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

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

Photothermally Activated Artificial Neuromorphic Synapses.

Biological nervous systems rely on the coordination of billions of neurons with complex, dynamic connectivity to enable the ability to process information and form memories. In turn, artificial intelligence and neuromorphic computing platforms have sought to mimic biological cognition through software-based neural networks and hardware demonstrations utilizing memristive circuitry with fixed dynamics. To incorporate the advantages of tunable dynamic software implementations of neural networks into hardware, we develop a proof-of-concept artificial synapse with adaptable resistivity. This synapse leverages the photothermally induced local phase transition of VO2 thin films by temporally modulated laser pulses. Such a process quickly modifies the conductivity of the film site-selectively by a factor of 500 to "activate" these neurons and store "memory" by applying varying bias voltages to induce self-sustained Joule heating between electrodes after activation with a laser. These synapses are demonstrated to undergo a complete heating and cooling cycle in less than 120 ns.

Sub-Nanosecond Switching of Si:HfO2 Ferroelectric Field-Effect Transistor.

The discovery of ferroelectric doped HfO2 enabled the emergence of scalable and CMOS-compatible ferroelectric field-effect transistor (FeFET) technology which has the potential to meet the growing need for fast, low-power, low-cost, and high-density nonvolatile memory, and neuromorphic devices. Although HfO2 FeFETs have been widely studied in the past few years, their fundamental switching speed is yet to be explored. Importantly, the shortest polarization time demonstrated to date in HfO2-based FeFET was ∼10 ns. Here, we report that a single subnanosecond pulse can fully switch HfO2-based FeFET. We also study the polarization switching kinetics across 11 orders of magnitude in time (300 ps to 8 s) and find a remarkably steep time-voltage relation, which is captured by the classical nucleation theory across this wide range of pulse widths. These results demonstrate the high-speed capabilities of FeFETs and help better understand their fundamental polarization switching speed limits and switching kinetics.

PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing.

Low-power electronic devices play a pivotal role in the burgeoning artificial intelligence era. The study of such devices encompasses low-subthreshold swing (SS) transistors and neuromorphic devices. However, conventional field-effect transistors (FETs) face the inherent limitation of the "Boltzmann tyranny", which restricts SS to 60 mV decade-1 at room temperature. Additionally, FET-based neuromorphic devices lack sufficient conductance states for highly accurate neuromorphic computing due to a narrow memory window. In this study, we propose a pioneering PZT-enabled MoS2 floating gate transistor (PFGT) configuration, demonstrating a low SS of 46 mV decade-1 and a wide memory window of 7.2 V in the dual-sweeping gate voltage range from -7 to 7 V. The wide memory window provides 112 distinct conductance states for PFGT. Moreover, the PFGT-based artificial neural network achieves an outstanding facial-recognition accuracy of 97.3%. This study lays the groundwork for the development of low-SS transistors and highly energy efficient artificial synapses utilizing two-dimensional materials.

Neuromorphic Liquids, Colloids, and Gels: A Review.

Advances in flexible electronic devices and robotic software require that sensors and controllers be virtually devoid of traditional electronic components, be deformable and stretch-resistant. Liquid electronic devices that mimic biological synapses would make an ideal core component for flexible liquid circuits. This is due to their unbeatable features such as flexibility, reconfiguration, fault tolerance. To mimic synaptic functions in fluids we need to imitate dynamics and complexity similar to those that occurring in living systems. Mimicking ionic movements are considered as the simplest platform for implementation of neuromorphic in material computing systems. We overview a series of experimental laboratory prototypes where neuromorphic systems are implemented in liquids, colloids and gels.

Emerging electrolyte-gated transistors for neuromorphic perception.

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.

2D MoS2 Neuromorphic Devices for Brain-Like Computational Systems.

Hardware implementation of artificial synapses/neurons with 2D solid-state devices is of great significance for nanoscale brain-like computational systems. Here, 2D MoS2 synaptic/neuronal transistors are fabricated by using poly(vinyl alcohol) as the laterally coupled, proton-conducting electrolytes. Fundamental synaptic functions, such as an excitatory postsynaptic current, paired-pulse facilitation, and a dynamic filter for information transmission of biological synapse, are successfully emulated. Most importantly, with multiple input gates and one modulatory gate, spiking-dependent logic operation/modulation, multiplicative neural coding, and neuronal gain modulation are also experimentally demonstrated. The results indicate that the intriguing 2D MoS2 transistors are also very promising for the next-generation of nanoscale neuromorphic device applications.

Versatile Papertronics: Photo-Induced Synapse and Security Applications on Papers.

