Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability

A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing

With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased. Neuromorphic computing, an emerging computational paradigm inspired by the human brain, has garnered growing attention as a promising research frontier. Inspired by the human brain's functionality, this technology mimics the behavior of neurons and synapses to enable efficient, low-power computing. Unlike conventional digital systems, this approach offers a potentially superior alternative. This article delves into the application of nanowire materials (and devices) in neuromorphic computing simulations: First, it introduces the synthesis and preparation methods of nanowire materials. Then, it analyzes in detail the key role of nanowire devices in constructing artificial neural networks, especially their advantages in simulating the functions of neurons and synapses. Compared with traditional silicon-based material devices, it focuses on how nanowire devices can achieve higher connection density and lower energy consumption, thereby enabling new types of neuromorphic computing. Finally, it looks forward to the application potential of nanowire devices in the field of future neuromorphic computing, expecting them to become a key force in promoting the development of intelligent computing, with extensive application prospects in the fields of informatics and medicine.

3D Vector Piezoresponse Imaging with Interferometric Atomic Force Microscopy

Forces acting between an Atomic Force Microscope (AFM) tip and sample are 3D. Despite this, most AFM force measurements are confined to one or two dimensions. Extending AFM force measurements into 3D has previously required complex, difficult, and time-consuming workflows. Here, an accurate, interferometric method for quantifying the full, 3D response of an AFM tip is demonstrated to localized forces. This approach is demonstrated on a series of piezoelectric materials and show that this approach yields quantitative 3D measurement independent of the sample orientation beneath the tip. This approach simplifies existing, angle-resolved piezoresponse force microscopy (PFM) techniques. These measurements benefit from the greatly reduced noise floor (≈ 5 fm / Hz $ \approx\!\!5{\mathrm{fm}}/\sqrt {{\mathrm{Hz}}} $ ) and intrinsic accuracy of the interferometric measurements. One important result is that the vertical piezo sensitivity (deff,z, units of pm/V) is systematically 2-3x larger than the in-plane piezo sensitivities (deff,lat). A simple analysis of vertical and lateral contact stiffnesses, due to the difference in the Young (vertical) and Shear (lateral) sample moduli dtheory,z/dtheory,lat ≈2.5, in good agreement with the measurements. While this work is confined to ferroelectric materials, it provides a general workflow and framework for other AFM-based mechanical measurements.

Unraveling the Interplay Between Memristive and Magnetoresistive Behaviors in LaCoO3/SrTiO3 Superlattice-Based Neural Synaptic Devices

Memristors and magnetic tunnel junctions are showing great potential in data storage and computing applications. A magnetoelectrically coupled memristor utilizing electron spin and electric field-induced ion migration can facilitate their operation, uncover new phenomena, and expand applications. In this study, devices consisting of Pt/(LaCoO3/SrTiO3)n/LaCoO3/Nb:SrTiO3 (Pt/(LCO/STO)n/LCO/NSTO) are engineered using pulsed laser deposition to form the LCO/STO superlattice layer, with Pt and NSTO serving as the top and bottom electrodes, respectively. The results show that both memristive and magnetoresistive properties can coexist without any compromise in performance, and the values of ROFF/RON and tunnel magnetoresistance (TMR) ratio are both improved by ≈1000% compared to a single-period heterostructure. Notably, the Pt/(LCO/STO)5/LCO/NSTO device demonstrates superior multilevel storage performance, characterized by extended endurance, reliable retention, high ROFF/RON ratio, significant TMR ratio, and fundamental synaptic behaviors. Furthermore, density functional theory (DFT) is employed to calculate the changes in oxygen vacancies, affecting the overall energy bands and magnetic moments in the monolayer and multi-periodic structures. Simulations using the handwritten digit recognition classification achieve the highest accuracy of 94.38%. These attributes suggest that the devices hold considerable promise for application in data storage and neuromorphic computing, offering a platform for high-density neural circuits in intelligent electronic devices.

Strategic Development of Memristors for Neuromorphic Systems: Low-Power and Reconfigurable Operation

The ongoing global energy crisis has heightened the demand for low-power electronic devices, driving interest in neuromorphic computing inspired by the parallel processing of human brains and energy efficiency. Reconfigurable memristors, which integrate both volatile and non-volatile behaviors within a single unit, offer a powerful solution for in-memory computing, addressing the von Neumann bottleneck that limits conventional computing architectures. These versatile devices combine the high density, low power consumption, and adaptability of memristors, positioning them as superior alternatives to traditional complementary metal-oxide-semiconductor (CMOS) technology for emulating brain-like functions. Despite their potential, studies on reconfigurable memristors remain sparse and are often limited to specific materials such as Mott insulators without fully addressing their unique reconfigurability. This review specifically focuses on reconfigurable memristors, examining their dual-mode operation, diverse physical mechanisms, structural designs, material properties, switching behaviors, and neuromorphic applications. It highlights the recent advancements in low-power-consumption solutions within memristor-based neural networks and critically evaluates the challenges in deploying reconfigurable memristors as standalone devices or within artificial neural systems. The review provides in-depth technical insights and quantitative benchmarks to guide the future development and implementation of reconfigurable memristors in low-power neuromorphic computing.

