Search

Copper‐based halides, a type of perovskite derivative, inherit their excellent optoelectronic properties and solution processability and also have the advantages of being nontoxic and stable. Despite the tremendous achievements of microfabrication in conventional halide perovskites, reports on copper‐based halide array devices are rare due to their difficult high‐quality crystallization. Here, by introducing single‐crystal thin films grown by space confinement method as substrates, reliable photolithography based on their single crystals is successfully achieved and controllable homoepitaxial single‐crystal arrays is demonstrated. By adjusting the substrate, angle, and growth time, the orientation, arrangement, and size of arrays could be controlled. Temperature and time are used to regulate the growth stage to obtain clean and appropriately sized arrays. ITO/Cs3Cu2I5/ITO neuromorphic device based on homoepitaxial arrays is developed. The variation of synaptic plasticity could be controlled by the number and frequency of light pulses. The device successfully simulated the process of “learning, forgetting, and relearning” in the human brain, demonstrating the potential of application in sensing‐storage‐computing‐integrated devices. [ABSTRACT FROM AUTHOR]

In this study, a hybrid organic‐inorganic field‐effect transistor (FET) is proposed with n‐type zinc‐tin oxide (ZTO) and p‐type dinaphtho[2,3‐b:2′,3′‐f] thieno[3,2‐b]thiophene (DNTT), presenting two applications: (1) random number generator (RNG) and (2) spiking neuron. Interface challenges lead to operational instabilities such as bias stress and hysteresis due to trap site formation from oxide surface hydroxyl groups. However, these trap sites are utilized to produce unstable noise for RNG. Also, the impact of an internal interlayer is explored to mitigate instability in the negative transconductance (NTC) effect. This interlayer enhances material compatibility, improving turn‐on voltage, on‐off current ratio, and reducing hysteresis in the FET. These improvements highlight and maximize the robustness of NTC characteristics. Utilizing this behavior, a spiking neuron is demonstrated that emulates neuronal spiking and generates neuronal spike signals. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Development of artificial opto‐electronic synaptic devices plays a crucial role for the practical implementation of energy‐efficient, parallel processing of human brain. In this article, two terminal inter‐digitated devices are fabricated on Gallium oxide (Ga2O3) thin films grown on sapphire substrates by pulsed laser deposition (PLD) technique to study its ability to mimic biological synaptic behaviors. The layers are found to exhibit long persistent photo‐conductivity (PPC) effect, which is identified to be the key parameter to replicate the behavior of biological synapses. Channel resistance and PPC time constants should also be optimized to improve the efficiency of response and energy consumption of synaptic devices. It has been observed that both conductivity and the PPC decay time of Ga2O3‐films can be controlled by varying oxygen pressure (∅O2)$( {{{\emptyset }_{{{O}_2}}}} )$ and growth temperature (TG). These devices demonstrate their ability to perform paired pulse facilitation (PPF) at very low applied bias in mV‐range. They can mimic biological synapses showing short‐to‐long‐term memory transition (STM‐to‐LTM) and learning‐forgetting behavior. One of these devices is found to show synaptic behavior with the energy consumption of as low as 71fJ electrical and 21nJ optical per synaptic event. These findings thus strengthen the candidature of Ga2O3 films for the development of next‐generation opto‐electronic neuromorphic devices and systems. [ABSTRACT FROM AUTHOR]

Despite the promise of superior efficiency and scalability, real‐world deployment of emerging nanoelectronic platforms for brain‐inspired computing have been limited thus far, primarily because of inter‐device variations and intrinsic non‐idealities. In this work, mitigation of these issues is demonstrated by performing learning directly on practical devices through a hardware‐in‐loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin–orbit torque heterostructures. The probabilistic switching and device‐to‐device variability of the fabricated devices of various sizes is characterized to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network‐level performance. The efficacy of the hardware‐in‐loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large‐scale implementations of neuromorphic hardware and realization of truly autonomous edge‐intelligent devices. [ABSTRACT FROM AUTHOR]

