Neuromorphic Functions in PEDOT:PSS Organic Electrochemical Transistors

Dendritic Organic Electrochemical Transistors Grown by Electropolymerization for 3D Neuromorphic Engineering

One of the major limitations of standard top-down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, it is shown how bipolar electropolymerization can be used to engineer 3D networks of PEDOT:PSS dendritic fibers. By controlling the growth conditions of the electropolymerized material, it is investigated how dendritic fibers can reproduce structural plasticity by creating structures of controllable shape. Gradual topologies evolution is demonstrated in a multielectrode configuration. A detailed electrical characterization of the PEDOT:PSS dendrites is conducted through DC and impedance spectroscopy measurements and it is shown how organic electrochemical transistors (OECT) can be realized with these structures. These measurements reveal that quasi-static and transient response of OECTs can be adjusted by controlling dendrites' morphologies. The unique properties of organic dendrites are used to demonstrate short-term, long-term, and structural plasticity, which are essential features required for future neuromorphic hardware development.

Analog programing of conducting-polymer dendritic interconnections and control of their morphology

Although materials and processes are different from biological cells', brain mimicries led to tremendous achievements in parallel information processing via neuromorphic engineering. Inexistent in electronics, we emulate dendritic morphogenesis by electropolymerization in water, aiming in operando material modification for hardware learning. Systematic study of applied voltage-pulse parameters details on tuning independently morphological aspects of micrometric dendrites': fractal number, branching degree, asymmetry, density or length. Growths time-lapse image processing shows spatial features to be dynamically dependent, and expand distinctively before and after conductive bridging with two electro-generated dendrites. Circuit-element analysis and impedance spectroscopy confirms their morphological control in temporal windows where growth kinetics is finely perturbed by the input frequency and duty cycle. By the emulation of one's most preponderant mechanisms for brain's long-term memory, its implementation in vicinity of sensing arrays, neural probes or biochips shall greatly optimize computational costs and recognition required to classify high-dimensional patterns from complex environments.

Organic electrochemical transistors in bioelectronic circuits

The organic electrochemical transistor (OECT) represents a versatile and impactful electronic building block in the areas of printed electronics, bioelectronics, and neuromorphic computing. Significant efforts in OECTs have focused on device physics, new active material design and synthesis, and on preliminary implementation of individual transistors as proof-of-concept components for sensing and computation. However, as most of the current studies are based on single devices, the integration of OECTs into circuits or high-level systems has lagged. In this review, we focus on recent efforts to incorporate individual OECTs into digital, analog, and neuromorphic circuits, and lay out important considerations relevant for (hybrid) systems integration. We summarize the operation principles and the functions of OECT-based circuits and discuss the approaches for wireless power and data transmission for practicality in biological and bio-inspired applications. Finally, we comment on the future directions and challenges facing OECT circuits from both a fundamental and applied perspective.

Electrolyte-gated transistors for enhanced performance bioelectronics

Electrolyte-gated transistors (EGTs), capable of transducing biological and biochemical inputs into amplified electronic signals and stably operating in aqueous environments, have emerged as fundamental building blocks in bioelectronics. In this Primer, the different EGT architectures are described with the fundamental mechanisms underpinning their functional operation, providing insight into key experiments including necessary data analysis and validation. Several organic and inorganic materials used in the EGT structures and the different fabrication approaches for an optimal experimental design are presented and compared. The functional bio-layers and/or biosystems integrated into or interfaced to EGTs, including self-organization and self-assembly strategies, are reviewed. Relevant and promising applications are discussed, including two-dimensional and three-dimensional cell monitoring, ultra-sensitive biosensors, electrophysiology, synaptic and neuromorphic bio-interfaces, prosthetics and robotics. Advantages, limitations and possible optimizations are also surveyed. Finally, current issues and future directions for further developments and applications are discussed.

UV light modulated synaptic behavior of MoTe2/BN heterostructure

Electrical synaptic devices are the basic components for the hardware based neuromorphic computational systems, which are expected to break the bottleneck of current von Neumann architecture. So far, synaptic devices based on three-terminal transistors are considered to provide the most stable performance, which usually use gate pulses to modulate the channel conductance through a floating gate and/or charge trapping layer. Herein, we report a three-terminal synaptic device based on a two-dimensional molybdenum ditelluride (MoTe₂)/hexagonal boron nitride (hBN) heterostructure. This structure enables stable and prominent conductance modulation of the MoTe₂channel by the photo-induced doping method through electron migration between the MoTe₂channel and ultraviolet (UV) light excited mid-gap defect states in hBN. Therefore, it is free of the floating gate and charge trapping layer to reduce the thickness and simplify the fabrication/design of the device. Moreover, since UV illumination is indispensable for stable doping in MoTe₂channel, the device can realize both short- (without UV illumination) and long- (with UV illumination) term plasticity. Meanwhile, the introduction of UV light allows additional tunability on the MoTe₂channel conductance through the wavelength and power intensity of incident UV, which may be important to mimic advanced synaptic functions. In addition, the photo-induced doping method can bidirectionally dope MoTe₂channel, which not only leads to large high/low resistance ratio for potential multi-level storage, but also implement both potentiation (n-doping) and depression (p-doping) of synaptic weight. This work explores alternative three-terminal synaptic configuration without floating gate and charge trapping layer, which may inspire researches on novel electrical synapse mechanisms.