Low-Power Memristor for Neuromorphic Computing: From Materials to Applications

As an emerging memory device, memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption. This review paper focuses on the application of low-power-based memristors in various aspects. The concept and structure of memristor devices are introduced. The selection of functional materials for low-power memristors is discussed, including ion transport materials, phase change materials, magnetoresistive materials, and ferroelectric materials. Two common types of memristor arrays, 1T1R and 1S1R crossbar arrays are introduced, and physical diagrams of edge computing memristor chips are discussed in detail. Potential applications of low-power memristors in advanced multi-value storage, digital logic gates, and analogue neuromorphic computing are summarized. Furthermore, the future challenges and outlook of neuromorphic computing based on memristor are deeply discussed.

Advances in Metal Halide Perovskite Memristors: A Review from a Co-Design Perspective

The memristor has recently demonstrated considerable potential in the field of large-scale data information processing. Metal halide perovskites (MHPs) have emerged as the leading contenders for memristors due to their sensitive optoelectronic response, low power consumption, and ability to be prepared at low temperatures. This work presents a comprehensive enumeration and analysis of the predominant research advancements in mechanisms of resistance switch (RS) behaviors in MHPs-based memristors, along with a summary of useful characterization techniques. The impact of diverse optimization techniques on the functionality of perovskite memristors is examined and synthesized. Additionally, the potential of MHPs memristors in data processing, physical encryption devices, artificial synapses, and brain-like computing advancement of MHPs memristors is evaluated. This review can prove a valuable reference point for the future development of perovskite memristors applications. In conclusion, the current challenges and prospects of MHPs-based memristors are discussed in order to provide insights into potential avenues for the development of next-generation information storage technologies and biomimetic applications.

Robust Sodium Carboxymethyl Cellulose-Based Neuromorphic Device with High Biocompatibility Engineered through Molecular Polarization for the Emulation of Learning Behaviors in the Human Brain

Sodium carboxymethyl cellulose (CMC-Na), derived from natural cellulose and frequently employed as a biocompatible coating, thus renders it an ideal component for the construction of highly biocompatible neuromorphic devices aimed at biomachine interfaces. Here, an array of Mo/CMC-Na/ITO neuromorphic devices is fabricated, with CMC-Na serving as the functional layer. The devices exhibit capabilities to emulate various synaptic learning rules and demonstrate high endurance performance among biomaterial-based electronics, achieving stability over 2 × 104 pulses. Then, simulations of human brain-inspired learning and forgetting paradigms are conducted, highlighting the versatility of the device array in mimicking learning processes. Applications in pattern recognition leverage "learning-forgetting" paradigms, showcasing the potential of the device in cognitive tasks. Electrical measurements elucidate the mechanism of molecular polarization rotation, which offers insights into the modulation of synaptic weights within biocompatible biomaterial-based devices. Furthermore, the biocompatible properties of the devices are evaluated using human embryonic kidney 293 cells, confirming their excellent biocompatibility. The biodegradability of the devices is assessed by using physical transient tests to evaluate their sustainability in biomedical applications. Such advances represent pivotal improvements in implantable bioinspired electronics and show potential in biomachine interface and cognitive computing applications.

Recent Advancements in 2D Material-Based Memristor Technology Toward Neuromorphic Computing

Two-dimensional (2D) layered materials have recently gained significant attention and have been extensively studied for their potential applications in neuromorphic computing, where they are used to mimic the functions of the human brain. Their unique properties, including atomic-level thickness, exceptional mechanical stability, and tunable optical and electrical characteristics, make them highly versatile for a wide range of applications. In this review, we offer a comprehensive analysis of 2D material-based memristors. Furthermore, we examine the ability of 2D material-based memristors to successfully mimic the human brain by referencing their neuromorphic applications.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Biocompatible Acellular Dermal Matrix-Based Neuromorphic Device with Ultralow Voltage, Ion Channel Emulation, and Synaptic Forgetting Visualization Computation

Neuromorphic bioelectronics aim to integrate electronics with biological systems yet encounter challenges in biocompatibility, operating voltages, power consumption, and stability. This study presents biocompatible neuromorphic devices fabricated from acellular dermal matrix (ADM) derived from porcine dermis using low-temperature supercritical CO2 extraction. The ADM preserves the natural scaffold structure of collagen and minimizes immunogenicity by eliminating cells, fats, and noncollagenous impurities, ensuring excellent biocompatibility. The ADM-based devices emulate biological ion channels with biphasic membrane current modulation, exhibiting temperature dependency and pH sensitivity. It operates at an ultralow voltage of 1 mV and demonstrates reliable synaptic modulation exceeding 4 × 104 endurance cycles. The activation voltage can be theoretically as low as 59 μV, comparable to brainwave signals with a power of merely 7 aJ/event. Furthermore, a brain-like forgetting visualization algorithm is developed, leveraging the synaptic forgetting plasticity of ADM-based devices to achieve complex computing tasks in a highly energy-efficient manner. Neuromorphic devices based on ADM not only hold potential in implantable biointerfaces due to their exceptional biocompatibility, ultralow voltage, and power but also provide a feasible way for energy-efficient computing paradigms through a synergistic hardware-software approach.