The concept of neuromorphic devices, aiming to process large amounts of information in parallel, at low power, high speed, and high efficiency, is to mimic the functions of human brain by emulating biological neural behavior. Optoelectronic neuromorphic devices are particularly suitable for neuromorphic applications with their ability to generate various pulses based on wavelength and to control synaptic stimulation. Each wavelength (ultraviolet, visible, and infrared) has specific advantages and optimal applications. Here, the heterostructure-based optoelectronic neuromorphic devices are explored across the full wavelength range (ultraviolet to infrared) by categorizing them on the basis of irradiated wavelength and structure (two-terminal and three-terminal) with respect to emerging optoelectrical materials. The relationship between neuromorphic applications, light wavelength, and mechanism is revisited. Finally, the potential and challenging aspects of next-generation optoelectronic neuromorphic devices are presented, which can assist in the design of suitable materials and structures for neuromorphic-based applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Ferroelectric materials are promising candidates for neuromorphic computing synaptic devices due to the nonvolatile multiplicity of spontaneous polarization. To ensure a sufficient memory window, ferroelectric materials with a large coercivity are urgently required for practical applications in highly scaled multi‐bit memory devices. Herein, a remarkable reliability of intermediate ferroelectric polarization states is demonstrated in a textured Al0.66Sc0.34N thin film with a coercive field of 2.4 MV cm−1. Al0.66Sc0.34N thin films are prepared at 300 °C on Pt (111)/Ti/SiO2/Si substrates using a radio frequency reactive sputtering method. Al0.66Sc0.34N thin films exhibit viable ferroelectricity with a large remanent polarization value of >100 µC cm−2. Through the conventional current–voltage characteristics, polarization switching kinetics, and temperature dependence of coercivity, the reproducibility of multiple polarization states with apparent accuracy is attributed to a small critical volume (3.7 × 10−28 m3) and a large activation energy (3.3 × 1027 eV m−3) for nucleation of the ferroelectric domain. This study demonstrates the potential of ferroelectric Al1‐xScxN for synaptic weight elements in neural network hardware. [ABSTRACT FROM AUTHOR]

Interconnecting nanodevices with the aim of assembling quantum computing architectures is one of the current outstanding challenges. At the nanoscale, the quantum interconnects become comparable in complexity with the active devices and should be treated on equal footing. In addition, they can play an active role in the switching properties. Here, we investigate the charge localization in neuromorphic bi-dimensional systems, which serve as quantum interconnects (QIs) between quantum dot registers. We define a device structure where, by manipulating the charging of a floating gate array, one defines the QI potential map, which can host a few interacting electrons. The ground state charge density may be extracted by measuring the tunneling current perpendicular to the device surface, yielding a convoluted image of the electron distribution. Using image-to-image translation methods, we achieve the mapping of the charge density from the confinement potential, as well as by deconvoluting the tunneling current map, which can be obtained by a direct measurement. Thus, we provide a proof-of-concept for a reconfigurable device, which can be used to design quantum many-electron devices. [ABSTRACT FROM AUTHOR]

Despite the promise of superior efficiency and scalability, real‐world deployment of emerging nanoelectronic platforms for brain‐inspired computing have been limited thus far, primarily because of inter‐device variations and intrinsic non‐idealities. In this work, mitigation of these issues is demonstrated by performing learning directly on practical devices through a hardware‐in‐loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin–orbit torque heterostructures. The probabilistic switching and device‐to‐device variability of the fabricated devices of various sizes is characterized to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network‐level performance. The efficacy of the hardware‐in‐loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large‐scale implementations of neuromorphic hardware and realization of truly autonomous edge‐intelligent devices.

Abstract 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.

Abstract 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.

Large, open‐gate transistors made from metal nanoparticle arrays offer possibilities to build new electronic devices, such as sensors. A nanoparticle necklace network (N3) of Au particles from 300 K to cryogenic temperatures exhibit a nonohmic I–Vd behavior, I ≈ (Vd–VT)ζ, where VT is a conduction gap and ζ is a constant critical exponent. The conduction gap in N3, made from disordered networks of 1D chains of 10 nm diameter Au particles exhibits room temperature (RT) gating. Although the I–Vd behavior at RT is identical to Coulomb blockade, the conduction is modulated by field‐assisted tunneling exhibiting classical critical behavior. In this study, based on three results, invariance of VT on gating, invariance of VT on temperature, and zero–bias conductance, a sharp transition temperature at ≈140 K is discovered where the conduction mechanism switches from Coulomb blockade to classical critical percolation behavior. The N3 architecture allows the reconciliation of the Coulomb blockade versus activation process as a sharp thermal transition to serve as a model system to study the exotic behavior in nanogranular‐metallic materials. The novel global critical behavior to local Coulomb blockade governed transition in these N3 architectures may potentially lead to novel sensors and biosensors. [ABSTRACT FROM AUTHOR]