Harnessing the Wide-range Strain Sensitivity of Bilayered PEDOT:PSS Films for Wearable Health Monitoring

Mechanical deformation of human skin provides essential information about human motions, muscle stretching, vocal fold vibration, and heart rates. Monitoring these activities requires the measurement of strains at different levels. Herein, we report a wearable wide-range strain sensor based on conducting polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). A bioinspired bilayer structure was constructed to enable a wide-range strain sensing (1%~100%). Besides, hydrogel was chosen as the biological- and mechanical-compatible interface layer with the human skin. Finally, we demonstrated that the strain sensor is capable of monitoring various strain-related activities, including subtle skin deformation (pulse and phonation), mid-level body stretch (swallowing and facial expressions), and substantial joint movement (elbow bending).

Two-Dimensional Metal-Organic Framework Film for Realizing Optoelectronic Synaptic Plasticity

2D metal-organic framework (MOF) film as the active layer show promising application prospects in various fields including sensors, catalysis, and electronic devices. However, exploring the application of 2D MOF film in the field of artificial synapses has not been implemented yet. In this work, we fabricated a novel 2D MOF film (Cu-THPP, THPP=5,10,15,20-Tetrakis(4-hydroxyphenyl)-21H,23H-porphine), and further used it as an active layer to explore the application in the simulation of human brain synapses. It shows excellent light-stimulated synaptic plasticity properties, and exhibits the foundation function of synapses such as long-term plasticity (LTP), short-term plasticity (STP), and the conversion of STP to LTP. Most critically, the MOF based artificial synaptic device exhibits an excellent stability in atmosphere. This work opens the door for the application of 2D MOF film in the simulation of human brain synapses.

Dendritic Network Implementable Organic Neurofiber Transistors with Enhanced Memory Cyclic Endurance for Spatiotemporal Iterative Learning

Dendritic network implementable organic neurofiber transistors with enhanced memory cyclic endurance for spatiotemporal iterative learning are proposed. The architecture of the fibrous organic electrochemical transistors consisting of a double-stranded assembly of electrode microfibers and an iongel gate insulator enables the highly sensitive multiple implementation of synaptic junctions via simple physical contact of gate-electrode microfibers, similar to the dendritic connections of a biological neuron fiber. In particular, carboxylic-acid-functionalized polythiophene as a semiconductor channel material provides stable gate-field-dependent multilevel memory characteristics with long-term stability and cyclic endurance, unlike the conventional poly(alkylthiophene)-based neuromorphic electrochemical transistors, which exhibit short retention and unstable endurance. The dissociation of the carboxylic acid of the polythiophene enables reversible doping and dedoping of the polythiophene channel by effectively stabilizing the ions that penetrate the channel during potentiation and depression cycles, leading to the reliable cyclic endurance of the device. The synaptic weight of the neurofiber transistors with a dendritic network maintains the state levels stably and is independently updated with each synapse connected with the presynaptic neuron to a specific state level. Finally, the neurofiber transistor demonstrates successful speech recognition based on iterative spiking neural network learning in the time domain, showing a substantial recognition accuracy of 88.9%.

Neuromorphic Devices for Bionic Sensing and Perception

Neuromorphic devices that can emulate the bionic sensory and perceptual functions of neural systems have great applications in personal healthcare monitoring, neuro-prosthetics, and human-machine interfaces. In order to realize bionic sensing and perception, it's crucial to prepare neuromorphic devices with the function of perceiving environment in real-time. Up to now, lots of efforts have been made in the incorporation of the bio-inspired sensing and neuromorphic engineering in the booming artificial intelligence industry. In this review, we first introduce neuromorphic devices based on diverse materials and mechanisms. Then we summarize the progress made in the emulation of biological sensing and perception systems. Finally, the challenges and opportunities in these fields are also discussed.

PEDOT:PSS-Based Bioelectronic Devices for Recording and Modulation of Electrophysiological and Biochemical Cell Signals

To understand the physiology and pathology of electrogenic cells and the corresponding tissue in their full complexity, the quantitative investigation of the transmission of ions as well as the release of chemical signals is important. Organic (semi-) conducting materials and in particular organic electrochemical transistor are gaining in importance for the investigation of electrophysiological and recently biochemical signals due to their synthetic nature and thus chemical diversity and modifiability, their biocompatible and compliant properties, as well as their mixed electronic and ionic conductivity featuring ion-to-electron conversion. Here, the aim is to summarize recent progress on the development of bioelectronic devices utilizing polymer polyethylenedioxythiophene: poly(styrene sulfonate) (PEDOT:PSS) to interface electronics and biological matter including microelectrode arrays, neural cuff electrodes, organic electrochemical transistors, PEDOT:PSS-based biosensors, and organic electronic ion pumps. Finally, progress in the material development is summarized for the improvement of polymer conductivity, stretchability, higher transistor transconductance, or to extend their field of application such as cation sensing or metabolite recognition. This survey of recent trends in PEDOT:PSS electrophysiological sensors highlights the potential of this multifunctional material to revolve current technology and to enable long-lasting, multichannel polymer probes for simultaneous recordings of electrophysiological and biochemical signals from electrogenic cells.