Ultralow-Energy-Consumption Photosynaptic Transistor Utilizing Conjugated Polymers/Perovskite Quantum Dots Nanocomposites With Ligand Density Optimization

The photosynaptic transistor stands as a promising contender for overcoming the von Neumann bottleneck in the realm of photo-communication. In this context, photonic synaptic transistors is developed through a straightforward solution process, employing an organic semiconducting polymer with pendant-naphthalene-containing side chains (PDPPNA) in combination with ligand-density-engineered CsPbBr3 perovskite quantum dots (PQDs). This fabrication approach allows the devices to emulate fundamental synaptic behaviors, encompassing excitatory postsynaptic current, paired-pulse facilitation, the transition from short-to-long-term memory, and the concept of "learning experience." Notably, the phototransistor, incorporating the blend of the PDPPNA and CsPbBr3 PQDs washed with ethyl acetate, achieved an exceptional memory ratio of 104. Simultaneously, the same device exhibited an impressive paired-pulse facilitation ratio of 223% at a moderate operating voltage of -4 V and an extraordinarily low energy consumption of 0.215 aJ at an ultralow operating voltage of -0.1 mV. Consequently, these low-voltage synaptic devices, constructed with a pendant side-chain engineering of organic semiconductors and a ligand density engineering of PQDs through a simple fabrication process, exhibit substantial potential for replicating the visual memory capabilities of the human brain.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ activation

Affective computing, representing the forefront of human-machine interaction, is confronted with the pressing challenges of the execution speed and power consumption brought by the transmission of massive data. Herein, we introduce a bionic organic memristor inspired by the ligand-gated ion channels (LGICs) to facilitate near-sensor affective computing based on electroencephalography (EEG). It is constructed from a coordination polymer comprising Co ions and benzothiadiazole (Co-BTA), featuring multiple switching sites for redox reactions. Through advanced characterizations and theoretical calculations, we demonstrate that when subjected to a bias voltage, only the site where Co ions bind with N atoms from four BTA molecules becomes activated, while others remain inert. This remarkable phenomenon resembles the selective in situ activation of LGICs on the postsynaptic membrane for neural signal regulation. Consequently, the bionic organic memristor network exhibits outstanding reliability (200 000 cycles), exceptional integration level (210 pixels), ultra-low energy consumption (4.05 pJ), and fast switching speed (94 ns). Moreover, the built near-sensor system based on it achieves emotion recognition with an accuracy exceeding 95%. This research substantively adds to the ambition of realizing empathetic interaction and presents an appealing bionic approach for the development of novel electronic devices.

Recent advances in flexible memristors for advanced computing and sensing

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.

Three-Dimensional Integrated Synaptic Devices Based on a Silver-Cluster Conduction Mechanism with High Thermostability

During the operation of synaptic devices based on traditional conductive filament (CF) models, the formation and dissolution of CFs are usually uncertain. Moreover, when the device is operated for a long time, the CFs may dissolve due to both the Joule heat generated by the device itself and the thermal coupling between the devices. These problems seriously reduce the reliability and stability of the synaptic device. Here, an artificial synapse device based on polyimide-molybdenum disulfide quantum dot (MoS2 QD) nanocomposites is presented. Research has shown that MoS2 QDs doped into the active layer can effectively induce the reduction of Ag ions into Ag atoms, leading to the formation of Ag clusters and thereby achieving control over the growth of the CFs. Therefore, the device is capable of stably realizing various basic synaptic functions. Moreover, the long-term potentiation/long-term depression (LTP/LTD) of this device shows good linearity. In addition, due to the change in the shape of the CFs, the highly integrated devices with a three-dimensional (3D) stacked structure can operate normally even in a high-temperature environment of 110 °C. Finally, the synaptic characteristics of the devices on learning and inference tests show that their recognition rates are approximately 90.75% (room temperature) and 90.63% (110 °C).

A tunable multi-timescale Indium-Gallium-Zinc-Oxide thin-film transistor neuron towards hybrid solutions for spiking neuromorphic applications

Spiking neural network algorithms require fine-tuned neuromorphic hardware to increase their effectiveness. Such hardware, mainly digital, is typically built on mature silicon nodes. Future artificial intelligence applications will demand the execution of tasks with increasing complexity and over timescales spanning several decades. The multi-timescale requirements for certain tasks cannot be attained effectively enough through the existing silicon-based solutions. Indium-Gallium-Zinc-Oxide thin-film transistors can alleviate the timescale-related shortcomings of silicon platforms thanks to their bellow atto-ampere leakage currents. These small currents enable wide timescale ranges, far beyond what has been feasible through various emerging technologies. Here we have estimated and exploited these low leakage currents to create a multi-timescale neuron that integrates information spanning a range of 7 orders of magnitude and assessed its advantages in larger networks. The multi-timescale ability of this neuron can be utilized together with silicon to create hybrid spiking neural networks capable of effectively executing more complex tasks than their single-technology counterparts.

