium of each relevant performance factor for nonvolatile and volatile applications. Finally, we state the remaining challenges in MXene-based devices for artificial synapses and the next steps that could be taken for future development.

Ferroelectric Al0.85Sc0.15N and Hf0.5Zr0.5O2 Domain Switching Dynamics

The capability to reliably program partial polarization states with nanosecond programming speed and femtojoule energies per bit in ferroelectrics makes them an ideal candidate to realize multibit memory elements for high-density crossbar arrays, which could enable neural network models with a large number of parameters at the edge. However, a thorough understanding of the domain switching dynamics involved in the polarization reversal is required to achieve full control of the multibit capability. Transient current integration measurements are adopted to investigate the domain dynamics in aluminum scandium nitride (Al0.85Sc0.15N) and hafnium zirconium oxide (Hf0.5Zr0.5O2). The switching dynamics are correlated to the crystal structure of the films. The contributions of domain nucleation and domain wall motion are decoupled by analyzing the rate of change of the time-dependent normalized switched polarization. Thermally activated creep domain wall motion characterizes the Al0.85Sc0.15N switching dynamics. The statistics of independently nucleating domains and the domain wall creep motion in Hf0.5Zr0.5O2 are associated with the spatially inhomogeneous distribution of local switching field due to polymorphism, absence of preferential crystallite orientation, as well as defects and charges that can be located at the grain boundaries. The c-axis texture, single-phase nature, and strong likelihood of less fabrication process-induced defects contribute to the homogeneity of the local switching field in Al0.85Sc0.15N. Nonetheless, defects generated and redistributed upon bipolar electric field switching cycling result in Al0.85Sc0.15N domain wall pinning. The wake-up effect in Hf0.5Zr0.5O2 is explained thorough the continuous addition of switchable regions associated with two independent distributions of characteristic switching times.

Memristive True Random Number Generator for Security Applications

This study explores memristor-based true random number generators (TRNGs) through their evolution and optimization, stemming from the concept of memristors first introduced by Leon Chua in 1971 and realized in 2008. We will consider memristor TRNGs coming from various entropy sources for producing high-quality random numbers. However, we must take into account both their strengths and weaknesses. The comparison with CMOS-based TRNGs will serve as an illustration that memristor TRNGs stand out due to their simpler circuits and lower power consumption- thus leading us into a case study involving electroless YMnO3 (YMO) memristors as TRNG entropy sources that demonstrate good security properties by being able to produce unpredictable random numbers effectively. The end of our analysis sees us pinpointing challenges: post-processing algorithm optimization coupled with ensuring reliability over time for memristor-based TRNGs aimed at next-generation security applications.

Voltage-Driven Fluorine Motion for Novel Organic Spintronic Memristor

Integrating tunneling magnetoresistance (TMR) effect in memristors is a long-term aspiration because it allows to realize multifunctional devices, such as multi-state memory and tunable plasticity for synaptic function. However, the reported TMR in different multiferroic tunnel junctions is limited to 100%. This work demonstrates a giant TMR of -266% in La0.6Sr0.4MnO3(LSMO)/poly(vinylidene fluoride)(PVDF)/Co memristor with thin organic barrier. Different from the ferroelectricity-based memristors, this work discovers that the voltage-driven florine (F) motion in the junction generates a huge reversible resistivity change up to 106% with nanosecond (ns) timescale. Removing F from PVDF layer suppresses the dipole field in the tunneling barrier, thereby significantly enhances the TMR. Furthermore, the TMR can be tuned by different polarizing voltage due to the strong modification of spin-polarization at the LSMO/PVDF interface upon F doping. Combining of high TMR in the organic memristor paves the way to develop high-performance multifunctional devices for storage and neuromorphic applications.

Floating-gate memristor based on a MoS2/h-BN/AuNPs mixed-dimensional heterostructure

Memristors have recently received substantial attention because of their promising and unique emerging applications in neuromorphic computing, which can achieve gains in computation speed by mimicking the topology of the brain in electronic circuits. Traditional memristors made of bulk MoO3and HfO2, for example, suffer from a low switching ratio and poor durability and stability. In this work, a floating-gate memristor is developed based on a mixed-dimensional heterostructure comprising two-dimensional (2D) molybdenum disulfide (MoS2) and zero-dimensional (0D) Au nanoparticles (AuNPs) separated by an insulating hexagonal boron nitride (h-BN) layer (MoS2/h-BN/AuNPs). We find that under the modulation of back-gate voltages, the MoS2/h-BN/AuNPs device operates reliably between a high-resistance state (HRS) and a low-resistance state (LRS) and shows multiple stable LRS states, demonstrating the excellent potential of our memristor in multibit storage applications. The modulation effect can be attributed to electron quantum tunneling between the AuNP charge-trapping layer and the MoS2channel. Our memristor exhibits excellent durability and stability: the HRS and LRS are retained for more than 104s without obvious degradation and the on/off ratio is >104after more than 3000 switching cycles. We also demonstrate frequency-dependent memory properties upon stimulation with electrical and optical pulses.