Paper is a readily available material in nature. Its recyclability, eco-friendliness, portability, flexibility, and affordability make it a favored substrate for researchers seeking cost-effective solutions. Electronic devices based on solution process are fabricated on paper and banknotes using PVK and SnO2 nanoparticles. The devices manufactured on paper substrates exhibit photosynaptic behavior under ultraviolet pulse illumination, stemming from numerous interactions on the surface of the SnO2 nanoparticles. A light-modulated artificial synapse device is realized on a paper at a low voltage bias of -0.01 V, with an average recognition rate of 91.7% based on the Yale Face Database. As a security device on a banknote, 400 devices in a 20 × 20 array configuration exhibited random electrical characteristics owing to the local morphology of the SnO2 nanoparticles and differences in the depletion layer width at the SnO2/PVK interface. The security Physically Unclonable Functions (PUF) key based on the current distribution extracted at -1 V show unpredictable reproducibility with 50% uniformity, 48.7% inter-Hamming distance, and 50.1% bit-aliasing rates. Moreover, the device maintained its properties for more than 210 days under a curvature radius of 8.75 mm and bias and UV irradiation stress conditions.

Concealing Organic Neuromorphic Devices with Neuronal-Inspired Supported Lipid Bilayers.

Neurohybrid systems have gained large attention for their potential as in vitro and in vivo platform to interrogate and modulate the activity of cells and tissue within nervous system. In this scenario organic neuromorphic devices have been engineered as bioelectronic platforms to resemble characteristic neuronal functions. However, aiming to a functional communication with neuronal cells, material synthesis, and surface engineering can yet be exploited for optimizing bio-recognition processes at the neuromorphic-neuronal hybrid interface. In this work, artificial neuronal-inspired lipid bilayers have been assembled on an electrochemical neuromorphic organic device (ENODe) to resemble post-synaptic structural and functional features of living synapses. Here, synaptic conditioning has been achieved by introducing two neurotransmitter-mediated biochemical signals, to induce an irreversible change in the device conductance thus achieving Pavlovian associative learning. This new class of in vitro devices can be further exploited for assembling hybrid neuronal networks and potentially for in vivo integration within living neuronal tissues.

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision.

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.

Electron-Sponge Nature of Polyoxometalates for Next-Generation Electrocatalytic Water Splitting and Nonvolatile Neuromorphic Devices.

Designing next-generation molecular devices typically necessitates plentiful oxygen-bearing sites to facilitate multiple-electron transfers. However, the theoretical limits of existing materials for energy conversion and information storage devices make it inevitable to hunt for new competitors. Polyoxometalates (POMs), a unique class of metal-oxide clusters, have been investigated exponentially due to their structural diversity and tunable redox properties. POMs behave as electron-sponges owing to their intrinsic ability of reversible uptake-release of multiple electrons. In this review, numerous POM-frameworks together with desired features of a contender material and inherited properties of POMs are systematically discussed to demonstrate how and why the electron-sponge-like nature of POMs is beneficial to design next-generation water oxidation/reduction electrocatalysts, and neuromorphic nonvolatile resistance-switching random-access memory devices. The aim is to converge the attention of scientists who are working separately on electrocatalysts and memory devices, on a point that, although the application types are different, they all hunt for a material that could exhibit electron-sponge-like feature to realize boosted performances and thus, encouraging the scientists of two completely different fields to explore POMs as imperious contenders to design next-generation nanodevices. Finally, challenges and promising prospects in this research field are also highlighted.

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.

Nanocrystal Materials for Resistive Memory and Artificial Synapses: Progress and Prospects.

BACKGROUND: Resistive random-access memory (RRAM) is considered to be the most promising next-generation non-volatile memory because of its low cost, low energy consumption, and excellent data storage characteristics. However, the on/off (SET/RESET) voltages of RRAM are too random to replace the traditional memory. Nanocrystals (NCs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low-cost, large-area, and solution-processed technologies. Therefore, the doping NCs in the function layer of RRAM are proposed to localize the electric field and guide conductance filaments (CFs) growth. OBJECTIVE: The purpose of this article is to focus on a comprehensive and systematical survey of the NC materials, which are used to improve the performance of resistive memory (RM) and optoelectronic synaptic devices and review recent experimental advances in NC-based neuromorphic devices from artificial synapses to light-sensory synaptic platforms. METHODS: Extensive information related to NCs for RRAM and artificial synapses and their associated patents were collected. This review aimed to highlight the unique electrical and optical features of metal and semiconductor NCs for designing future RRAM and artificial synapses. RESULTS: It was demonstrated that doping NCs in the function layer of RRAM could not only improve the homogeneity of SET/RESET voltage but also reduce the threshold voltage. At the same time, it could still increase the retention time and provide the probability of mimicking the bio-synapse. CONCLUSION: NC doping can significantly enhance the overall performance of RM devices, but there are still many problems to be solved. This review highlights the relevance of NCs for RM and artificial synapses and also provides a perspective on the opportunities, challenges, and potential future directions.