Centimeter-Scale Tellurium Oxide Films for Artificial Optoelectronic Synapses with Broadband Responsiveness and Mechanical Flexibility

Prevailing over the bottleneck of von Neumann computing has been significant attention due to the inevitableness of proceeding through enormous data volumes in current digital technologies. Inspired by the human brain's operational principle, the artificial synapse of neuromorphic computing has been explored as an emerging solution. Especially, the optoelectronic synapse is of growing interest as vision is an essential source of information in which dealing with optical stimuli is vital. Herein, flexible optoelectronic synaptic devices composed of centimeter-scale tellurium dioxide (TeO2) films detecting and exhibiting synaptic characteristics to broadband wavelengths are presented. The TeO2-based flexible devices demonstrate a comprehensive set of emulating basic optoelectronic synaptic characteristics; i.e., excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), conversion of short-term to long-term memory, and learning/forgetting. Furthermore, they feature linear and symmetric conductance synaptic weight updates at various wavelengths, which are applicable to broadband neuromorphic computations. Based on this large set of synaptic attributes, a variety of applications such as logistic functions or deep learning and image recognition as well as learning simulations are demonstrated. This work proposes a significant milestone of wafer-scale metal oxide semiconductor-based artificial synapses solely utilizing their optoelectronic features and mechanical flexibility, which is attractive toward scaled-up neuromorphic architectures.

Conformational transitions in redissolved silk fibroin films and application for printable self-powered multistate resistive memory biomaterials

3D printing of water stable proteins with elastic properties offers a broad range of applications including self-powered biomedical devices driven by piezoelectric biomaterials. Here, we present a study on water-soluble silk fibroin (SF) films. These films were prepared by mixing degummed silk fibers and calcium chloride (CaCl2) in formic acid, resulting in a silk I-like conformation, which was then converted into silk II by redissolving in phosphate buffer (PBS). Circular dichroism, Raman and infrared (IR) spectroscopies were used to investigate the transitions of secondary structure in silk I and silk II as the pH of the solvent and the sonication time were changed. We showed that a solvent with low pH (e.g. 4) maintains the silk I β-turn structure; in contrast solvent with higher pH (e.g. 7.4) promotes β-sheet features of silk II. Ultrasonic treatment facilitates the transition to water stable silk II only for the SF redissolved in PBS. SF from pH 7.4 solution has been printed using extrusion-based 3D printing. A self-powered memristor was realized, comprising an SF-based electric generator and an SF 3D-printed memristive unit connected in series. By exploiting the piezoelectric properties of silk II with higher β-sheet content and Ca2+ ion transport phenomena, the application of an input voltage driven by a SF generator to SF 3D printed holey structures induces a variation from an initial low resistance state (LRS) to a high resistance state (HRS) that recovers in a few minutes, mimicking the transient memory, also known as short-term memory. Thanks to this holistic approach, these findings can contribute to the development of self-powered neuromorphic networks based on biomaterials with memory capabilities.

Gate-Tunable Positive and Negative Photoconductance in Near-Infrared Organic Heterostructures for In-Sensor Computing

The rapid growth of sensor data in the artificial intelligence often causes significant reductions in processing speed and power efficiency. Addressing this challenge, in-sensor computing is introduced as an advanced sensor architecture that simultaneously senses, memorizes, and processes images at the sensor level. However, this is rarely reported for organic semiconductors that possess inherent flexibility and tunable bandgap. Herein, an organic heterostructure that exhibits a robust photoresponse to near-infrared (NIR) light is introduced, making it ideal for in-sensor computing applications. This heterostructure, consisting of partially overlapping p-type and n-type organic thin films, is compatible with conventional photolithography techniques, allowing for high integration density of up to 520 devices cm-2 with a 5 µm channel length. Importantly, by modulating gate voltage, both positive and negative photoresponses to NIR light (1050 nm) are attained, which establishes a linear correlation between responsivity and gate voltage and consequently enables real-time matrix multiplication within the sensor. As a result, this organic heterostructure facilitates efficient and precise NIR in-sensor computing, including image processing and nondestructive reading and classification, achieving a recognition accuracy of 97.06%. This work serves as a foundation for the development of reconfigurable and multifunctional NIR neuromorphic vision systems.

Visualized in-sensor computing

In artificial nervous systems, conductivity changes indicate synaptic weight updates, but they provide limited information compared to living organisms. We present the pioneering design and production of an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for in-sensor computing. Here, we engineer a specialized mechanism for adaptively regulating ion doping through an ion-exchange membrane, enabling precise control over color-coded synaptic weight, an unprecedented achievement. The electrochromic neuromorphic transistor not only enhances electrochromatic capabilities for hardware coding but also establishes a visualized pattern-recognition network. Integrating the electrochromic neuromorphic transistor with an artificial whisker, we simulate a bionic reflex system inspired by the longicorn beetle, achieving real-time visualization of signal flow within the reflex arc in response to environmental stimuli. This research holds promise in extending the biomimetic coding paradigm and advancing the development of bio-hybrid interfaces, particularly in incorporating color-based expressions.

Recent advancements in implantable neural links based on organic synaptic transistors

The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.