Nanofluidic Ionic Memristors

Living organisms use ions and small molecules as information carriers to communicate with the external environment at ultralow power consumption. Inspired by biological systems, artificial ion-based devices have emerged in recent years to try to realize efficient information-processing paradigms. Nanofluidic ionic memristors, memory resistors based on confined fluidic systems whose internal ionic conductance states depend on the historical voltage, have attracted broad attention and are used as neuromorphic devices for computing. Despite their high exposure, nanofluidic ionic memristors are still in the initial stage. Therefore, systematic guidance for developing and reasonably designing ionic memristors is necessary. This review systematically summarizes the history, mechanisms, and potential applications of nanofluidic ionic memristors. The essential challenges in the field and the outlook for the future potential applications of nanofluidic ionic memristors are also discussed.

Memristive switching in the surface of a charge-density-wave topological semimetal

Owing to the outstanding properties provided by nontrivial band topology, topological phases of matter are considered as a promising platform towards low-dissipation electronics, efficient spin-charge conversion, and topological quantum computation. Achieving ferroelectricity in topological materials enables the non-volatile control of the quantum states, which could greatly facilitate topological electronic research. However, ferroelectricity is generally incompatible with systems featuring metallicity due to the screening effect of free carriers. In this study, we report the observation of memristive switching based on the ferroelectric surface state of a topological semimetal (TaSe4)2I. We find that the surface state of (TaSe4)2I presents out-of-plane ferroelectric polarization due to surface reconstruction. With the combination of ferroelectric surface and charge-density-wave-gapped bulk states, an electric-switchable barrier height can be achieved in (TaSe4)2I-metal contact. By employing a multi-terminal-grounding design, we manage to construct a prototype ferroelectric memristor based on (TaSe4)2I with on/off ratio up to 103, endurance over 103 cycles, and good retention characteristics. The origin of the ferroelectric surface state is further investigated by first-principles calculations, which reveal an interplay between ferroelectricity and band topology. The emergence of ferroelectricity in (TaSe4)2I not only demonstrates it as a rare but essential case of ferroelectric topological materials, but also opens new routes towards the implementation of topological materials in functional electronic devices.

Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics

Organic artificial neurons operating in liquid environments are crucial components in neuromorphic bioelectronics. However, the current understanding of these neurons is limited, hindering their rational design and development for realistic neuronal emulation in biological settings. Here we combine experiments, numerical non-linear simulations, and analytical tools to unravel the operation of organic artificial neurons. This comprehensive approach elucidates a broad spectrum of biorealistic behaviors, including firing properties, excitability, wetware operation, and biohybrid integration. The non-linear simulations are grounded in a physics-based framework, accounting for ion type and ion concentration in the electrolytic medium, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link the neurons spiking features with material and physical parameters, bridging closer the domains of artificial neurons and neuroscience. This work provides streamlined and transferable guidelines for the design, development, engineering, and optimization of organic artificial neurons, advancing next generation neuronal networks, neuromorphic electronics, and bioelectronics.

New-Style Logic Operation and Neuromorphic Computing Enabled by Optoelectronic Artificial Synapses in an MXene/Y:HfO2 Ferroelectric Memristor

Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.

Emerging ferroelectric materials ScAlN: applications and prospects in memristors

The research found that after doping with rare earth elements, a large number of electrons and holes will be produced on the surface of AlN, which makes the material have the characteristics of spontaneous polarization. A new type of ferroelectric material has made a new breakthrough in the application of nitride-materials in the field of integrated devices. In this paper, the application prospects and development trends of ferroelectric material ScAlN in memristors are reviewed. Firstly, various fabrication processes and structures of the current ScAlN thin films are described in detail to explore the implementation of their applications in synaptic devices. Secondly, a series of electrical properties of ScAlN films, such as the current switching ratio and long-term cycle durability, were tested to explore whether their electrical properties could meet the basic needs of memristor device materials. Finally, a series of summaries on the current research studies of ScAlN thin films in the synaptic simulation are made, and the working state of ScAlN thin films as a synaptic device is observed. The results show that the ScAlN ferroelectric material has high residual polarization, no wake-up function, excellent stability and obvious STDP behavior, which indicates that the modified material has wide application prospects in the research and development of memristors.

An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing

Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of traditional von Neumann computing architecture. In this work, an adjustable multistage resistance switching ferroelectric Bi2FeCrO6 diode artificial synaptic device was fabricated using a sol-gel method with a simple process. The device exhibits nonlinearity in its electrical characteristics, demonstrating tunable multistage resistance switching behavior and a strong ferroelectric diode effect through the manipulation of ferroelectric polarization. One of its salient advantages resides in its capacity to dynamically regulate its polarization state in response to an external electric field, thereby facilitating the fine-tuning of synaptic connection strength while maintaining synaptic stability. The device is capable of accurately simulating the fundamental properties of biological synapses, including long/short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity. Additionally, the device exhibits a distinctive photoelectric response and is capable of inducing synaptic plasticity by light signal activation. The utilization of a femtosecond laser for the scrutiny of carrier transport mechanisms imparts profound insights into the intricate dynamics governing the optical memory effect. Furthermore, utilizing a convolutional neural network (CNN) architecture, the recognition accuracy of the MNIST and fashion MNIST datasets was improved to 95.6% and 78%, respectively, through the implementation of improved random adaptive algorithms. These findings present a new opportunity for utilizing Bi2FeCrO6 materials in the development of artificial synapses for neuromorphic computation.

