Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing.

Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff's law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.

Large-Area Flexible Memory Arrays of Oriented Molecular Ferroelectric Single Crystals with Nearly Saturated Polarization.

Molecular ferroelectrics (MFs) have been proven to demonstrate excellent properties even comparable to those of inorganic counterparts usually with heavy metals. However, the validation of their device applications is still at the infant stage. The polycrystalline feature of conventionally obtained MF films, the patterning challenges for microelectronics and the brittleness of crystalline films significantly hinder their development for organic integrated circuits, as well as emerging flexible electronics. Here, a large-area flexible memory array is demonstrated of oriented molecular ferroelectric single crystals (MFSCs) with nearly saturated polarization. Highly-uniform MFSC arrays are  prepared on large-scale substrates including Si wafers and flexible substrates using an asymmetric-wetting and microgroove-assisted coating (AWMAC) strategy. Resultant flexible memory arrays exhibit excellent nonvolatile memory properties with a low-operating voltage of <5 V, i.e., nearly saturated ferroelectric polarization (6.5 µC cm-2 ), and long bending endurance (>103 ) under various bending radii. These results may open an avenue for scalable flexible MF electronics with high performance.

Ultra-wide temperature electronic synapses based on self-rectifying ferroelectric memristors.

Memristors have been intensively studied in recent years as promising building blocks for next-generation nonvolatile memory, artificial neural networks and brain-inspired computing systems. However, most memristors cannot simultaneously function in extremely low and high temperatures, limiting their use for many harsh environment applications. Here, we demonstrate that the memristors based on high-Curie temperature ferroelectrics can resolve these issues. Excellent synaptic learning and memory functions can be achieved in BiFeO3 (BFO)-based ferroelectric memristors in an ultra-wide temperature range. Correlation between electronic transport and ferroelectric properties is established by the coincidence of resistance and ferroelectricity switch and the direct visualization of local current and domain distributions. The interfacial barrier modification by the reversal of ferroelectric polarization leads to a robust resistance switching behavior. Various synaptic functions including long-term potentiation/depression, consecutive potentiation/depression and spike-timing dependent plasticity have been realized in the BFO ferroelectric memristors over an extremely wide temperature range of -170 °C âˆ¼ 300 °C, which even can be extended to 500 °C due to the robust ferroelectricity of BFO at high temperatures. Our findings illustrate that the BFO ferroelectric memristors are promising candidates for ultra-wide temperature electronic synapse in extreme or harsh environments.

Graphene Dirac point tuned by ferroelectric polarization field.

Graphene has received numerous attention for future nanoelectronics and optoelectronics. The Dirac point is a key parameter of graphene that provides information about its carrier properties. There are lots of methods to tune the Dirac point of graphene, such as chemical doping, impurities, defects, and disorder. In this study, we report a different approach to tune the Dirac point of graphene using a ferroelectric polarization field. The Dirac point can be adjusted to near the ferroelectric coercive voltage regardless its original position. We have ensured this phenomenon by temperature-dependent experiments, and analyzed its mechanism with the theory of impurity correlation in graphene. Additionally, with the modulation of ferroelectric polymer, the current on/off ratio and mobility of graphene transistor both have been improved. This work provides an effective method to tune the Dirac point of graphene, which can be readily used to configure functional devices such as p-n junctions and inverters.

Interfacial memristors in Al-LaNiO3 heterostructures.

Memristive devices are promising circuit elements that enable novel computational approaches which go beyond the von-Neumann paradigms. Here by tuning the chemistry at the Al-LaNiO3 (LNO) interface, a metal-metal junction, we engineer good switching behavior with good electroresistance (ON-OFF resistance ratios of 100), and repeatable multiple resistance states. The active material responsible for such a behavior is a self-formed sandwich of an AlxOy layer at the interface obtained by grabbing oxygen by Al from LNO. Using aberration corrected electron microscopy and transport measurements, it is confirmed that the memristive hysteresis occurs due to the electric field driven O2- (or ) cycling between LNO (reservoir) and the interlayer, which drives the redox reactions forming and dissolving Al nanoclusters in the AlxOy matrix. This work provides clear insights into and details on precise oxygen control at such interfaces and can be useful for newer opportunities in oxitronics.