3D-integrated multilayered physical reservoir array for learning and forecasting time-series information

A wide reservoir computing system is an advanced architecture composed of multiple reservoir layers in parallel, which enables more complex and diverse internal dynamics for multiple time-series information processing. However, its hardware implementation has not yet been realized due to the lack of a high-performance physical reservoir and the complexity of fabricating multiple stacks. Here, we achieve a proof-of-principle demonstration of such hardware made of a multilayered three-dimensional stacked 3 × 10 × 10 tungsten oxide memristive crossbar array, with which we further realize a wide physical reservoir computing for efficient learning and forecasting of multiple time-series data. Because a three-layer structure allows the seamless and effective extraction of intricate three-dimensional local features produced by various temporal inputs, it can readily outperform two-dimensional based approaches extensively studied previously. Our demonstration paves the way for wide physical reservoir computing systems capable of efficiently processing multiple dynamic time-series information.

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.

Highly Reliable Performance of Flexible Synaptic Devices Based on PVP-GO QD Nanocomposites Due to the Formation of Directional Filaments

The metallic conductive filament (CF) model, which serves as an important conduction mechanism for realizing synaptic functions in electronic devices, has gained recognition and is the subject of extensive research. However, the formation of CFs within the active layer is plagued by issues such as uncontrolled and random growth, which severely impacts the stability of the devices. Therefore, controlling the growth of CFs and improving the performance of the devices have become the focus of that research. Herein, a synaptic device based on polyvinylpyrrolidone (PVP)/graphene oxide quantum dot (GO QD) nanocomposites is proposed. Doping GO QDs in the PVP provides a large number of active centers for the reduction of silver ions, which allows, to a certain extent, the growth of CFs to be controlled. Because of this, the proposed device can simulate a variety of synaptic functions, including the transition from long-term potentiation to long-term depression, paired-pulse facilitation, post-tetanic potentiation, transition from short-term memory to long-term memory, and the behavior of the "learning experience". Furthermore, after being bent repeatedly, the devices were still able to simulate multiple synaptic functions accurately. Finally, the devices achieved a high recognition accuracy rate of 89.39% in the learning and inference tests, producing clear digit classification results.

Reliable synaptic plasticity of InGaZnO transistor with TiO2interlayer

We demonstrate an InGaZnO (IGZO)-based synaptic transistor with a TiO2buffer layer. The structure of the synaptic transistor with TiO2inserted between the Ti metal electrode and an IGZO semiconductor channel O2trapping layer produces a large hysteresis window, which is crucial for achieving synaptic functionality. The Ti/TiO2/IGZO synaptic transistor exhibits reliable synaptic plasticity features such as excitatory post-synaptic current, paired-pulse facilitation, and potentiation and depression, originating from the reversible charge trapping and detrapping in the TiO2layer. Finally, the pattern recognition accuracy of Modified National Institute of Standards and Technology handwritten digit images was modeled using CrossSim simulation software. The simulation results present a high image recognition accuracy of ∼89%. Therefore, this simple approach using an oxide buffer layer can aid the implementation of high-performance synaptic devices for neuromorphic computing systems.

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.

Reliable Memristive Synapses Based on Parylene-MoOx Nanocomposites for Neuromorphic Applications

Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoOx (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.

Biodegradable Oxide Neuromorphic Transistors for Neuromorphic Computing and Anxiety Disorder Emulation

Brain-inspired neuromorphic computing and portable intelligent electronic products have received increasing attention. In the present work, nanocellulose-gated indium tin oxide neuromorphic transistors are fabricated. The device exhibits good electrical performance. Short-term synaptic plasticities were mimicked, including excitatory postsynaptic current, paired-pulse facilitation, and dynamic high-pass synaptic filtering. Interestingly, an effective linear synaptic weight updating strategy was adopted, resulting in an excellent recognition accuracy of ∼92.93% for the Modified National Institute of Standard and Technology database adopting a two-layer multilayer perceptron neural network. Moreover, with unique interfacial protonic coupling, anxiety disorder behavior was conceptually emulated, exhibiting "neurosensitization", "primary and secondary fear", and "fear-adrenaline secretion-exacerbated fear". Finally, the neuromorphic transistors could be dissolved in water, demonstrating potential in "green" electronics. These findings indicate that the proposed oxide neuromorphic transistors would have potential as implantable chips for nerve health diagnosis, neural prostheses, and brain-machine interfaces.

Trainable Bilingual Synaptic Functions in Bio-enabled Synaptic Transistors

The signal transmission of the nervous system is regulated by neurotransmitters. Depending on the type of neurotransmitter released by presynaptic neurons, neuron cells can either be excited or inhibited. Maintaining a balance between excitatory and inhibitory synaptic responses is crucial for the nervous system's versatility, elasticity, and ability to perform parallel computing. On the way to mimic the brain's versatility and plasticity traits, creating a preprogrammed balance between excitatory and inhibitory responses is required. Despite substantial efforts to investigate the balancing of the nervous system, a complex circuit configuration has been suggested to simulate the interaction between excitatory and inhibitory synapses. As a meaningful approach, an optoelectronic synapse for balancing the excitatory and inhibitory responses assisted by light mediation is proposed here by deploying humidity-sensitive chiral nematic phases of known polysaccharide cellulose nanocrystals. The environment-induced pitch tuning changes the polarization of the helicoidal organization, affording different hysteresis effects with the subsequent excitatory and inhibitory nonvolatile behavior in the bio-electrolyte-gated transistors. By applying voltage pulses combined with stimulation of chiral light, the artificial optoelectronic synapse tunes not only synaptic functions but also learning pathways and color recognition. These multifunctional bio-based synaptic field-effect transistors exhibit potential for enhanced parallel neuromorphic computing and robot vision technology.