Computational Study on Interlocked-Ferroelectricity-Contributed High-Performance Memristors Based on Two-Dimensional van der Waals Ferroelectric Semiconductors

In order to realize the prevailing artificial intelligence technology, memristor-implemented in-memory or neuromorphic computing is highly expected to break the bottleneck of von Neumann computers. Although high-performance memristors have been vigorously developed in labs or in industry, systematic computational investigations on memristors are seldom. Hence, it is urgent to provide theoretical or computational support for the exploration of memristor operating mechanisms or the screening of memristor materials. Here, a computational method based on the main input parameters learned from the first-principles calculations was developed to measure resistance switching of two-terminal memristors with sandwiched metal/ferroelectric semiconductor/metal architectures, which strikingly agrees with the experimental measurements. Based on our developed method, the diverse multiterminal memristors were designed to fully exploit the application of interlocked ferroelectricity of a ferroelectric semiconductor and realize their heterosynaptic plasticity, and their heterosynaptic behaviors can still be well described. Our developed method can provide a paradigm for the emulation of ferroelectric memristors and inspire subsequent computational exploration. Furthermore, our study also supplies a device optimization strategy based on the interlocked ferroelectricity and easy processing of two-dimensional van der Waals ferroelectric semiconductors, and our proposed heterosynaptic memristors still await further experimental exploration.

Halide Perovskite Inducing Anomalous Nonvolatile Polarization in Poly(vinylidene fluoride)-based Flexible Nanocomposites

Ferroelectric materials have important applications in transduction, data storage, and nonlinear optics. Inorganic ferroelectrics such as lead zirconate titanate possess large polarization, though they are rigid and brittle. Ferroelectric polymers are light weight and flexible, yet their polarization is low, bottlenecked at 10 μC cm-2. Here we show poly(vinylidene fluoride) nanocomposite with only 0.94% of self-nucleated CH3NH3PbBr3 nanocrystals exhibits anomalously large polarization (~19.6 μC cm-2) while retaining superior stretchability and photoluminance, resulting in unprecedented electromechanical figures of merit among ferroelectrics. Comprehensive analysis suggests the enhancement is accomplished via delicate defect engineering, with field-induced Frenkel pairs in halide perovskite stabilized by the poled ferroelectric polymer through interfacial coupling. The strategy is general, working in poly(vinylidene fluoride-co-hexafluoropropylene) as well, and the nanocomposite is stable. The study thus presents a solution for overcoming the electromechanical dilemma of ferroelectrics while enabling additional optic-activity, ideal for multifunctional flexible electronics applications.

Kinetics of N- to M-Polar Switching in Ferroelectric Al1-xScxN Capacitors

Ferroelectric wurtzite-type aluminum scandium nitride (Al1-xScxN) presents unique properties that can enhance the performance of non-volatile memory technologies. The realization of the full potential of Al1-xScxN requires a comprehensive understanding of the mechanism of polarization reversal and domain structure dynamics involved in the ferroelectric switching process. In this work, transient current integration measurements performed by a pulse switching method are combined with domain imaging by piezoresponse force microscopy (PFM) to investigate the kinetics of domain nucleation and wall motion during polarization reversal in Al0.85Sc0.15N capacitors. In the studied electric field range (from 4.4 to 5.6 MV cm-1), ferroelectric switching proceeds via domain nucleation and wall movement. The currently available phenomenological models are shown to not fully capture all the details of the complex dynamics of polarization reversal in Al0.85Sc0.15N. PFM reveals a non-linear increase of both domain nucleation rate and lateral wall velocity during the switching process, as well as the dependency of the domain pattern on the polarization reversal direction. A continuously faster N- to M-polar switching upon cycling is reported and ascribed to an increasing number of M-polar nucleation sites and density of domain walls.

Silicon-Compatible Ferroelectric Tunnel Junctions with a SiO2/Hf0.5Zr0.5O2 Composite Barrier as Low-Voltage and Ultra-High-Speed Memristors

The big data era requires ultrafast, low-power, and silicon-compatible materials and devices for information storage and processing. Here, ferroelectric tunnel junctions (FTJs) based on SiO2/Hf0.5Zr0.5O2 composite barrier and both conducting electrodes are designed and fabricated on Si substrates. The FTJ achieves the fastest write speed of 500 ps under 5 V (2 orders of magnitude faster than reported silicon-compatible FTJs) or 10 ns speed at a low voltage of 1.5 V (the lowest voltage among FTJs at similar speeds), low write current density of 1.3 × 104 A cm-2, 8 discrete states, good retention > 105 s at 85 °C, and endurance > 107. In addition, it provides a large read current (88 A cm-2) at 0.1 V, 2 orders of magnitude larger than reported FTJs. Interestingly, in FTJ-based synapses, gradually tunable conductance states (128 states) with high linearity (<1) are obtained by 10 ns pulses of <1.2 V, and a high accuracy of 91.8% in recognizing fashion product images is achieved by online neural network simulations. These results highlight that silicon-compatible HfO2-based FTJs are promising for high-performance nonvolatile memories and electrical synapses.