Realization and training of an inverter-based printed neuromorphic computing system

Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction with electronic functionality. Due to high production costs and conformity restrictions, rigid silicon technologies do not meet application requirements in these new domains. However, whenever signal processing becomes too comprehensive, silicon technology must be used for the high-performance computing unit. At the same time, designing everything in flexible or printed electronics using conventional digital logic is not feasible yet due to the limitations of printed technologies in terms of performance, power and integration density. We propose to rather use the strengths of neuromorphic computing architectures consisting in their homogeneous topologies, few building blocks and analog signal processing to be mapped to an inkjet-printed hardware architecture. It has remained a challenge to demonstrate non-linear elements besides weighted aggregation. We demonstrate in this work printed hardware building blocks such as inverter-based comprehensive weight representation and resistive crossbars as well as printed transistor-based activation functions. In addition, we present a learning algorithm developed to train the proposed printed NCS architecture based on specific requirements and constraints of the technology.

Recent advances toward environment-friendly photodetectors based on lead-free metal halide perovskites and perovskite derivatives

Recently, metal-halide perovskites have emerged as promising materials for photodetector (PD) applications owing to their superior optoelectronic properties, such as ambipolar charge transport characteristics, high carrier mobility, and so on. In the past few years, rapid progress in lead-based perovskite PDs has been witnessed. However, the critical environmental instability and lead-toxicity seriously hinder their further applications and commercialization. Therefore, searching for environmentally stable and lead-free halide perovskites (LFHPs) to address the above hurdles is certainly a worthwhile subject. In this review, we present a comprehensive overview of currently explored LFHPs with an emphasis on their crystal structures, optoelectronic properties, synthesis and modification methods, as well as the PD applications. LFHPs are classified into four categories according to the replacement strategies of Pb²⁺, including AB(ii)X₃, A₃B(iii)₂X₉, A₂B(i)B(iii)'X₆, and newly-emerging perovskite derivatives. Then, we give a demonstration of the preliminary achievements and limitations in environment-friendly PDs based on such LFHPs and perovskite derivatives, and also discuss their applications in biological synapses, imaging, and X-ray detection. With the perspective of their properties and current challenges, we provide an outlook for future directions in this rapidly evolving field to achieve high-quality LFHPs and perovskite derivatives for a broader range of fundamental research and practical applications.

Nanofiber Channel Organic Electrochemical Transistors for Low-Power Neuromorphic Computing and Wide-Bandwidth Sensing Platforms

Organic neuromorphic computing/sensing platforms are a promising concept for local monitoring and processing of biological signals in real time. Neuromorphic devices and sensors with low conductance for low power consumption and high conductance for low-impedance sensing are desired. However, it has been a struggle to find materials and fabrication methods that satisfy both of these properties simultaneously in a single substrate. Here, nanofiber channels with a self-formed ion-blocking layer are fabricated to create organic electrochemical transistors (OECTs) that can be tailored to achieve low-power neuromorphic computing and fast-response sensing by transferring different amounts of electrospun nanofibers to each device. With their nanofiber architecture, the OECTs exhibit a low switching energy of 113 fJ and operate within a wide bandwidth (cut-off frequency of 13.5 kHz), opening a new paradigm for energy-efficient neuromorphic computing/sensing platforms in a biological environment without the leakage of personal information.

Artificial Skin Perception

Skin is the largest organ, with the functionalities of protection, regulation, and sensation. The emulation of human skin via flexible and stretchable electronics gives rise to electronic skin (e-skin), which has realized artificial sensation and other functions that cannot be achieved by conventional electronics. To date, tremendous progress has been made in data acquisition and transmission for e-skin systems, while the implementation of perception within systems, that is, sensory data processing, is still in its infancy. Integrating the perception functionality into a flexible and stretchable sensing system, namely artificial skin perception, is critical to endow current e-skin systems with higher intelligence. Here, recent progress in the design and fabrication of artificial skin perception devices and systems is summarized, and challenges and prospects are discussed. The strategies for implementing artificial skin perception utilize either conventional silicon-based circuits or novel flexible computing devices such as memristive devices and synaptic transistors, which enable artificial skin to surpass human skin, with a distributed, low-latency, and energy-efficient information-processing ability. In future, artificial skin perception would be a new enabling technology to construct next-generation intelligent electronic devices and systems for advanced applications, such as robotic surgery, rehabilitation, and prosthetics.

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual's adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

A high-conductivity n-type polymeric ink for printed electronics

Conducting polymers, such as the p-doped poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), have enabled the development of an array of opto- and bio-electronics devices. However, to make these technologies truly pervasive, stable and easily processable, n-doped conducting polymers are also needed. Despite major efforts, no n-type equivalents to the benchmark PEDOT:PSS exist to date. Here, we report on the development of poly(benzimidazobenzophenanthroline):poly(ethyleneimine) (BBL:PEI) as an ethanol-based n-type conductive ink. BBL:PEI thin films yield an n-type electrical conductivity reaching 8 S cm^-1, along with excellent thermal, ambient, and solvent stability. This printable n-type mixed ion-electron conductor has several technological implications for realizing high-performance organic electronic devices, as demonstrated for organic thermoelectric generators with record high power output and n-type organic electrochemical transistors with a unique depletion mode of operation. BBL:PEI inks hold promise for the development of next-generation bioelectronics and wearable devices, in particular targeting novel functionality, efficiency, and power performance.