Polymeric Memristor Based Artificial Synapses with Ultra-Wide Operating Temperature

Neuromorphic electronics, being inspired by how the brain works, hold great promise to the successful implementation of smart artificial systems. Among several neuromorphic hardware issues, a robust device functionality under extreme temperature is of particular importance for practical applications. Given that the organic memristors for artificial synapse applications are demonstrated under room temperature, achieving a robust device performance at extremely low or high temperature is still utterly challenging. In this work, the temperature issue is addressed by tuning the functionality of the solution-based organic polymeric memristor. The optimized memristor demonstrates a reliable performance under both the cryogenic and high-temperature environments. The unencapsulated organic polymeric memristor shows a robust memristive response under test temperature ranging from 77 to 573 K. Utilizing X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary-ion mass spectrometry (ToF-SIMS) depth profiling, the device working mechanism is unveiled by comparing the compositional profiles of the fresh and written organic polymeric memristors. A reversible ion migration induced by an applied voltage contributes to the characteristic switching behavior of the memristor. Herein, both the robust memristive response achieved at extreme temperatures and the verified device working mechanism will remarkably accelerate the development of memristors in neuromorphic systems.

Biocompatible memristive device based on an agarose@gold nanoparticle-nanocomposite layer obtained from nature for neuromorphic computing

Natural, organic, materials-based artificial synaptic devices have been in the spotlight for wearable/flexible devices due to their lightweight, biocompatibility, and scalability. In this study, an electronic memristive device based on agarose extracted from plants in the Rhodophyceae class was fabricated, and its memory characteristics and analog data processing capabilities were evaluated. The Al/agarose@gold nanoparticle (AuNP) film/indium-tin-oxide (ITO)-structured memristive device exhibited reliable resistive switching characteristics with excellent retention with a large Ron/Roff ratio of 10⁴. Also, analog conductance changes in our device were achieved with power consumption at the pJ level. This notable behavior could be maintained under mechanical deformations from a flat to a 4-mm bent state. In the recognition simulation based on the device's performance, an 91% accuracy and clear digit classification were achieved.

Optimized coaxial focused electrohydrodynamic jet printing of highly ordered semiconductor sub-microwire arrays for high-performance organic field-effect transistors

Patterning of semiconductor polymers is pertinent to preparing and applying organic field-effect transistors (OFETs). In this study, coaxial focused electrohydrodynamic jet printing (high resolution, high speed, and convenient) was used to pattern polymer semiconductors. The influence of the key printing parameters on the width of polymer sub-microwires was evaluated. The width decreased with increasing applied voltage, printing speed, and concentration of the polymer ink. However, the width increased gradually with increasing polymer ink flow rate. A regression analysis model of the relationship between the printing parameters and width was established. Based on a regression analysis/genetic algorithm, the optimal printing parameters were obtained and the correctness of the printing parameters was verified. The optimized printing parameters stabilized the width of the arrays to ca. 110 nm and imparted a smooth morphology. Additionally, the corresponding OFETs exhibited a high mobility of 2 cm² V^-1 s^-1, which is 5× higher than that of thin-film-based OFETs. One can conveniently obtain high-performance OFETs from ordered sub-microwire arrays fabricated by CFEJ printing.

Nanoscale molecular rectifiers

The use of molecules bridged between two electrodes as a stable rectifier is an important goal in molecular electronics. Until recently, however, and despite extensive experimental and theoretical work, many aspects of our fundamental understanding and practical challenges have remained unresolved and prevented the realization of such devices. Recent advances in custom-designed molecular systems with rectification ratios exceeding 10⁵ have now made these systems potentially competitive with existing silicon-based devices. Here, we provide an overview and critical analysis of recent progress in molecular rectification within single molecules, self-assembled monolayers, molecular multilayers, heterostructures, and metal-organic frameworks and coordination polymers. Examples of conceptually important and best-performing systems are discussed, alongside their rectification mechanisms. We present an outlook for the field, as well as prospects for the commercialization of molecular rectifiers.

Two-Dimensional Perovskite-Gated AlGaN/GaN High-Electron-Mobility-Transistor for Neuromorphic Vision Sensor

The extraordinary optoelectronic properties and continued commercialization of GaN enable it a promising component for neuromorphic visual system (NVS). However, typical GaN-based optoelectronic devices demonstrated to data only show temporary and unidirectional photoresponse in ultraviolet region, which is an insurmountable obstacle for construction of NVS in practical applications. Herein, an ultrasensitive visual sensor with phototransistor architecture consisting of AlGaN/GaN high-electron-mobility-transistor (HEMT) and two-dimensional Ruddlesden-Popper organic-inorganic halide perovskite (2D OIHP) is reported. Utilizing the significant variation in activation energy for ion transport in 2D OIHP (from 1.3 eV under dark to 0.4 eV under illumination), the sensor can efficiently perceive and storage optical information in ultraviolet-visible region. Meanwhile, the photo-enhanced field-effect mechanism in the depletion-mode HEMT enables gate-tunable negative and positive photoresponse, where some typical optoelectronic synaptic functions including inhibitory and excitatory postsynaptic current as well as paired-pulse facilitation are demonstrated. More importantly, a NVS based on the proposed visual sensor array is constructed for achieving neuromorphic visual preprocessing with an improved color image recognition rate of 100%.

Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing

In order to imitate brain-inspired biological information processing systems, various neuromorphic computing devices have been proposed, most of which were prepared on rigid substrates and have energy consumption levels several orders of magnitude higher than those of biological synapses (∼10 fJ per spike). Herein, a new type of wearable organic ferroelectric artificial synapse is proposed, which has two modulation modes (optical and electrical modulation). Because of the high photosensitivity of organic semiconductors and the ultrafast polarization switching of ferroelectric materials, the synaptic device has an ultrafast operation speed of 30 ns and an ultralow power consumption of 0.0675 aJ per synaptic event. Under combined photoelectric modulation, the artificial synapse realizes associative learning. The proposed artificial synapse with ultralow power consumption demonstrates good synaptic plasticity under different bending strains. This provides new avenues for the construction of ultralow power artificial intelligence system and the development of future wearable devices.

A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing

Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiO_x ) memristor array devices are fabricated to be utilized as constituent device element in hardware neural network, representing passive matrix array structure enabling vector-matrix multiplication process between multisignal and trained synaptic weight. In particular, in situ convolutional neural network hardware system is designed and implemented using a multiple 25 × 25 TiO_x memristor arrays and the memristor device parameters are developed to bring global constant voltage programming scheme for entire cells in crossbar array without any voltage tuning peripheral circuit such as transistor. Moreover, the learning rate modulation during in situ hardware training process is successfully achieved due to superior TiO_x memristor performance such as threshold uniformity (≈2.7%), device yield (> 99%), repetitive stability (≈3000 spikes), low asymmetry value of ≈1.43, ambient stability (6 months), and nonlinear pulse response. The learning rate modulable fast-converging in situ training based on direct memristor operation shows five times less training iterations and reduces training energy compared to the conventional hardware in situ training at ≈95.2% of classification accuracy.

Neuro-inspired electronic skin for robots

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.

Ionic liquid multistate resistive switching characteristics in two terminal soft and flexible discrete channels for neuromorphic computing

By exploiting ion transport phenomena in a soft and flexible discrete channel, liquid material conductance can be controlled by using an electrical input signal, which results in analog neuromorphic behavior. This paper proposes an ionic liquid (IL) multistate resistive switching device capable of mimicking synapse analog behavior by using IL BMIM FeCL₄ and H₂O into the two ends of a discrete polydimethylsiloxane (PDMS) channel. The spike rate-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP) behavior are highly stable by modulating the input signal. Furthermore, the discrete channel device presents highly durable performance under mechanical bending and stretching. Using the obtained parameters from the proposed ionic liquid-based synaptic device, convolutional neural network simulation runs to an image recognition task, reaching an accuracy of 84%. The bending test of a device opens a new gateway for the future of soft and flexible brain-inspired neuromorphic computing systems for various shaped artificial intelligence applications.

Advances in materials and devices for mimicking sensory adaptation

Adaptive devices, which aim to adjust electrical behaviors autonomically to external stimuli, are considered to be attractive candidates for next-generation artificial perception systems. Compared with typical electronic devices with stable signal output, adaptive devices possess unique features in exhibiting dynamic fitness to varying environments. To meet this requirement, increasing efforts have been made focusing on developing new materials, functional interfaces and novel device geometry for sensory perception applications. In this review, we summarize the recent advances in materials and devices for mimicking sensory adaptation. Keeping this in mind, we first introduce the fundamentals of biological sensory adaptation. Thereafter, the recent progress in mimicking sensory adaptation, such as tactile and visual adaptive systems, is overviewed. Moreover, we suggest five strategies to construct adaptive devices. Finally, challenges and perspectives are proposed to highlight the directions that deserve focused attention in this flourishing field.

Dynamic Self-Rectifying Liquid Metal-Semiconductor Heterointerfaces: A Platform for Development of Bioinspired Afferent Systems

The assembly of geometrically complex and dynamically active liquid metal/semiconductor heterointerfaces has drawn extensive attention in multidimensional electronic systems. In this study the chemovoltaic driven reactions have enabled the microfluidity of hydrophobic galinstan into a three-dimensional (3D) semiconductor matrix. A dynamic heterointerface is developed between the atomically thin surface oxide of galinstan and the TiO₂-Ni interface. Upon the growth of Ga₂O₃ film at the Ga₂O₃-TiO₂ heterointerface, the partial reduction of the TiO₂ film was confirmed by material characterization techniques. The conductance imaging spectroscopy and electrical measurements are used to investigate the charge transfer at heterointerfaces. Concurrently, the dynamic conductance in artificial synaptic junctions is modulated to mimic the biofunctional communication characteristics of multipolar neurons, including slow and fast inhibitory and excitatory postsynaptic responses. The self-rectifying characteristics, femtojoule energy processing, tunable synaptic events, and notably the coordinated signal recognition are the main characteristics of this multisynaptic device. This novel 3D design of liquid metal-semiconductor structure opens up new opportunities for the development of bioinspired afferent systems. It further facilitates the realization of physical phenomena at liquid metal-semiconductor heterointerfaces.