Epitaxial Strain Enhanced Ferroelectric Polarization toward a Giant Tunneling Electroresistance

A substantial ferroelectric polarization is the key for designing high-performance ferroelectric nonvolatile memories. As a promising candidate system, the BaTiO3/La0.67Sr0.33MnO3 (BTO/LSMO) ferroelectric/ferromagnetic heterostructure has attracted a lot of attention thanks to the merits of high Curie temperature, large spin polarization, and low ferroelectric coercivity. Nevertheless, the BTO/LSMO heterostructure suffers from a moderate FE polarization, primarily due to the quick film-thickness-driven strain relaxation. In response to this challenge, we propose an approach for enhancing the FE properties of BTO films by using a Sr3Al2O6 (SAO) buffering layer to mitigate the interfacial strain relaxation. The continuously tunable strain allows us to illustrate the linear dependence of polarization on epitaxial strain with a large strain-sensitive coefficient of ∼27 μC/cm2 per percent strain. This results in a giant polarization of ∼80 μC/cm2 on the BTO/LSMO interface. Leveraging this large polarization, we achieved a giant tunneling electroresistance (TER) of ∼105 in SAO-buffered Pt/BTO/LSMO ferroelectric tunnel junctions (FTJs). Our research uncovers the fundamental interplay between strain, polarization magnitude, and device performance, such as on/off ratio, thereby advancing the potential of FTJs for next-generation information storage applications.

Nanotube ferroelectric tunnel junctions with an ultrahigh tunneling electroresistance ratio

Low-dimensional ferroelectric tunnel junctions are appealing for the realization of nanoscale nonvolatile memory devices due to their inherent advantages of device miniaturization. Those based on current mechanisms have limitations, including low tunneling electroresistance (TER) effects and complex heterostructures. Here, we introduce an entirely new TER mechanism to construct a nanotube ferroelectric tunnel junction with ferroelectric nanotubes as the tunneling region. When rolling a ferroelectric monolayer into a nanotube, due to the coexistence of its intrinsic ferroelectric polarization with the flexoelectric polarization induced by bending, a metal-insulator transition occurs depending on the radiative polarization states. For the pristine monolayer, its out-of-plane polarization is tunable by an in-plane electric field, and the conducting states of the ferroelectric nanotube can thus be tuned between metallic and insulating states via axial electric means. Using α-In2Se3 as an example, our first-principles density functional theory calculations and nonequilibrium Green's function formalism confirm the feasibility of the TER mechanism and indicate an ultrahigh TER ratio that exceeds 9.9 × 1010% of the proposed nanotube ferroelectric tunnel junctions. Our findings provide a promising approach based on simple homogeneous structures for high density ferroelectric microelectric devices with excellent ON/OFF performance.

Chemical Vapor Deposition Synthesis of Intrinsic High-Temperature Ferroelectric 2D CuCrSe2

Ultrathin 2D ferroelectrics with high Curie temperature are critical for multifunctional ferroelectric devices. However, the ferroelectric spontaneous polarization is consistently broken by the strong thermal fluctuations at high temperature, resulting in the rare discovery of high-temperature ferroelectricity in 2D materials. Here, a chemical vapor deposition method is reported to synthesize 2D CuCrSe2 nanosheets. The crystal structure is confirmed by scanning transmission electron microscopy characterization. The measured ferroelectric phase transition temperature of ultrathin CuCrSe2 is about ≈800 K. Significantly, the switchable ferroelectric polarization is observed in ≈5.2 nm nanosheet. Moreover, the in-plane and out-of-plane ferroelectric response are modulated by different maximum bias voltage. This work provides a new insight into the construction of 2D ferroelectrics with high Curie temperature.

Proposition of Adaptive Read Bias: A Solution to Overcome Power and Scaling Limitations in Ferroelectric-Based Neuromorphic System

Hardware neuromorphic systems are crucial for the energy-efficient processing of massive amounts of data. Among various candidates, hafnium oxide ferroelectric tunnel junctions (FTJs) are highly promising for artificial synaptic devices. However, FTJs exhibit non-ideal characteristics that introduce variations in synaptic weights, presenting a considerable challenge in achieving high-performance neuromorphic systems. The primary objective of this study is to analyze the origin and impact of these variations in neuromorphic systems. The analysis reveals that the major bottleneck in achieving a high-performance neuromorphic system is the dynamic variation, primarily caused by the intrinsic 1/f noise of the device. As the device area is reduced and the read bias (VRead ) is lowered, the intrinsic noise of the FTJs increases, presenting an inherent limitation for implementing area- and power-efficient neuromorphic systems. To overcome this limitation, an adaptive read-biasing (ARB) scheme is proposed that applies a different VRead to each layer of the neuromorphic system. By exploiting the different noise sensitivities of each layer, the ARB method demonstrates significant power savings of 61.3% and a scaling effect of 91.9% compared with conventional biasing methods. These findings contribute significantly to the development of more accurate, efficient, and scalable neuromorphic systems.