Artificial stimulus-response system capable of conscious response

A stimulus-response system and conscious response enable humans to respond effectively to environmental changes and external stimuli. This paper presents an artificial stimulus-response system that is inspired by human conscious response and is capable of emulating it. The system is composed of an artificial visual receptor, artificial synapse, artificial neuron circuits, and actuator. By incorporating these artificial nervous components, a series of conscious response processes that markedly reduces response time as a result of learning from repeated stimuli are demonstrated. The proposed artificial stimulus-response system offers the promise of a new research field that would aid the development of artificial intelligence-based organs for patients with neurological disorders.

A CMOS Multi-Modal Electrochemical and Impedance Cellular Sensing Array for Massively Paralleled Exoelectrogen Screening

The paper presents a 256-pixel CMOS sensor array with in-pixel dual electrochemical and impedance detection modalities for rapid, multi-dimensional characterization of exoelectrogens. The CMOS IC has 16 parallel readout channels, allowing it to perform multiple measurements with a high throughput and enable the chip to handle different samples simultaneously. The chip contains a total of 2 × 256 working electrodes of size 44 μm × 52 μm, along with 16 reference electrodes of dimensions 56 μm × 399 μm and 32 counter electrodes of dimensions 399 μm × 106 μm, which together facilitate the high resolution screening of the test samples. The chip was fabricated in a standard 130nm BiCMOS process. The on-chip electrodes are subjected to additional fabrication processes, including a critical Al-etch step that ensures the excellent biocompatibility and long-term reliability of the CMOS sensor array in bio-environment. The electrochemical sensing modality is verified by detecting the electroactive analyte NaFeEDTA and the exoelectrogenic Shewanella oneidensis MR-1 bacteria, illustrating the chip's ability to quantify the generated electrochemical current and distinguish between different analyte concentrations. The impedance measurements with the HEK-293 cancer cells cultured on-chip successfully capture the cell-to-surface adhesion information between the electrodes and the cancer cells. The reported CMOS sensor array outperforms the conventional discrete setups for exoelectrogen characterization in terms of spatial resolution and speed, which demonstrates the chip's potential to radically accelerate synthetic biology engineering.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Evolution of Bio-Inspired Artificial Synapses: Materials, Structures, and Mechanisms

Artificial synapses (ASs) are electronic devices emulating important functions of biological synapses, which are essential building blocks of artificial neuromorphic networks for brain-inspired computing. A human brain consists of several quadrillion synapses for information storage and processing, and massively parallel computation. Neuromorphic systems require ASs to mimic biological synaptic functions, such as paired-pulse facilitation, short-term potentiation, long-term potentiation, spatiotemporally-correlated signal processing, and spike-timing-dependent plasticity, etc. Feature size and energy consumption of ASs need to be minimized for high-density energy-efficient integration. This work reviews recent progress on ASs. First, synaptic plasticity and functional emulation are introduced, and then synaptic electronic devices for neuromorphic computing systems are discussed. Recent advances in flexible artificial synapses for artificial sensory nerves are also briefly introduced. Finally, challenges and opportunities in the field are discussed.

Selective Release of Different Neurotransmitters Emulated by a p-i-n Junction Synaptic Transistor for Environment-Responsive Action Control

The design of the first p-i-n junction synaptic transistor (JST) based on n-type TiO2 film covered with poly(methyl methacrylate) (PMMA) and with a p-type P3HT/PEO nanowire (NW) on top. Except for basic synaptic functions that can be realized by a single neurotransmitter, the electronic device emulates the multiplexed neurotransmission of different neurotransmissions, i.e., glutamate and acetylcholine, for fast switching between short- and long-term plasticity (STP and LTP). This is realized by the special p-i-n junction with hole transport in the p-type P3HT NW to form STP, and electron transport in the n-type TiO2 layer and trapped under the PMMA inversion layer to form LTP. Altering the external input induces changes of the polarity of the charge carriers in the conductive channel, promoting fast switching between STP and LTP modes. When stimulated using two parallel inputs, the response of PMMA/TiO2 emulates the synergistic effect of taste and aroma on the control of food-intake in the brain. Because of the bipolarity, the p-i-n JST has excellent reconfigurability, which importantly is attributed to simulate the plasticity of synapses and to mimic how distinct types of gustatory receptor neurons respond to different concentrations of salt. The electronic device lays the technical foundation for the realization of the future complex artificial neural networks.