New-Generation Ferroelectric AlScN Materials.

Ferroelectrics have great potential in the field of nonvolatile memory due to programmable polarization states by external electric field in nonvolatile manner. However, complementary metal oxide semiconductor compatibility and uniformity of ferroelectric performance after size scaling have always been two thorny issues hindering practical application of ferroelectric memory devices. The emerging ferroelectricity of wurtzite structure nitride offers opportunities to circumvent the dilemma. This review covers the mechanism of ferroelectricity and domain dynamics in ferroelectric AlScN films. The performance optimization of AlScN films grown by different techniques is summarized and their applications for memories and emerging in-memory computing are illustrated. Finally, the challenges and perspectives regarding the commercial avenue of ferroelectric AlScN are discussed.

Ferroelectric materials for neuroinspired computing applications.

In recent years, the emergence of numerous applications of artificial intelligence (AI) has sparked a new technological revolution. These applications include facial recognition, autonomous driving, intelligent robotics, and image restoration. However, the data processing and storage procedures in the conventional von Neumann architecture are discrete, which leads to the "memory wall" problem. As a result, such architecture is incompatible with AI requirements for efficient and sustainable processing. Exploring new computing architectures and material bases is therefore imperative. Inspired by neurobiological systems, in-memory and in-sensor computing techniques provide a new means of overcoming the limitations inherent in the von Neumann architecture. The basis of neural morphological computation is a crossbar array of high-density, high-efficiency non-volatile memory devices. Among the numerous candidate memory devices, ferroelectric memory devices with non-volatile polarization states, low power consumption and strong endurance are expected to be ideal candidates for neuromorphic computing. Further research on the complementary metal-oxide-semiconductor (CMOS) compatibility for these devices is underway and has yielded favorable results. Herein, we first introduce the development of ferroelectric materials as well as their mechanisms of polarization reversal and detail the applications of ferroelectric synaptic devices in artificial neural networks. Subsequently, we introduce the latest developments in ferroelectrics-based in-memory and in-sensor computing. Finally, we review recent works on hafnium-based ferroelectric memory devices with CMOS process compatibility and give a perspective for future developments.

Highly Reliable Van Der Waals Memory Boosted by a Single 2D Charge Trap Medium.

Charge trap materials that can store carriers efficiently and controllably are desired for memory applications. 2D materials are promising for highly compacted and reliable memory mainly due to their ease of constructing atomically uniform interfaces, however, remain unexplored as being charge trap media. Here it is discovered that 2D semiconducting PbI2 is an excellent charge trap material for nonvolatile memory and artificial synapses. It is simple to construct PbI2 -based charge trap devices since no complicated synthesis or additional defect manufacturing are required. As a demonstration, MoS2 /PbI2 device exhibits a large memory window of 120 V, fast write speed of 5 µs, high on-off ratio around 106 , multilevel memory of over 8 distinct states, high reliability with endurance up to 104 cycles and retention over 1.2 × 104 s. It is envisioned that PbI2 with ionic activity caused by the natively formed iodine vacancies is unique to combine with unlimited 2D materials for versatile van der Waals devices with high-integration and multifunctionality.

A ferroelectric fin diode for robust non-volatile memory.

Among today's nonvolatile memories, ferroelectric-based capacitors, tunnel junctions and field-effect transistors (FET) are already industrially integrated and/or intensively investigated to improve their performances. Concurrently, because of the tremendous development of artificial intelligence and big-data issues, there is an urgent need to realize high-density crossbar arrays, a prerequisite for the future of memories and emerging computing algorithms. Here, a two-terminal ferroelectric fin diode (FFD) in which a ferroelectric capacitor and a fin-like semiconductor channel are combined to share both top and bottom electrodes is designed. Such a device not only shows both digital and analog memory functionalities but is also robust and universal as it works using two very different ferroelectric materials. When compared to all current nonvolatile memories, it cumulatively demonstrates an endurance up to 1010 cycles, an ON/OFF ratio of ~102, a feature size of 30 nm, an operating energy of ~20 fJ and an operation speed of 100 ns. Beyond these superior performances, the simple two-terminal structure and their self-rectifying ratio of ~ 104 permit to consider them as new electronic building blocks for designing passive crossbar arrays which are crucial for the future in-memory computing.