Artificial sensory system based on memristive devices

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.

Quasi-Zero-Dimensional Ferroelectric Polarization Charges-Coupled Resistance Switching with High-Current Density in Ultrascaled Semiconductors

Ferroelectric memristors hold immense promise for advanced memory and neuromorphic computing. However, they face limitations due to low readout current density in conventional designs with low-conductive ferroelectric channels, especially at the nanoscale. Here, we report a ferroelectric-mediated memristor utilizing a 2D MoS2 nanoribbon channel with an ultrascaled cross-sectional area of <1000 nm2, defined by a ferroelectric BaTiO3 nanoribbon stacked on top. Strikingly, the Schottky barrier at the MoS2 contact can be effectively tuned by the charge transfers coupled with quasi-zero-dimensional polarization charges formed at the two ends of the nanoribbon, which results in distinctive resistance switching accompanied by multiple negative differential resistance showing the high-current density of >104 A/cm2. The associated space charges in BaTiO3 are minimized to ∼3.7% of the polarization charges, preserving nonvolatile polarization. This achievement establishes ferroelectric-mediated nanoscale semiconductor memristors with high readout current density as promising candidates for memory and highly energy-efficient in-memory computing applications.

Nanoscale Probing of Electrical Memory Effects in van der Waals Layered PdSe2

Tunable electronic materials that can be switched between different impedance states are fundamental to the hardware elements for neuromorphic computing architectures. This "brain-like" computing paradigm uses highly paralleled and colocated data processing, leading to greatly improved energy efficiency and performance compared to traditional architectures in which data have to be frequently transferred between processor and memory. In this work, we use scanning microwave impedance microscopy for nanoscale electrical and electronic characterization of two-dimensional layered semiconductor PdSe2 to probe neuromorphic properties. The local resolution of tens of nanometers reveals significant differences in electronic behavior between and within PdSe2 nanosheets (NSs). In particular, we detected both n-type and p-type behaviors, although previous reports only point to ambipolar n-type dominating characteristics. Nanoscale capacitance-voltage curves and subsequent calculation of characteristic maps revealed a hysteretic behavior originating from the creation and erasure of Se vacancies as well as the switching of defect charge states. In addition, stacks consisting of two NSs show enhanced resistive and capacitive switching, which is attributed to trapped charge carriers at the interfaces between the stacked NSs. Stacking n- and p-type NSs results in a combined behavior that allows one to tune electrical characteristics. As local inhomogeneities of electrical and electronic behavior can have a significant impact on the overall device performance, the demonstrated nanoscale characterization and analysis will be applicable to a wide range of semiconducting materials.

Giant tunnelling electroresistance in atomic-scale ferroelectric tunnel junctions

Ferroelectric tunnel junctions are promising towards high-reliability and low-power non-volatile memories and computing devices. Yet it is challenging to maintain a high tunnelling electroresistance when the ferroelectric layer is thinned down towards atomic scale because of the ferroelectric structural instability and large depolarization field. Here we report ferroelectric tunnel junctions based on samarium-substituted layered bismuth oxide, which can maintain tunnelling electroresistance of 7 × 105 with the samarium-substituted bismuth oxide film down to one nanometer, three orders of magnitude higher than previous reports with such thickness, owing to efficient barrier modulation by the large ferroelectric polarization. These ferroelectric tunnel junctions demonstrate up to 32 resistance states without any write-verify technique, high endurance (over 5 × 109), high linearity of conductance modulation, and long retention time (10 years). Furthermore, tunnelling electroresistance over 109 is achieved in ferroelectric tunnel junctions with 4.6-nanometer samarium-substituted bismuth oxide layer, which is higher than commercial flash memories. The results show high potential towards multi-level and reliable non-volatile memories.

Leakage Mechanism and Cycling Behavior of Ferroelectric Al0.7Sc0.3N

Ferroelectric scandium-doped aluminum nitride (Al1-xScxN) is of considerable research interest because of its superior ferroelectricity. Studies indicate that Al1-xScxN may suffer from a high leakage current, which can hinder further thickness scaling and long-term reliability. In this work, we systematically investigate the origin of the leakage current in Al0.7Sc0.3N films via experiments and theoretical calculations. The results reveal that the leakage may originate from the nitrogen vacancies with positively charged states and fits well with the trap-assisted Poole-Frenkel (P-F) emission. Moreover, we examine the cycling behavior of ferroelectric Al0.7Sc0.3N-based FeRAM devices. We observe that the leakage current substantially increases when the device undergoes bipolar cycling with a pulse amplitude larger than the coercive electric field. Our analysis shows that the increased leakage current in bipolar cycling is caused by the monotonously reduced trap energy level by monitoring the direct current (DC) leakage under different temperatures and the P-F emission fitting.