Recent Process of Flexible Transistor-Structured Memory

Flexible transistor-structured memory (FTSM) has attracted great attention for its important role in flexible electronics. For nonvolatile information storage, FTSMs with floating-gate, charge-trap, and ferroelectric mechanisms have been developed. By introducing an optical sensory module, FTSM can be operated by optical inputs to function as an optical memory transistor. As a special type of FTSM, transistor-structured artificial synapse emulates important functions of a biological synapse to mimic brain-inspired memory behaviors and nervous signal transmissions. This work reviews the recent development of the above mentioned FTSMs, with a focus on working mechanism and materials, and flexibility.

Polaron Delocalization in Donor-Acceptor Polymers and its Impact on Organic Electrochemical Transistor Performance

Donor-acceptor (D-A) polymers are promising materials for organic electrochemical transistors (OECTs), as they minimize detrimental faradaic side-reactions during OECT operation, yet their steady-state OECT performance still lags far behind their all-donor counterparts. We report three D-A polymers based on the diketopyrrolopyrrole unit that afford OECT performances similar to those of all-donor polymers, hence representing a significant improvement to the previously developed D-A copolymers. In addition to improved OECT performance, DFT simulations of the polymers and their respective hole polarons also reveal a positive correlation between hole polaron delocalization and steady-state OECT performance, providing new insights into the design of OECT materials. Importantly, we demonstrate how polaron delocalization can be tuned directly at the molecular level by selection of the building blocks comprising the polymers' conjugated backbone, thus paving the way for the development of even higher performing OECT polymers.

Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics

A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic biological signal transmission behavior. The impulse response of the GAS has been reduced to several millivolts with competitive femtowatt-level consumption, exceeding the biological level by orders of magnitude. Most importantly, the GAS is capable of parallelly processing signals transmitted from multiple pre-neurons and therefore realizing dynamic logic and spatiotemporal rules. It is also found that the GAS is thermally stable (at 353 K) and environmentally stable (in a relative humidity up to 35%). Our artificial efferent nerve, connecting the GAS with artificial muscles, has been demonstrated to complete the information integration of pre-neurons and the information output of motor neurons, which is advantageous for coalescing multiple sensory feedbacks and reacting to events. Our synaptic element has potential applications in bioinspired peripheral nervous systems of soft electronics, neurorobotics, and biohybrid systems of brain-computer interfaces.

Solution-processed electronics for artificial synapses

Artificial synaptic devices and systems have become hot topics due to parallel computing, high plasticity, integration of storage, and processing to meet the challenges of the traditional Von Neumann computers. Currently, two-terminal memristors and three-terminal transistors have been mainly developed for high-density storage with high switching speed and high reliability because of the adjustable resistivity, controllable ion migration, and abundant choices of functional materials and fabrication processes. To achieve the low-cost, large-scale, and easy-process fabrication, solution-processed techniques have been extensively employed to develop synaptic electronics towards flexible and highly integrated three-dimensional (3D) neural networks. Herein, we have summarized and discussed solution-processed techniques in the fabrication of two-terminal memristors and three-terminal transistors for the application of artificial synaptic electronics mainly reported in the recent five years from the view of fabrication processes, functional materials, electronic operating mechanisms, and system applications. Furthermore, the challenges and prospects were discussed in depth to promote solution-processed techniques in the future development of artificial synapse with high performance and high integration.

Synaptic Iontronic Devices for Brain-Mimicking Functions: Fundamentals and Applications

Inspired by the information transmission mechanism in the central nervous systems of life, synapse-mimicking devices have been designed and fabricated for the purpose of breaking the bottleneck of von Neumann architecture and realizing the construction of effective hardware-based artificial intelligence. In this case, synaptic iontronic devices, dealing with current information with ions instead of electrons, have attracted enormous scientific interests owing to their unique characteristics provided by ions, such as the designability of charge carriers and the diversity of chemical regulation. Herein, the basic conception, working mechanism, performance metrics, and advanced applications of synaptic iontronic devices based on three-terminal transistors and two-terminal memristors are systematically reviewed and comprehensively discussed. This Review provides a prospect on how to realize artificial synaptic functions based on the regulation of ions and raises a series of further challenges unsolved in this area.

Highly Sensitive Artificial Visual Array Using Transistors Based on Porphyrins and Semiconductors

Artificial visual systems with image sensing and storage functions have considerable potential in the field of artificial intelligence. Light-stimulated synaptic devices can be applied for neuromorphic computing to build artificial visual systems. Here, optoelectronic synaptic transistors based on 5,15-(2-hydroxyphenyl)-10,20-(4-nitrophenyl)porphyrin (TPP) and dinaphtho[2,3-b:2',3'-f ]thieno[3,2-b]thiophene (DNTT) are demonstrated. By utilizing stable TPP with high light absorption, the number of photogenerated carriers in the transport layer can be increased significantly. The devices exhibit high photosensitivity and tunable synaptic plasticity. The synaptic weight can be effectively modulated by the intensity, width, and wavelength of the light signals. Due to the high light absorption of TPP, an ultrasensitive artificial visual array based on these devices is developed, which can detect weak light signals as low as 1 µW cm^-2 . Low-voltage operation is further demonstrated. Even with applied voltages as low as -70 µV, the devices can still show obvious responses, leading to an ultralow energy consumption of 1.4 fJ. The devices successfully demonstrate image sensing and storage functions, which can accurately identify visual information. In addition, the devices can preprocess information and achieve noise reduction. The excellent synaptic behavior of the TPP-based electronics suggests their good potential in the development of new intelligent visual systems.