Organic Mixed Ionic-Electronic Conductors for Bioelectronic Sensors: Materials and Operation Mechanisms.

The field of organic mixed ionic-electronic conductors (OMIECs) has gained significant attention due to their ability to transport both electrons and ions, making them promising candidates for various applications. Initially focused on inorganic materials, the exploration of mixed conduction has expanded to organic materials, especially polymers, owing to their advantages such as solution processability, flexibility, and property tunability. OMIECs, particularly in the form of polymers, possess both electronic and ionic transport functionalities. This review provides an overview of OMIECs in various aspects covering mechanisms of charge transport including electronic transport, ionic transport, and ionic-electronic coupling, as well as conducting/semiconducting conjugated polymers and their applications in organic bioelectronics, including (multi)sensors, neuromorphic devices, and electrochromic devices. OMIECs show promise in organic bioelectronics due to their compatibility with biological systems and the ability to modulate electronic conduction and ionic transport, resembling the principles of biological systems. Organic electrochemical transistors (OECTs) based on OMIECs offer significant potential for bioelectronic applications, responding to external stimuli through modulation of ionic transport. An in-depth review of recent research achievements in organic bioelectronic applications using OMIECs, categorized based on physical and chemical stimuli as well as neuromorphic devices and circuit applications, is presented.

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.

Ionic Transistors.

Biological voltage-gated ion channels, which behave as life's transistors, regulate ion transport precisely and selectively through atomic-scale selectivity filters to sustain important life activities. By this inspiration, voltage-adaptable ionic transistors that use ions as signal carriers may provide an alternative information processing unit beyond solid-state electronic devices. This review provides a comprehensive overview of the first generation of biomimetic ionic transistors, including their operating mechanisms, device architecture development, and property characterizations. Despite its infancy, significant progress has been made in the applications of ionic transistors in fields such as DNA detection, drug delivery, and ionic circuits. Challenges and prospects of full exploitation of ionic transistors for a broad spectrum of practical applications are also discussed.

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive Devices.

Solid-state devices made from correlated oxides, such as perovskite nickelates, are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando infrared nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO3, H-NNO), devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying conductivity perpendicular to the applied field, which define the macroscale electrical characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with density functional theory calculations unveils a real-space map of multiple vibrational states of H-NNO associated with OH stretching modes and their dependence on the dopant concentration. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in situ X-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential for the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function.

Electro-Thermal Characterization of Dynamical VO2 Memristors via Local Activity Modeling.

Translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO2 /SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.

Advances in the Application of Perovskite Materials.

Nowadays, the soar of photovoltaic performance of perovskite solar cells has set off a fever in the study of metal halide perovskite materials. The excellent optoelectronic properties and defect tolerance feature allow metal halide perovskite to be employed in a wide variety of applications. This article provides a holistic review over the current progress and future prospects of metal halide perovskite materials in representative promising applications, including traditional optoelectronic devices (solar cells, light-emitting diodes, photodetectors, lasers), and cutting-edge technologies in terms of neuromorphic devices (artificial synapses and memristors) and pressure-induced emission. This review highlights the fundamentals, the current progress and the remaining challenges for each application, aiming to provide a comprehensive overview of the development status and a navigation of future research for metal halide perovskite materials and devices.

Toward the Next Generation of Neural Iontronic Interfaces.

Neural interfaces are evolving at a rapid pace owing to advances in material science and fabrication, reduced cost of scalable complementary metal oxide semiconductor (CMOS) technologies, and highly interdisciplinary teams of researchers and engineers that span a large range from basic to applied and clinical sciences. This study outlines currently established technologies, defined as instruments and biological study systems that are routinely used in neuroscientific research. After identifying the shortcomings of current technologies, such as a lack of biocompatibility, topological optimization, low bandwidth, and lack of transparency, it maps out promising directions along which progress should be made to achieve the next generation of symbiotic and intelligent neural interfaces. Lastly, it proposes novel applications that can be achieved by these developments, ranging from the understanding and reproduction of synaptic learning to live-long multimodal measurements to monitor and treat various neuronal disorders.