Giant Electroresistance in Ferroelectric Tunnel Junctions via High-Throughput Designs: Toward High-Performance Neuromorphic Computing

Ferroelectric tunnel junctions (FTJs) have been regarded as one of the most promising candidates for next-generation devices for data storage and neuromorphic computing owing to their advantages such as fast operation speed, low energy consumption, convenient 3D stack ability, etc. Here, dramatically different from the conventional engineering approaches, we have developed a tunnel barrier decoration strategy to improve the ON/OFF ratio, where the ultrathin SrTiO3 (STO) dielectric layers are periodically mounted onto the BaTiO3 (BTO) ferroelectric tunnel layer using the high-throughput technique. The inserted STO enhances the local tetragonality of the BTO, resulting in a strengthened ferroelectricity in the tunnel layer, which greatly improves the OFF state and reduces the ON state. Combined with the optimized oxygen migration, which can further manipulate the tunneling barrier, a record-high ON/OFF ratio of ∼108 has been achieved. Furthermore, utilizing these FTJ-based artificial synapses, an artificial neural network has been simulated via back-propagation algorithms, and a classification accuracy as high as 92% has been achieved. This study screens out the prominent FTJ by the high-throughput technique, advancing the tunnel layer decoration at the atomic level in the FTJ design and offering a fundamental understanding of the multimechanisms in the tunnel barrier.

Nonvolatile Memristive Effect in Few-Layer CrI3 Driven by Electrostatic Gating

The potential of memristive devices for applications in nonvolatile memory and neuromorphic computing has sparked considerable interest, particularly in exploring memristive effects in two-dimensional (2D) magnetic materials. However, the progress in developing nonvolatile, magnetic field-free memristive devices using 2D magnets has been limited. In this work, we report an electrostatic-gating-induced nonvolatile memristive effect in CrI3-based tunnel junctions. The few-layer CrI3-based tunnel junction manifests notable hysteresis in its tunneling resistance as a function of gate voltage. We further engineered a nonvolatile memristor using the CrI3 tunneling junction with low writing power and at zero magnetic field. We show that the hysteretic transport observed is not a result of trivial effects or inherent magnetic properties of CrI3. We propose a potential association between the memristive effect and the newly predicted ferroelectricity in CrI3 via gating-induced Jahn-Teller distortion. Our work illuminates the potential of 2D magnets in developing next-generation advanced computing technologies.

Filament-free memristors for computing

Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.

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.

Reconfigurable Multifunctional van der Waals Ferroelectric Devices and Logic Circuits

Emerging reconfigurable devices are fast gaining popularity in the search for next-generation computing hardware, while ferroelectric engineering of the doping state in semiconductor materials has the potential to offer alternatives to traditional von-Neumann architecture. In this work, we combine these concepts and demonstrate the suitability of reconfigurable ferroelectric field-effect transistors (Re-FeFETs) for designing nonvolatile reconfigurable logic-in-memory circuits with multifunctional capabilities. Modulation of the energy landscape within a homojunction of a 2D tungsten diselenide (WSe2) layer is achieved by independently controlling two split-gate electrodes made of a ferroelectric 2D copper indium thiophosphate (CuInP2S6) layer. Controlling the state encoded in the program gate enables switching between p, n, and ambipolar FeFET operating modes. The transistors exhibit on-off ratios exceeding 106 and hysteresis windows of up to 10 V width. The homojunction can change from Ohmic-like to diode behavior with a large rectification ratio of 104. When programmed in the diode mode, the large built-in p-n junction electric field enables efficient separation of photogenerated carriers, making the device attractive for energy-harvesting applications. The implementation of the Re-FeFET for reconfigurable logic functions shows how a circuit can be reconfigured to emulate either polymorphic ferroelectric NAND/AND logic-in-memory or electronic XNOR logic with a long retention time exceeding 104 s. We also illustrate how a circuit design made of just two Re-FeFETs exhibits high logic expressivity with reconfigurability at runtime to implement several key nonvolatile 2-input logic functions. Moreover, the Re-FeFET circuit demonstrates high compactness, with an up to 80% reduction in transistor count compared to standard CMOS design. The 2D van de Waals Re-FeFET devices therefore exhibit promising potential for both More-than-Moore and beyond-Moore future of electronics, in particular for an energy-efficient implementation of in-memory computing and machine learning hardware, due to their multifunctionality and design compactness.

Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era

Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.

All-Optical Data Processing with Photon-Avalanching Nanocrystalline Photonic Synapse

Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.

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.

Complex Oxides for Brain-Inspired Computing: A Review

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

ECRAM Materials, Devices, Circuits and Architectures: A Perspective

Non-von-Neumann computing using neuromorphic systems based on two-terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the "write" and "read" operations using a "gate" electrode to tune the conductance state through charge-transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in-depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low-power neuromorphic computing are discussed.