Emerging Materials for Neuromorphic Devices and Systems

Neuromorphic devices and systems have attracted attention as next-generation computing due to their high efficiency in processing complex data. So far, they have been demonstrated using both machine-learning software and complementary metal-oxide-semiconductor-based hardware. However, these approaches have drawbacks in power consumption and learning speed. An energy-efficient neuromorphic computing system requires hardware that can mimic the functions of a brain. Therefore, various materials have been introduced for the development of neuromorphic devices. Here, recent advances in neuromorphic devices are reviewed. First, the functions of biological synapses and neurons are discussed. Also, deep neural networks and spiking neural networks are described. Then, the operation mechanism and the neuromorphic functions of emerging devices are reviewed. Finally, the challenges and prospects for developing neuromorphic devices that use emerging materials are discussed.

The influence of sorbitol doping on aggregation and electronic properties of PEDOT:PSS: a theoretical study

Many organic electronics applications such as organic solar cells or thermoelectric generators rely on PEDOT:PSS as a conductive polymer that is printable and transparent. It was found that doping PEDOT:PSS with sorbitol enhances the conductivity through morphological changes. However, the microscopic mechanism is not well understood. In this work, we combine computational tools with machine learning to investigate changes in morphological and electronic properties of PEDOT:PSS when doped with sorbitol. We find that sorbitol improves the alignment of PEDOT oligomers, leading to a reduction of energy disorder and an increase in electronic couplings between PEDOT chains. The high accuracy (r 2 > 0.9) and speed up of energy level predictions of neural networks compared to density functional theory enables us to analyze HOMO energies of PEDOT oligomers as a function of time. We find a surprisingly low degree of static energy disorder compared to other organic semiconductors. This finding might help to better understand the microscopic origin of the high charge carrier mobility of PEDOT:PSS in general and potentially help to design new conductive polymers.

Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing

Ferroelectric synapses using polarization switching (a purely electronic switching process) to induce analog conductance change have attracted considerable interest. Here, we propose ferroelectric photovoltaic (FePV) synapses that use polarization-controlled photocurrent as the readout and thus have no limitations on the forms and thicknesses of the constituent ferroelectric and electrode materials. This not only makes FePV synapses easy to fabricate but also reduces the depolarization effect and hence enhances the polarization controllability. As a proof-of-concept implementation, a Pt/Pb(Zr_0.2Ti_0.8)O₃/LaNiO₃ FePV synapse is facilely grown on a silicon substrate, which demonstrates continuous photovoltaic response modulation with good controllability (small nonlinearity and write noise) enabled by gradual polarization switching. Using photovoltaic response as synaptic weight, this device exhibits versatile synaptic functions including long-term potentiation/depression and spike-timing-dependent plasticity. A simulated FePV synapse-based neural network achieves high accuracies (>93%) for image recognition. This study paves a new way toward highly controllable and silicon-compatible synapses for neuromorphic computing.

High-Performance Organic Electrochemical Transistors with Nanoscale Channel Length and Their Application to Artificial Synapse

Organic electrochemical transistors (OECTs) have attracted considerable interests for various applications ranging from biosensors to digital logic circuits and artificial synapses. However, the majority of reported OECTs utilize large channel length up to several or several tens of micrometers, which limits the device performance and leads to low transistor densities. Here, we demonstrate a new design of vertical OECT architecture with a nanoscale channel length down to ∼100 nm. The devices exhibit a high on-state current of over 20 mA under a low bias voltage of 0.5 V, a fast transient response of less than 300 μs, and an extraordinary transconductance up to 68.88 mS, representing a record-high value for OECTs. The excellent electrical performance is attributed to the novel structure with a nanoscale channel length defined by the channel material thickness, which is intrinsically different from that of conventional OECTs with the channel length limited by the lithography resolution. Owing to the low thermal budget, we fabricate flexible devices on a flexible substrate, which exhibit unprecedented endurance characteristics and mechanical robustness after 1000 blending cycles. Furthermore, the proposed device is capable of mimicking biological inhibitory synapses for application in intelligent artificial neural networks. Our work not only pushes the performance limit of OECTs but also opens up a new design of OECTs for high-performance biosensors, digital logic, and neuromorphic devices.

Artificial visual systems enabled by quasi-two-dimensional electron gases in oxide superlattice nanowires

Rapid development of artificial intelligence techniques ignites the emerging demand on accurate perception and understanding of optical signals from external environments via brain-like visual systems. Here, enabled by quasi-two-dimensional electron gases (quasi-2DEGs) in InGaO₃(ZnO)₃ superlattice nanowires (NWs), an artificial visual system was built to mimic the human ones. This system is based on an unreported device concept combining coexistence of oxygen adsorption-desorption kinetics on NW surface and strong carrier quantum-confinement effects in superlattice core, to resemble the biological Ca²⁺ ion flux and neurotransmitter release dynamics. Given outstanding mobility and sensitivity of superlattice NWs, an ultralow energy consumption down to subfemtojoule per synaptic event is realized in quasi-2DEG synapses, which rivals that of biological synapses and now available synapse-inspired electronics. A flexible quasi-2DEG artificial visual system is demonstrated to simultaneously perform high-performance light detection, brain-like information processing, nonvolatile charge retention, in situ multibit-level memory, orientation selectivity, and image memorizing.