In this work, we consider the possibility of using a mathematical model of spots for the representation of secondary mental imagery in psychology and imagery representation of information in AI, as well as for solving inverse problems, including application of a learning method. A basic mathematical apparatus has been developed for the presentation and processing of qualitative information about elementary spatial relations between spots. The proposed apparatus can be used to develop algorithms for neuromorphic systems based on new promising components of memristors and FeFETs, which have nonvolatile memory, extremely low switching losses and faster read/write operations. It provides information representation and processing in the imagery form. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Ferroelectric materials are promising candidates for neuromorphic computing synaptic devices due to the nonvolatile multiplicity of spontaneous polarization. To ensure a sufficient memory window, ferroelectric materials with a large coercivity are urgently required for practical applications in highly scaled multi‐bit memory devices. Herein, a remarkable reliability of intermediate ferroelectric polarization states is demonstrated in a textured Al0.66Sc0.34N thin film with a coercive field of 2.4 MV cm−1. Al0.66Sc0.34N thin films are prepared at 300 °C on Pt (111)/Ti/SiO2/Si substrates using a radio frequency reactive sputtering method. Al0.66Sc0.34N thin films exhibit viable ferroelectricity with a large remanent polarization value of >100 µC cm−2. Through the conventional current–voltage characteristics, polarization switching kinetics, and temperature dependence of coercivity, the reproducibility of multiple polarization states with apparent accuracy is attributed to a small critical volume (3.7 × 10−28 m3) and a large activation energy (3.3 × 1027 eV m−3) for nucleation of the ferroelectric domain. This study demonstrates the potential of ferroelectric Al1‐xScxN for synaptic weight elements in neural network hardware.

Abstract 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.

Highlights A comprehensive summary of the representative promising applications of metal halide perovskite materials, 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. For each application, the fundamentals of the field, the current progress and the remaining challenges are provided, based on the up-to-date works.

Abstract Large, open‐gate transistors made from metal nanoparticle arrays offer possibilities to build new electronic devices, such as sensors. A nanoparticle necklace network (N3) of Au particles from 300 K to cryogenic temperatures exhibit a nonohmic I–Vd behavior, I ≈ (Vd–VT)ζ, where VT is a conduction gap and ζ is a constant critical exponent. The conduction gap in N3, made from disordered networks of 1D chains of 10 nm diameter Au particles exhibits room temperature (RT) gating. Although the I–Vd behavior at RT is identical to Coulomb blockade, the conduction is modulated by field‐assisted tunneling exhibiting classical critical behavior. In this study, based on three results, invariance of VT on gating, invariance of VT on temperature, and zero–bias conductance, a sharp transition temperature at ≈140 K is discovered where the conduction mechanism switches from Coulomb blockade to classical critical percolation behavior. The N3 architecture allows the reconciliation of the Coulomb blockade versus activation process as a sharp thermal transition to serve as a model system to study the exotic behavior in nanogranular‐metallic materials. The novel global critical behavior to local Coulomb blockade governed transition in these N3 architectures may potentially lead to novel sensors and biosensors.

ABSTRACTWith 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.

In the realm of artificial intelligence, ultrahigh‐performance neuromorphic computing plays a significant role in executing multiple complex operations in parallel while adhering to a more biologically plausible model. Despite their importance, developing an artificial synaptic device to match the human brain's efficiency is an extremely complex task involving high energy consumption and poor parallel processing latency. Herein, a simple molecule, copper‐iodide‐based artificial synaptic device demonstrating core synaptic functions of human neural networks is introduced. Exceptionally high carrier mobility and dielectric constant in the developed device lead to superior efficacies in neuromorphic characteristics with ultrahigh paired‐pusle facilitation index (>195). The results demonstrate biomimetic capabilities that exert a direct influence on neural networks across multiple timescales, ranging from short‐ to long‐term memory. This flexible reconfiguration of neural excitability provided by the copper‐iodide‐based synaptic device positions it as a promising candidate for creating advanced artificial intelligence systems. [ABSTRACT FROM AUTHOR]

Neuromorphic hardware based on artificial synaptic devices has great potential to break the bottleneck of von Neumann architecture, which makes it possible to emulate the working mode of the human brain with low power consumption and high operation efficiency. However, current synaptic devices can barely detect photons and are bio‐incompatible for future all‐in‐one visual perception technology. Here, synaptic photoconductors based on an organic–inorganic hybrid structure, and composed of photosensitive bacteriorhodopsin protein layer and zinc oxide film are reported. The synaptic photoconductors demonstrate tunable synaptic plasticity with the modulation of the light illumination time and power intensity. The working mechanism of the photogating effect induced by the proton pump process of bR protein molecules is further investigated in detail. Assisting with these properties, the imaging memorization and preprocessing function are successfully emulated by the synaptic photoconductors. The prototype photosynaptic devices provide a unique opportunity to realize artificial synapses, enabling neuromorphic hardware. [ABSTRACT FROM AUTHOR]