From Ferroelectric Material Optimization to Neuromorphic Devices

Due to the voltage driven switching at low voltages combined with nonvolatility of the achieved polarization state, ferroelectric materials have a unique potential for low power nonvolatile electronic devices. The competitivity of such devices is hindered by compatibility issues of well-known ferroelectrics with established semiconductor technology. The discovery of ferroelectricity in hafnium oxide changed this situation. The natural application of nonvolatile devices is as a memory cell. Nonvolatile memory devices also built the basis for other applications like in-memory or neuromorphic computing. Three different basic ferroelectric devices can be constructed: ferroelectric capacitors, ferroelectric field effect transistors and ferroelectric tunneling junctions. In this article first the material science of the ferroelectricity in hafnium oxide will be summarized with a special focus on tailoring the switching characteristics towards different applications.The current status of nonvolatile ferroelectric memories then lays the ground for looking into applications like in-memory computing. Finally, a special focus will be given to showcase how the basic building blocks of spiking neural networks, the neuron and the synapse, can be realized and how they can be combined to realize neuromorphic computing systems. A summary, comparison with other technologies like resistive switching devices and an outlook completes the paper.

Compute in-Memory with Non-Volatile Elements for Neural Networks: A Review from a Co-Design Perspective

Deep learning has become ubiquitous, touching daily lives across the globe. Today, traditional computer architectures are stressed to their limits in efficiently executing the growing complexity of data and models. Compute-in-memory (CIM) can potentially play an important role in developing efficient hardware solutions that reduce data movement from compute-unit to memory, known as the von Neumann bottleneck. At its heart is a cross-bar architecture with nodal non-volatile-memory elements that performs an analog multiply-and-accumulate operation, enabling the matrix-vector-multiplications repeatedly used in all neural network workloads. The memory materials can significantly influence final system-level characteristics and chip performance, including speed, power, and classification accuracy. With an over-arching co-design viewpoint, this review assesses the use of cross-bar based CIM for neural networks, connecting the material properties and the associated design constraints and demands to application, architecture, and performance. Both digital and analog memory are considered, assessing the status for training and inference, and providing metrics for the collective set of properties non-volatile memory materials will need to demonstrate for a successful CIM technology.

All-ferroelectric implementation of reservoir computing

Reservoir computing (RC) offers efficient temporal information processing with low training cost. All-ferroelectric implementation of RC is appealing because it can fully exploit the merits of ferroelectric memristors (e.g., good controllability); however, this has been undemonstrated due to the challenge of developing ferroelectric memristors with distinctly different switching characteristics specific to the reservoir and readout network. Here, we experimentally demonstrate an all-ferroelectric RC system whose reservoir and readout network are implemented with volatile and nonvolatile ferroelectric diodes (FDs), respectively. The volatile and nonvolatile FDs are derived from the same Pt/BiFeO₃/SrRuO₃ structure via the manipulation of an imprint field (E_imp). It is shown that the volatile FD with E_imp exhibits short-term memory and nonlinearity while the nonvolatile FD with negligible E_imp displays long-term potentiation/depression, fulfilling the functional requirements of the reservoir and readout network, respectively. Hence, the all-ferroelectric RC system is competent for handling various temporal tasks. In particular, it achieves an ultralow normalized root mean square error of 0.017 in the Hénon map time-series prediction. Besides, both the volatile and nonvolatile FDs demonstrate long-term stability in ambient air, high endurance, and low power consumption, promising the all-ferroelectric RC system as a reliable and low-power neuromorphic hardware for temporal information processing.

Multi-state data storage in a two-dimensional stripy antiferromagnet implemented by magnetoelectric effect

A promising approach to the next generation of low-power, functional, and energy-efficient electronics relies on novel materials with coupled magnetic and electric degrees of freedom. In particular, stripy antiferromagnets often exhibit broken crystal and magnetic symmetries, which may bring about the magnetoelectric (ME) effect and enable the manipulation of intriguing properties and functionalities by electrical means. The demand for expanding the boundaries of data storage and processing technologies has led to the development of spintronics toward two-dimensional (2D) platforms. This work reports the ME effect in the 2D stripy antiferromagnetic insulator CrOCl down to a single layer. By measuring the tunneling resistance of CrOCl on the parameter space of temperature, magnetic field, and applied voltage, we verified the ME coupling down to the 2D limit and probed its mechanism. Utilizing the multi-stable states and ME coupling at magnetic phase transitions, we realize multi-state data storage in the tunneling devices. Our work not only advances the fundamental understanding of spin-charge coupling, but also demonstrates the great potential of 2D antiferromagnetic materials to deliver devices and circuits beyond the traditional binary operations.

Ultralow-power in-memory computing based on ferroelectric memcapacitor network

Analog storage through synaptic weights using conductance in resistive neuromorphic systems and devices inevitably generates harmful heat dissipation. This thermal issue not only limits the energy efficiency but also hampers the very-large-scale and highly complicated hardware integration as in the human brain. Here we demonstrate that the synaptic weights can be simulated by reconfigurable non-volatile capacitances of a ferroelectric-based memcapacitor with ultralow-power consumption. The as-designed metal/ferroelectric/metal/insulator/semiconductor memcapacitor shows distinct 3-bit capacitance states controlled by the ferroelectric domain dynamics. These robust memcapacitive states exhibit uniform maintenance of more than 104 s and well endurance of 109 cycles. In a wired memcapacitor crossbar network hardware, analog vector-matrix multiplication is successfully implemented to classify 9-pixel images by collecting the sum of displacement currents (I = C × dV/dt) in each column, which intrinsically consumes zero energy in memcapacitors themselves. Our work sheds light on an ultralow-power neural hardware based on ferroelectric memcapacitors.