Oxide-Based Electrolyte-Gated Transistors for Spatiotemporal Information Processing

Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time- and energy-efficient computational paradigms for the Internet-of-Things and edge computing. Nonvolatile electrolyte-gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large-scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide-based EGT employing amorphous Nb₂ O₅ and Li_x SiO₂ is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi-linear update, good endurance (10⁶ ) and retention, a high switching speed of 100 ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm^-2 ). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT-based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide-based EGT devices for energy-efficient neuromorphic computing to support edge application.

Suppression of Ionic Doping by Molecular Dopants in Conjugated Polymers for Improving Specificity and Sensitivity in Biosensing Applications

Ionic doping effects in conjugated polymers often cause nonspecific signaling and a low selectivity of bioelectronic sensing. Using remote-gate field-effect transistor characterization of molecular and ionic doping in poly(3-hexylthiophene) (P3HT) and acid-functionalized polythiophene, poly[3-(3-carboxypropyl) thiophene-2,5-diyl] (PT-COOH), we discovered that proton doping effects on the interfacial potential occurring in P3HT could be suppressed by sequentially doping P3HT by 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ). To be specific, intrinsic pH sensitivity shown by pure P3HT (18 mV/pH in a range from pH 3 to 9) was fully dissipated for doped P3HT:F4TCNQ. However, F4TCNQ sequential doping instead increases pH sensitivity of acid-functionalized polythiophene, PT-COOH (40 mV/pH), compared to that of a pure PT-COOH (30 mV/pH). Interactions between polythiophene backbone and side chains, which constrain the activity of COOH, are weakened by stronger F4TCNQ doping leaving behind responsive COOH groups exposed to aqueous solutions. This is supported by the reduced pH sensitivity of PT-COOH sequentially doped by a weaker dopant, tetracyanoethylene (TCNE) (21 mV/pH). Thus, doping is shown to stabilize a nonpolar conjugated polymer to pH-induced fluctuations on one hand, and to activate a COOH side chain to pH-induced response on the other.

Designing Polymeric Mixed Conductors and Their Application to Electrochemical-Transistor-Based Biosensors

Organic electrochemical transistors that employ polymeric mixed conductors as their active channels are one of the most prominent biosensor platforms because of their signal amplification capability, low fabrication cost, mechanical flexibility, and various properties tunable through molecular design. For application to biomedical devices, polymeric mixed conductors should fulfill several requirements, such as excellent conductivities of both holes/electrons and ions, long-term operation stability, and decent biocompatibility. However, trade-offs may exist, for instance, one between ionic conduction and overall device stability. In this report, the fundamental understanding of polymeric mixed conductors, the recent advance in enhancing their ionic and electrical conductivity, and their practical applications as biosensors based on organic electrochemical transistors are reviewed. Finally, key strategies are suggested for developing novel polymeric mixed conductors that may exceed the trade-off between device performance and stability.

High-Resolution Electrochemical Transistors Defined by Mold-Guided Drying of PEDOT:PSS Liquid Suspension

Ion-sensitive transistors with nanoscale or microscale dimensions are promising for high-resolution electrophysiological recording and sensing. Technology that can pattern polymer functional materials directly from a solution can effectively avoid any chemical damage induced by conventional lithography techniques. The application of a mold-guided drying technique to pattern PEDOT:PSS-based transistors with high resolution directly from the water-based suspension is presented in this paper. Gold electrodes with short channels were first defined by creating high-resolution polymer lines with mold-guided drying followed by pattern transfer through a lift-off process. Then, PEDOT:PSS lines were subsequently created through an identical mold-guided drying process on the predefined electrodes. Small-scale transistor devices with both shortened channel length and width exhibited a good high-frequency response because of the weak capacitive effect. This is particularly advantageous for electrochemical transistors since the use of conventional fabrication techniques is extremely challenging in this case. In addition, modified polymer chain alignment of the assembled PEDOT:PSS lines during the drying process was observed by optical and electrical measurement. The mold-guided drying technique has been proven to be a promising method to fabricate small-scale devices, especially for biological applications.

Anisotropic Signal Processing with Trigonal Selenium Nanosheet Synaptic Transistors

Hardware implementation of an artificial neural network requires neuromorphic devices to process information with low energy consumption and high heterogeneity. Here we demonstrate an electrolyte-gated synaptic transistor (EGT) based on a trigonal selenium (t-Se) nanosheet. Due to the intrinsic low conductivity of the Se channel, the t-Se synaptic transistor exhibits ultralow energy consumption, less than 0.1 pJ per spike. More importantly, the intrinsic low symmetry of t-Se offers a strong anisotropy along its c- and a-axis in electrical conductance with a ratio of up to 8.6. The multiterminal EGT device exhibits an anisotropic response of filtering behavior to the same external stimulus, which enables it to mimic the heterogeneous signal transmission process of the axon-multisynapse biostructure in the human brain. The proof-of-concept device in this work represents an important step to develop neuromorphic electronics for processing complex signals.