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning‐relearning‐forgetting stages is demonstrated. Critically, to emulate the real‐time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision‐making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next‐gen neuromorphic computing for the development of intelligent machines and humanoids. [ABSTRACT FROM AUTHOR]

Abstract Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning‐relearning‐forgetting stages is demonstrated. Critically, to emulate the real‐time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision‐making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next‐gen neuromorphic computing for the development of intelligent machines and humanoids.

Highlights: A comprehensive summary of the representative promising applications of metal halide perovskite materials, 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. For each application, the fundamentals of the field, the current progress and the remaining challenges are provided, based on the up-to-date works. 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. [ABSTRACT FROM AUTHOR]

This article summarizes the recent developments in organic neuromorphic devices, mainly resistive switches (memristors) and organic electrochemical transistors. The materials for the active layers and device architectures are summarized to highlight particular aspects of organic devices, including flexibility, biocompatibility and facile fabrication processes. © 2023 Society of Industrial Chemistry. [ABSTRACT FROM AUTHOR]

Neuromorphic electronics has demonstrated great promise in mimicking the sensory and memory functions of biological systems. However, synaptic devices with desirable sensitivity, selectivity, and operational voltage imitating the olfactory system have rarely been reported. Here, a flexible and biomimetic olfactory synapse based on an organic electrochemical transistor (OECT) coupled with a breath‐figure derived porous solid polymer electrolyte (SPE) is proposed. The device demonstrates excellent sensitivity with a ppb‐level response limit and desirable selectivity toward hydrogen sulfide (H2S) over other gases, and successfully achieves wireless real‐time detection of excessive concentration of H2S from rotten eggs. H2S‐mediated synaptic plasticity is accomplished with the device and typical synaptic behaviors are realized, including short‐term memory (STM), long‐term memory (LTM), transition from STM to LTM, etc., enabling the imitation of potential cumulative damages upon H2S exposure. The proposed device paves new ways toward next‐generation olfactory systems capable of sensing and memorizing functionalities mimicking neurobiological systems, offering critical materials strategies to accomplish intelligent artificial sensory systems. [ABSTRACT FROM AUTHOR]

Neuromorphic systems can parallelize the perception and computation of information, making it possible to break through the von Neumann bottleneck. Neuromorphic engineering has been developed over a long period of time based on Hebbian learning rules. The optoelectronic neuromorphic analog device combines the advantages of electricity and optics, and can simulate the biological visual system, which has a very strong development potential. Low‐dimensional materials play a very important role in the field of optoelectronic neuromorphic devices due to their flexible bandgap tuning mechanism and strong light‐matter coupling efficiency. This review introduces the basic synaptic plasticity of neuromorphic devices. According to the different number of terminals, two‐terminal neuromorphic memristors, three‐terminal neuromorphic transistors and artificial visual system are introduced from the aspects of the action mechanism and device structure. Finally, the development prospect of optoelectronic neuromorphic analog devices based on low‐dimensional materials is prospected. [ABSTRACT FROM AUTHOR]

Rather than generating a photocurrent through photo-excited carriers by the photoelectric effect, the photogating effect enables us to detect sub-bandgap rays. The photogating effect is caused by trapped photo-induced charges that modulate the potential energy of the semiconductor/dielectric interface, where these trapped charges contribute an additional electrical gating-field, resulting in a shift in the threshold voltage. This approach clearly separates the drain current in dark versus bright exposures. In this review, we discuss the photogating effect-driven photodetectors with respect to emerging optoelectrical materials, device structures, and mechanisms. Representative examples that reported the photogating effect-based sub-bandgap photodetection are revisited. Furthermore, emerging applications using these photogating effects are highlighted. The potential and challenging aspects of next-generation photodetector devices are presented with an emphasis on the photogating effect. [ABSTRACT FROM AUTHOR]