Artificial Intelligence and Advanced Materials

Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.

Bimodal ionic photomemristor based on a high-temperature oxide superconductor/semiconductor junction

Memristors, a cornerstone for neuromorphic electronics, respond to the history of electrical stimuli by varying their electrical resistance across a continuum of states. Much effort has been recently devoted to developing an analogous response to optical excitation. Here we realize a novel tunnelling photo-memristor whose behaviour is bimodal: its resistance is determined by the dual electrical-optical history. This is obtained in a device of ultimate simplicity: an interface between a high-temperature superconductor and a transparent semiconductor. The exploited mechanism is a reversible nanoscale redox reaction between both materials, whose oxygen content determines the electron tunnelling rate across their interface. The redox reaction is optically driven via an interplay between electrochemistry, photovoltaic effects and photo-assisted ion migration. Besides their fundamental interest, the unveiled electro-optic memory effects have considerable technological potential. Especially in combination with high-temperature superconductivity which, in addition to facilitating low-dissipation connectivity, brings photo-memristive effects to the realm of superconducting electronics.

Observation of Electric Hysteresis, Polarization Oscillation, and Pyroelectricity in Nonferroelectric p-n Heterojunctions

The switchable electric polarization is usually achieved in ferroelectric materials with noncentrosymmetric structures, which opens exciting opportunities for information storage and neuromorphic computing. In another polar system of p-n junction, there exists the electric polarization at the interface due to the Fermi level misalignment. However, the resultant built-in electric field is unavailable to manipulate, thus attracting less attention for memory devices. Here, we report the interfacial polarization hysteresis (IPH) in the vertical sidewall van der Waals heterojunctions of black phosphorus and quasi-two-dimensional electron gas on SrTiO_{3}. A nonvolatile switching of electric polarization can be achieved by reconstructing the space charge region (SCR) with long-lifetime nonequilibrium carriers. The resulting electric-field controllable IPH is experimentally verified by electric hysteresis, polarization oscillation, and pyroelectric effect. Further studies confirm the transition temperature of 340 K, beyond which the IPH vanishes. The second transition is revealed with the temperature dropping below 230 K, corresponding to the sharp improvement of IPH and the freezing of SCR reconstruction. This work offers new possibilities for exploring the memory phenomena in nonferroelectric p-n heterojunctions.

Lessons from hafnium dioxide-based ferroelectrics

A bit more than a decade after the first report of ferroelectric switching in hafnium dioxide-based ultrathin layers, this family of materials continues to elicit interest. There is ample consensus that the observed switching does not obey the same mechanisms present in most other ferroelectrics, but its exact nature is still under debate. Next to this fundamental relevance, a large research effort is dedicated to optimizing the use of this extraordinary material, which already shows direct integrability in current semiconductor chips and potential for scalability to the smallest node architectures, in smaller and more reliable devices. Here we present a perspective on how, despite our incomplete understanding and remaining device endurance issues, the lessons learned from hafnium dioxide-based ferroelectrics offer interesting avenues beyond ferroelectric random-access memories and field-effect transistors. We hope that research along these other directions will stimulate discoveries that, in turn, will mitigate some of the current issues. Extending the scope of available systems will eventually enable the way to low-power electronics, self-powered devices and energy-efficient information processing.

Ultrathin Nitride Ferroic Memory with Large ON/OFF Ratios for Analog In-Memory Computing

Computing in the analog regime using nonlinear ferroelectric resistive memory arrays can potentially alleviate the energy constraints and complexity/footprint challenges imposed by digital von Neumann systems. Yet the current ferroelectric resistive memories suffer from either low ON/OFF ratios/imprint or limited compatibility with mainstream semiconductors. Here, for the first time, ferroelectric and analog resistive switching in an epitaxial nitride heterojunction comprising ultrathin (≈5 nm) nitride ferroelectrics, i.e., ScAlN, with potentiality to bridge the gap between performance and compatibility is demonstrated. High ON/OFF ratios (up to 10⁵ ), high uniformity, good retention, (<20% variation after > 10⁵  s) and cycling endurance (>10⁴ ) are simultaneously demonstrated in a metal/oxide/nitride ferroelectric junction. It is further demonstrated that the memristor can provide programmability to enable multistate operation and linear analogue computing as well as image processing with high accuracy. Neural network simulations based on the weight update characteristics of the nitride memory yielded an image recognition accuracy of 92.9% (baseline 96.2%) on the images from Modified National Institute of Standards and Technology. The non-volatile multi-level programmability and analog computing capability provide first-hand and landmark evidence for constructing advanced memory/computing architectures based on emerging nitride ferroelectrics, and promote homo and hybrid integrated functional edge devices beyond silicon.