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.

Porous Metal-Organic Framework/ReS2 Heterojunction Phototransistor for Polarization-Sensitive Visual Adaptation Emulation.

Visual adaptation allows organisms to accurately perceive the external world even in dramatically changing environments, from dim starlight to bright sunlight. In particular, polarization-sensitive visual adaptation can effectively process the polarized visual information that is ubiquitous in nature. However, such an intriguing characteristic still remains a great challenge in semiconductor devices. Herein, a novel porous metal-organic-framework phototransistor with anisotropic-ReS2 -based heterojunction is demonstrated for polarization-sensitive visual adaptation emulation. The device exhibits intriguing polarized sensitivity and an adaptive ability due to its strong anisotropic and trapping-detrapping characteristics, respectively. A series of polarization-sensitive neuromorphic behaviors like polarization-perceptual excitatory postsynaptic current, multimode adjustable dichroic ratio and reconfigurable sensory adaption, are experimentally demonstrated through this porous heterojunction phototransistor. More importantly, with the polarization-electricity cooperation strategy, advanced polarization-sensitive visual adaptation with strong bottom-gate control and environment dependence is successfully realized. These results represent a significant step toward the new generation of intelligent visual perception systems in autonomous navigation and human-machine interaction, etc.

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.

Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir.

A neuromorphic visual system integrating optoelectronic synapses to perform the in-sensor computing is triggering a revolution due to the reduction of latency and energy consumption. Here it is demonstrated that the dwell time of photon-generated carriers in the space-charge region can be effectively extended by embedding a potential well on the shoulder of Schottky energy barrier. It permits the nonlinear interaction of photocurrents stimulated by spatiotemporal optical signals, which is necessary for in-sensor reservoir computing (RC). The machine vision with the sensor reservoir constituted by designed self-powered Au/P(VDF-TrFE)/Cs2 AgBiBr6 /ITO devices is competent for both static and dynamic vision tasks. It shows an accuracy of 99.97% for face classification and 100% for dynamic vehicle flow recognition. The in-sensor RC system takes advantage of near-zero energy consumption in the reservoir, resulting in decades-time lower training costs than a conventional neural network. This work paves the way for ultralow-power machine vision using photonic devices.

Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision.

Nowadays the development of machine vision is oriented toward real-time applications such as autonomous driving. This demands a hardware solution with low latency, high energy efficiency, and good reliability. Here, we demonstrate a robust and self-powered in-sensor computing paradigm with a ferroelectric photosensor network (FE-PS-NET). The FE-PS-NET, constituted by ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, is capable of simultaneously capturing and processing images. In each FE-PS, self-powered photovoltaic responses, modulated by remanent polarization of an epitaxial ferroelectric Pb(Zr0.2Ti0.8)O3 layer, show not only multiple nonvolatile levels but also sign reversibility, enabling the representation of a signed weight in a single device and hence reducing the hardware overhead for network construction. With multiple FE-PSs wired together, the FE-PS-NET acts on its own as an artificial neural network. In situ multiply-accumulate operation between an input image and a stored photoresponsivity matrix is demonstrated in the FE-PS-NET. Moreover, the FE-PS-NET is faultlessly competent for real-time image processing functionalities, including binary classification between 'X' and 'T' patterns with 100% accuracy and edge detection for an arrow sign with an F-Measure of 1 (under 365 nm ultraviolet light). This study highlights the great potential of ferroelectric photovoltaics as the hardware basis of real-time machine vision.

Research progress on solutions to the sneak path issue in memristor crossbar arrays.