With the arrival of the era of artificial intelligence (AI) and big data, the explosive growth of data has raised higher demands on computer hardware and systems. Neuromorphic techniques inspired by biological nervous systems are expected to be one of the approaches to breaking the von Neumann bottleneck. Piezotronic neuromorphic devices modulate electrical transport characteristics by piezopotential and directly associate external mechanical motion with electrical output signals in an active manner, with the capability to sense/store/process information of external stimuli. In this review, we have presented the piezotronic neuromorphic devices (which are classified into strain-gated piezotronic transistors and piezoelectric nanogenerator-gated field effect transistors based on device structure) and discussed their operating mechanisms and related manufacture techniques. Secondly, we summarized the research progress of piezotronic neuromorphic devices in recent years and provided a detailed discussion on multifunctional applications, including bionic sensing, information storage, logic computing, and electrical/optical artificial synapses. Finally, in the context of future development, challenges, and perspectives, we have discussed how to modulate novel neuromorphic devices with piezotronic effects more effectively. It is believed that the piezotronic neuromorphic devices have great potential for the next generation of interactive sensation/memory/computation to facilitate the development of the Internet of Things, AI, biomedical engineering, etc.

Interconnecting nanodevices with the aim of assembling quantum computing architectures is one of the current outstanding challenges. At the nanoscale, the quantum interconnects become comparable in complexity with the active devices and should be treated on equal footing. In addition, they can play an active role in the switching properties. Here, we investigate the charge localization in neuromorphic bi-dimensional systems, which serve as quantum interconnects (QIs) between quantum dot registers. We define a device structure where, by manipulating the charging of a floating gate array, one defines the QI potential map, which can host a few interacting electrons. The ground state charge density may be extracted by measuring the tunneling current perpendicular to the device surface, yielding a convoluted image of the electron distribution. Using image-to-image translation methods, we achieve the mapping of the charge density from the confinement potential, as well as by deconvoluting the tunneling current map, which can be obtained by a direct measurement. Thus, we provide a proof-of-concept for a reconfigurable device, which can be used to design quantum many-electron devices.

Electrical leakage is one of the fatal faults associated with thin‐film devices (TFDs) including organic light‐emitting devices (OLEDs). To prevent electrical leakage, highly smooth electrodes are required to avoid the concentration of the electric field at local points. However, this requires the use of expensive electrode substrates. Here, OLEDs using inexpensive, aluminum‐foil‐based electrode substrates with an extremely high surface roughness are fabricated. A low leakage current is realized by incorporating extraordinary thick buffer layers of phosphotungstic acid (PWA) exhibiting the negative differential resistance (NDR) property. In its pristine state, PWA exhibits a high electrical resistance. However, upon applying electric current, the resistance is significantly decreased, resulting in increased conductivity and expression of NDR property, owing to the formation of conductive filaments and charge‐storage effects. The OLEDs produced with PWA achieve a low‐driving voltage and high external quantum efficiency simultaneously. Aluminum‐foil‐based OLEDs with a light‐emission area of 64 cm2 are fabricated. They emit steady luminescence even in crumpled and torn states. To the best of authors' knowledge, this is the first report of a large‐area OLED exhibiting steady luminescence in such deformed states. [ABSTRACT FROM AUTHOR]

[Display omitted] The performance of conventional computer based on von Neumann architecture is limited due to the physical separation of memory and processor. By synergistically integrating various sensors with synaptic devices, recently emerging interactive neuromorphic devices can directly sense/store/process various stimuli information from external environments and implement functions of perception, learning, memory, and computation. In this review, we present the basic model of bioinspired interactive neuromorphic devices and discuss the performance metrics. Next, we summarize the recent progress and development of bioinspired interactive neuromorphic devices, which are classified into neuromorphic tactile systems, visual systems, auditory systems, and multisensory system. They are discussed in detail from the aspects of materials, device architectures, operating mechanisms, synaptic plasticity, and potential applications. Additionally, the bioinspired interactive neuromorphic devices that can fuse multiple/mixed sensing signals are proposed to address more realistic and sophisticated problems. Finally, we discuss the pros and cons regarding to the computing neurons and integrating sensory neurons and deliver the perspectives on interactive neuromorphic devices at the material, device, network, and system levels. It is believed the neuromorphic devices can provide promising solutions to next generation of interactive sensation/memory/computation toward the development of multimodal, low-power, and large-scale intelligent systems endowed with neuromorphic features. [ABSTRACT FROM AUTHOR]