Highly Self-Healable Write-Once-Read-Many-Times Devices Based on Polyvinylalcohol-Imidazole Modified Graphene Nanocomposites

Repetitious mechanical stress or external mechanical impact can damage wearable electronic devices, leading to serious degradations in their electrical performances, which limits their applications. Because self-healing would be an excellent solution to the above-mentioned issue, this paper presents a self-healable memory device based on a novel nanocomposite layer consisting of a polyvinyl alcohol matrix and imidazole-modified graphene quantum dots. The device exhibits reliable electrical performance over 600 cycles, and the electrical properties of the device are maintained without any failure under this bending stress. Further, it is confirmed that the damaged device can recover its original electric characteristics after the self-healing process. It is believed that such outstanding results will lead the way to the realization of future wearable electronic systems.

An Organic/Inorganic Nanomaterial and Nanocrystal Quantum Dots-Based Multi-Level Resistive Memory Device

A cadmium selenide/zinc sulfide (CdSe/ZnS) quantum dot (QD)-based multi-level memory device with the structure [ITO/PEDOT:PSS/QDs/ZnO/Al:Al₂O₃/QDs/Al] was fabricated via a spin-coating method used to deposit thin films. Two layers of QD thin films present in the device act as charge storage layers to form three distinct states. Zinc oxide (ZnO) and aluminum oxide (Al₂O₃) were added to prevent leakage. ZnO NPs provide orthogonality between the two QD layers, and a poly(3,4-ethylenedioxythio-phene): poly(styrenesulfonate) (PEDOT:PSS) thin film was formed for effective hole injection from the electrodes. The core/shell structure of the QDs provides the quantum well, which causes the trapping of injected charges. The resistance changes according to the charging and discharging of the QDs' trap site and, as a result, the current through the device also changes. There are two quantum wells, two current changes, and three stable states. The role of each thin film was confirmed through I-V curve analysis and the fabrication conditions of each thin film were optimized. The synthesized QDs and ZnO nanoparticles were evaluated via X-ray diffraction, transmission electron microscopy, and absorbance and photoluminescence spectroscopy. The measured write voltages of the fabricated device were at 1.8 and 2.4 V, and the erase voltages were -4.05 and -4.6 V. The on/off ratio at 0.5 V was 2.2 × 10³. The proposed memory device showed retention characteristics of ≥100 h and maintained the initial write/erase voltage even after 200 iterative operations.

Biocompatible artificial synapses based on a zein active layer obtained from maize for neuromorphic computing

Artificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

Synaptic Plasticity Powering Long-Afterglow Organic Light-Emitting Transistors

Long-lasting luminescence in optoelectronic devices is highly sought after for applications in optical data storage and display technology. While in light-emitting diodes this is achieved by exploiting long-afterglow organic materials as active components, such a strategy has never been pursued in light-emitting transistors, which are still rather unexplored and whose technological potential is yet to be demonstrated. Herein, the fabrication of long-afterglow organic light-emitting transistors (LAOLETs) is reported whose operation relies on an unprecedented strategy based on a photoinduced synaptic effect in an inorganic indium-gallium-zinc-oxide (IGZO) semiconducting channel layer, to power a persistent electroluminescence in organic light-emitting materials. Oxygen vacancies in the IGZO layer, produced by irradiation at λ = 312 nm, free electrons in excess yielding to a channel conductance increase. Due to the slow recombination kinetics of photogenerated electrons to oxygen vacancies in the channel layer, the organic material can be fueled by postsynaptic current and displays a long-lived light-emission (hundreds of seconds) after ceasing UV irradiation. As a proof-of-concept, the LAOLETs are integrated in active-matrix light-emitting arrays operating as visual UV sensors capable of long-lifetime green-light emission in the irradiated regions.

Near-Infrared Artificial Synapses for Artificial Sensory Neuron System

The computing based on artificial neuron network is expected to break through the von Neumann bottleneck of traditional computer, and to greatly improve the computing efficiency, displaying a broad prospect in the application of artificial visual system. In the specific structural layout, it is a common method to connect the discrete photodetector with the artificial neuron in series, which enhances the complexity of signal recognition, conversion and storage. In this work, organic small molecule IR-780 iodide is inserted into the memory device as both the charge trapping layer and near-infrared (NIR) photoresponsive film. Through electrical and optical regulation, artificial synaptic functions including short-term plasticity, long-term plasticity, and spike rate dependence are realized. In the established artificial sensory neuron system, NIR optical pulses can significantly improve the spiking rate. Moreover, the spiking neural networks are further constructed by simulation for handwritten digit classification. This research may contribute to the development of light driven neural robots, optical signal encryption, and neural computing.