Since the emergence of memristors (or memristive devices), how to integrate them into arrays has been widely investigated. After years of research, memristor crossbar arrays have been proposed and realized with potential applications in nonvolatile memory, logic and neuromorphic computing systems. Despite the promising prospects of memristor crossbar arrays, one of the main obstacles for their development is the so-called sneak-path current causing cross-talk interference between adjacent memory cells and thus may result in misinterpretation which greatly influences the operation of memristor crossbar arrays. Solving the sneak-path current issue, the power consumption of the array will immensely decrease, and the reliability and stability will simultaneously increase. In order to suppress the sneak-path current, various solutions have been provided. So far, some reviews have considered some of these solutions and established a sophisticated classification, including 1D1M, 1T1M, 1S1M (D: diode, M: memristor, T: transistor, S: selector), self-selective and self-rectifying memristors. Recently, a mass of studies have been additionally reported. This review thus attempts to provide a survey on these new findings, by highlighting the latest research progress realized for relieving the sneak-path issue. Here, we first present the concept of the sneak-path current issue and solutions proposed to solve it. Consequently, we select some typical and promising devices, and present their structures and properties in detail. Then, the latest research activities focusing on single-device structures are introduced taking into account the mechanisms underlying these devices. Finally, we summarize the properties and perspectives of these solutions.

Characterization and Application of PVDF and Its Copolymer Films Prepared by Spin-Coating and Langmuir-Blodgett Method.

Poly(vinylidene fluoride) (PVDF) and its copolymers are key polymers, displaying properties such as flexibility and electroactive responses, including piezoelectricity, pyroelectricity, and ferroelectricity. In the past several years, they have been applied in numerous applications, such as memory, transducers, actuators, and energy harvesting and have shown thriving prospects in the ongoing research and commercialization process. The crystalline polymorphs of PVDF can present nonpolar α, ε phase and polar ß, γ, and δ phases with different processing methods. The copolymers, such as poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)), can crystallize directly into a phase analogous to the ß phase of PVDF. Since the ß phase shows the highest dipole moment among polar phases, many reproducible and efficient methods producing ß-phase PVDF and its copolymer have been proposed. In this review, PVDF and its copolymer films prepared by spin-coating and Langmuir-Blodgett (LB) method are introduced, and relevant characterization techniques are highlighted. Finally, the development of memory, artificial synapses, and medical applications based on PVDF and its copolymers is elaborated.

Mediation in the second-order synaptic emulator with conductive atomic force microscopy.

Memristors have been extensively studied for synaptic simulation and neuromorphic computation. Instead of focusing on implementing specific synaptic learning rules by carefully engineering external programming parameters, researchers recently have paid more attention to taking advantage of the second-order memristor that is more analogous to biologic synapses and modulated not only by external inputs but also by internal mechanisms. However, experimental evidence is still scarce. Here, we explore a BiMnO3 memristor by applying simple spike forms. The filament evolution dynamics, including processes of forming and spontaneous decay, were directly observed by the conductive atomic force microscopy (c-AFM) technique. We propose that the unique conductance state of the BMO memristor is regulated by the oxygen vacancy (VO) dynamic process. We believe this primary result is helpful to improve understanding of the internal mechanisms of the second-order oxide memristor, which exhibits promising application in building selectors, memories and neuromorphic-computing systems.

Solid-State Synapse Based on Magnetoelectrically Coupled Memristor.

Brain-inspired computing architectures attempt to emulate the computations performed in the neurons and the synapses in the human brain. Memristors with continuously tunable resistances are ideal building blocks for artificial synapses. Through investigating the memristor behaviors in a La0.7Sr0.3MnO3/BaTiO3/La0.7Sr0.3MnO3 multiferroic tunnel junction, it was found that the ferroelectric domain dynamics characteristics are influenced by the relative magnetization alignment of the electrodes, and the interfacial spin polarization is manipulated continuously by ferroelectric domain reversal, enriching our understanding of the magnetoelectric coupling fundamentally. This creates a functionality that not only the resistance of the memristor but also the synaptic plasticity form can be further manipulated, as demonstrated by the spike-timing-dependent plasticity investigations. Density functional theory calculations are carried out to describe the obtained magnetoelectric coupling, which is probably related to the Mn-Ti intermixing at the interfaces. The multiple and controllable plasticity characteristic in a single artificial synapse, to resemble the synaptic morphological alteration property in a biological synapse, will be conducive to the development of artificial intelligence.