In‐sensor computing with visual information, which can integrate photo‐sensing, data storage, and computation functions within the same physical element, has promised a fundamentally different architecture for future machine vision technology with extreme energy and time efficiency. The elementary devices required to fulfil the goal of such a new sensory computation scheme would demand a bold functional variation to the existing sensor and data processing hardware. Here, a van der Waals (vdW) heterostructure‐based optoelectronic transistor that can act as an integrated photoreceptor, memory, and computation unit by exploiting its own physical attributes is demonstrated. It is found that diverse photoelectric control of device conductance can lead to versatile photoresponse characteristics, including memristive behaviors, retention‐, polarity‐ and strength‐tunable photoconductance, photoelectric‐coupling effect, etc. Exploiting the photoelectric‐coupling effect, reconfigurable and nonvolatile optoelectronic logic functions are realized in this device, featuring a logic‐in‐sensor unit. With the same device, both short‐ and long‐term synaptic plasticity can be faithfully emulated, rendering it an optoelectronic synaptic transistor. Moreover, a psychologic human memory model is implemented with the device, showing the emulation of memorization and learning processes. This prototypical demonstration provides a promising hardware system for visual information in‐sensor computing capable of addressing complex computation tasks. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new Furan-based bisphosphoramide displays strong UV light absorption but its Th(IV) complex resists the absorption of UV light. • A UV-light sensor has been demonstrated for Th4+ ions selectively. • The limit of detection (LOD) and limit of quantitation (LOQ) for Th4+ ions are 1.04 x 10-7 M and 3.472 x 10-7 M, respectively. • This salient UV light absorption phenomenon explores the artificial synaptic behavior of the L-Th4+ complex which can have remarkable memory and applications in neuromorphic devices. This article describes the synthesis and photophysical properties of a new furan-based bisphosphoramide ligand (L) and its Th(IV) complex characterized by various analytical and spectroscopic techniques. As expected, L , associated with several double bonds within the structure and the possible π → π* transitions, displays strong absorption in the UV region. Notably, the absorption of UV light decreases significantly in an exclusive manner for the L-Th4+ complex, and such behavior was not observed with most of the transition metals as well as f -metal ions. The ligand-to-metal binding ratio of L-Th4+ was found to be 1:1 through the Job's plot. This photophysical property was successfully explored in the selective determination of Th4+ ions in aqueous solutions. The limit of detection (LOD) for Th4+ and limit of quantitation (LOQ) were determined to be 1.04 x 10-7 M and 3.472 x 10-7 M, respectively. In order to get an insight at the molecular level, a density functional theory (DFT) investigation was performed to comprehend the sensing mechanism and interaction in the detection of Th4+ by L. Furthermore, this salient UV light absorption phenomenon was employed in exploring the artificial synaptic behavior of the L-Th4+ complex which can have remarkable memory and applications in neuromorphic devices. [ABSTRACT FROM AUTHOR]

Mitigating the nonlinear weight update of synaptic devices is one of the main challenges in designing compute-in-memory (CIM) crossbar arrays for artificial neural networks (ANNs). While various nonlinearity mitigation schemes have been proposed so far, only a few of them have dealt with high-weight update nonlinearity. This article presents a hardware-efficient on-chip weight update scheme named the conditional reverse update scheme (CRUS), which algorithmically mitigates highly nonlinear weight change in synaptic devices. For hardware efficiency, CRUS is implemented on-chip using low precision (1-bit) and infrequent circuit operations. To utilize algorithmic insights, the impact of the nonlinear weight update on training is investigated. We first introduce a metric called update noise (UN), which quantifies the deviation of the actual weight update in synaptic devices from the expected weight update calculated from the stochastic gradient descent (SGD) algorithm. Based on UN analysis, we aim to reduce AUN, the UN average over the entire training process. The key principle to reducing average UN (AUN) is to conditionally skip long-term depression (LTD) pulses during training. The trends of AUN and accuracy under various LTD skip conditions are investigated to find maximum accuracy conditions. By properly tuning LTD skip conditions, CRUS achieves >90% accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset even under high-weight update nonlinearity. Furthermore, it shows better accuracy than previous nonlinearity mitigation techniques under similar hardware conditions. It also exhibits robustness to cycle-to-cycle variations (CCVs) in conductance updates. The results suggest that CRUS can be an effective solution to relieve the algorithm-hardware tradeoff in CIM crossbar array design.

Habituation and sensitization represent nonassociative learning mechanisms in both non‐neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular‐like learning in oxygen‐deficient NiOx devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiOx under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next‐generation neuroinspired computing hardware. [ABSTRACT FROM AUTHOR]

Intrigued by the artificial intelligence and brain‐like neural networks, the photoelectronic neuromorphic devices that can simulate synaptic functions have been attracting wide attention. However, equalizing excitation‐inhibition has been seldom artificially realized despite its fundamental feature in a biological system. In this work, an artificial synapse is proposed based on a sandwiched Ag/Al2O3/graphene/ITO photodetector. The device exhibits a special ambipolar photoresponse at the millivoltage as a result of the interfacial trapping effects. Under the coupled light and electrical stimulus, the synaptic excitatory, inhibitory signals and their recovery are emulated on a single device via the positive and negative photoresponse, respectively. Further, the biologically dynamic balance of excitation and inhibition is reproduced on a simple analogous system integrating multiple photoelectronic synaptic cells. These remarkable results provide a potential foundation for building hardware units with neuromorphic architecture to mimic the complex human brain functionalities. [ABSTRACT FROM AUTHOR]

To realize the continuous development of information and communication equipment, it is also important to develop devices with new operating principles that overcome the functional and performance limitations of conventional electronics devices, such as transistors made of semiconductor materials. One group of devices that hold this promise is nanoionics devices that operate by using local ion transport and electrochemical reaction in solids. A number of nanoionics devices that control nanoelectrochemical phenomena through the use of ionic nanoarchitectonics have been created. Here the use of ionic nanoarchitectonics to locally control ion transport and electrochemical reactions through the use of ion conductors or mixed conductors as device materials is described. Further, the structures of nanoionics devices created by using the nanoarchitectonics, as well as their various electrical, magnetic and optical functionalities are introduced. It is expected that such nanoionics devices will complement conventional electronics devices, and make possible the further development of information and communication technologies. [ABSTRACT FROM AUTHOR]

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 SnO 2 nanoparticles. The devices manufactured on paper substrates exhibit photosynaptic behavior under ultraviolet pulse illumination, stemming from numerous interactions on the surface of the SnO 2 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 SnO 2 nanoparticles and differences in the depletion layer width at the SnO 2 /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., (© 2024 Wiley‐VCH GmbH.)

Habituation and sensitization represent nonassociative learning mechanisms in both non‐neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular‐like learning in oxygen‐deficient NiOx devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiOx under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next‐generation neuroinspired computing hardware.

Neuromorphic engineering aims to mimic brain functions to achieve energy‐efficient artificial intelligence. Since researchers have indicated that memristors can mimic synapses and neurons, various studies have demonstrated the operation of neural networks using memristive dot product engine (MDPE) hardware. However, although several feasible implementations of synapse and neuron behaviors have been reported, few studies have demonstrated the system‐level energy‐efficient operation on the hardware. This work proposes a novel system inspired by the neuromodulation of the brain, referred to as a "stashing system." In the system, the trained synapses are stashed temporarily during the training of the spiking neural network and then merged for inferencing. The software simulation first confirmed the working principle of the stashing system. Then, a hardware demonstration is performed at an integrated 32 × 32 MDPE embodying a self‐rectifying and electroforming‐free memristor cell to validate the system. The results confirm that energy consumption in the memristor array is reduced by 37% for the unsupervised learning of the MNIST dataset. [ABSTRACT FROM AUTHOR]

Organic electrochemical transistors (OECTs) have emerged as building blocks for low power circuits, biosensors, and neuromorphic computing. While p‐type polymer materials for OECTs are well developed, the choice of high‐performance n‐type polymers is limited, despite being essential for cation and metabolite biosensors, and crucial for constructing complementary circuits. N‐type conjugated polymers that have efficient ion‐to‐electron transduction are highly desired for electrochemical applications. In this contribution, three non‐fused, planar naphthalenediimide (NDI)‐dialkoxybithiazole (2Tz) copolymers, which systematically increase the amount of polar tri(ethylene glycol) (TEG) side chains: PNDI2OD‐2Tz (0 TEG), PNDIODTEG‐2Tz (1 TEG), PNDI2TEG‐2Tz (2 TEG), are reported. It is demonstrated that the OECT performance increases with the number of TEG side chains resulting from the progressively higher hydrophilicity and larger electron affinities. Benefiting from the high electron mobility, excellent ion conduction capability, efficient ion‐to‐electron transduction, and low‐lying lowest unoccupied molecular orbital energy level, the 2 TEG polymer achieves close to 105 on‐off ratio, fast switching, 1000 stable operation cycles in aqueous electrolyte, and has a long shelf life. Moreover, the higher number TEG chain substituted polymer exhibits good conductance state retention over two orders of magnitudes in electrochemical resistive random‐access memory devices, highlighting its potential for neuromorphic computing. [ABSTRACT FROM AUTHOR]

Neurotransmitters control signal transmission in the nervous system. The signals of neuron cells can be excited or inhibited based on the types of neurotransmitters that are released from pre‐synaptic neurons. The balance of the excitatory and inhibitory synaptic responses has important implications for the versatility, plasticity, and parallel computing characteristics of the nervous system. Emulating the excitatory–inhibitory balancing characteristics is one way to establish the versatility and plasticity characteristics of the brain. In this study, the authors develop artificial synapses to emulate the excitatory and inhibitory functions of biological synapses using electrochemical reactions between the channel and neurotransmitter solutions. The devices show excitatory and inhibitory characteristics depending on types of neurotransmitter solutions. The interaction between these two types of synaptic responses is employed for emulating the excitatory–inhibitory balance characteristics. The devices emulate the multifunctional characteristics of biological synapses, resulting in their potential for use in bio‐realistic neuromorphic devices. [ABSTRACT FROM AUTHOR]

The greatest strength of photogating effect is that the current level can be adjusted by light. Research on photogating effect is still actively underway, and various factors play a role in causing photo-reaction. In general, they work in such a way that photo-generated carriers generated under certain conditions are trapped, leading to threshold voltage shifts. Here we focus on the surface environment in which photocarriers are trapped/de-trapped. To analyze the difference in photo-response depending on the insulating layer of the phototransistor, we produced a DNTT TFTs with a SiO 2 dielectric and a DNTT TFTs with a TiO 2 /SiO 2 dielectric. Except for the insulating layer, the channel and source/drain electrodes use dinaphtho [2,3-b:2′,3′- f]thieno [3,23,2-b]thiophene (DNTT) and Au. 500 nm and 600 nm irradiation allowed a gradual current rise while DNTT TFTs with a TiO 2 /SiO 2 dielectric allowed fast de-trapping. Additionally, facial recognition synapse simulation using 500 nm and 600 nm light using a DNTT TFTs with a TiO 2 /SiO 2 dielectric achieved recognition rates of over 90%. [Display omitted] • Studying the photogating effects depending on dielectric. • Trap/de-trap characteristics according to dielectric-platform type were investigated. • Demonstration of facial recognition simulation using optical synaptic transistor. [ABSTRACT FROM AUTHOR]

Rather than generating a photocurrent through photo-excited carriers by the photoelectric effect, the photogating effect enables us to detect sub-bandgap rays. The photogating effect is caused by trapped photo-induced charges that modulate the potential energy of the semiconductor/dielectric interface, where these trapped charges contribute an additional electrical gating-field, resulting in a shift in the threshold voltage. This approach clearly separates the drain current in dark versus bright exposures. In this review, we discuss the photogating effect-driven photodetectors with respect to emerging optoelectrical materials, device structures, and mechanisms. Representative examples that reported the photogating effect-based sub-bandgap photodetection are revisited. Furthermore, emerging applications using these photogating effects are highlighted. The potential and challenging aspects of next-generation photodetector devices are presented with an emphasis on the photogating effect.

The factors controlling the neuromorphic response of organic electrochemical transistors (OECTs) are examined. In these devices, the gate voltage is used to supply the pre‐synaptic input, while the drain current is taken as the post‐synaptic output. The behavior of devices made from polymer blends of a mixed conductor (PEDOT:PSS) and an ion conductor (PSSNa) is analyzed. The experimental results highlight that the post‐synaptic response timescale depends on the size of ion in the electrolyte. Modeling shows that the neuromorphic response is controlled by the transient response of the ionic circuit of the OECT. These insights on device response time pave the way for a more rational design of OECT‐based neuromorphic devices. [ABSTRACT FROM AUTHOR]

Recent research demonstrates the viability of organic electrochemical transistors (OECTs) as an emergent technology for biosensor applications. Herein, a comprehensive summary is provided, highlighting the significant progress and most notable advances within the field of OECT‐based biosensors. The working principles of an OECT are detailed, with specific attention given to the current library of organic mixed ionic‐electronic conductor (OMIEC) channel materials utilized in OECT biosensors. The application of OECTs for metabolite, ion, neuromorphic, electrophysiological, and virus sensing as well as immunosensing is reported, detailing the breadth and scope of OECT‐based biosensors. Furthermore, an outlook and perspective on synthetic molecular design of future channel materials, specifically designed for OECT biosensors, is provided. The development of optimized channel materials, creative device architectures, and operational nuances will set the stage for OECT‐based biosensors to thrive and accelerate their clinical prevalence in the near future. [ABSTRACT FROM AUTHOR]