Multi-terminal ionic-gated low-power silicon nanowire synaptic transistors with dendritic functions for neuromorphic systems

Neuromorphic computing systems have shown powerful capability in tasks, such as recognition, learning, classification and decision-making, which are both challenging and inefficient in using the traditional computation architecture. The key elements including synapses and neurons, and their feasible hardware implementation are essential for practical neuromorphic computing. However, most existing synaptic devices used to emulate functions of a single synapse and the synapse-based networks are more energy intensive and less sustainable than their biological counterparts. The dendritic functions such as integration of spatiotemporal signals and spike-frequency coding characteristics have not been well implemented in a single synaptic device and thus play an imperative role in future practical hardware-based spiking neural networks. Moreover, most emerging synaptic transistors are fabricated by nanofabrication processes without CMOS compatibility for further wafer-scale integration. Herein, we demonstrate a novel ionic-gated silicon nanowire synaptic field-effect transistor (IGNWFET) with low power consumption (<400 fJ per switching event) based on the standard CMOS process platform. For the first time, the dendritic integration and dual-synaptic dendritic computations (such as "Add" and "Subtraction") could be realized by processing frequency coded spikes using a single device. Meanwhile, multi-functional characteristics of artificial synapses including the short-term and long-term synaptic plasticity, paired pulse facilitation and high-pass filtering were also successfully demonstrated based on 40 nm wide IGNWFETs. The migration of ions in polymer electrolyte and trapping in high-k dielectric were also experimentally studied in-depth to understand the short-term plasticity and long-term plasticity. Combined with statistical uniformity across a 4-inch wafer, the comprehensive performance of IGNWFET demonstrates its potential application in future biologically emulated neuromorphic systems.

Oxidation-boosted charge trapping in ultra-sensitive van der Waals materials for artificial synaptic features

Exploitation of the oxidation behaviour in an environmentally sensitive semiconductor is significant to modulate its electronic properties and develop unique applications. Here, we demonstrate a native oxidation-inspired InSe field-effect transistor as an artificial synapse in device level that benefits from the boosted charge trapping under ambient conditions. A thin InO_x layer is confirmed under the InSe channel, which can serve as an effective charge trapping layer for information storage. The dynamic characteristic measurement is further performed to reveal the corresponding uniform charge trapping and releasing process, which coincides with its surface-effect-governed carrier fluctuations. As a result, the oxide-decorated InSe device exhibits nonvolatile memory characteristics with flexible programming/erasing operations. Furthermore, an InSe-based artificial synapse is implemented to emulate the essential synaptic functions. The pattern recognition capability of the designed artificial neural network is believed to provide an excellent paradigm for ultra-sensitive van der Waals materials to develop electric-modulated neuromorphic computation architectures.

Developing efficient memory and artificial synaptic systems based on environmentally sensitive van der Waals materials remains a challenge. Here, the authors present a native oxidation-inspired InSe field-effect transistor that benefits from a boosted charge trapping behavior under ambient conditions.

Bioinspired bio-voltage memristors

Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens, that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated.

Multifunctional Polymer Memory via Bi-Interfacial Topography for Pressure Perception Recognition

Emerging memory devices, that can provide programmable information recording with tunable resistive switching under external stimuli, hold great potential for applications in data storage, logic circuits, and artificial synapses. Realization of multifunctional manipulation within individual memory devices is particularly important in the More-than-Moore era, yet remains a challenge. Here, both rewritable and nonerasable memory are demonstrated in a single stimuli-responsive polymer diode, based on a nanohole-nanowrinkle bi-interfacial structure. Such synergic nanostructure is constructed from interfacing a nanowrinkled bottom graphene electrode and top polymer matrix with nanoholes; and it can be easily prepared by spin coating, which is a low-cost and high-yield production method. Furthermore, the resulting device, with ternary and low-power operation under varied external stimuli, can enable both reversible and irreversible biomimetic pressure recognition memories using a device-to-system framework. This work offers both a general guideline to fabricate multifunctional memory devices via interfacial nanostructure engineering and a smart information storage basis for future artificial intelligence.

Artificial Sensory Memory

Sensory memory, formed at the beginning while perceiving and interacting with the environment, is considered a primary source of intelligence. Transferring such biological concepts into electronic implementation aims at achieving perceptual intelligence, which would profoundly advance a broad spectrum of applications, such as prosthetics, robotics, and cyborg systems. Here, the recent developments in the design and fabrication of artificial sensory memory devices are summarized and their applications in recognition, manipulation, and learning are highlighted. The emergence of such devices benefits from recent progress in both bioinspired sensing and neuromorphic engineering technologies and derives from abundant inspiration and benchmarks from an improved understanding of biological sensory processing. Increasing attention to this area would offer unprecedented opportunities toward new hardware architecture of artificial intelligence, which could extend the capabilities of digital systems with emotional/psychological attributes. Pending challenges are also addressed to aspects such as integration level, energy efficiency, and functionality, which would undoubtedly shed light on the future development of translational implementations.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

Thermal Stability of π-Conjugated n-Ethylene-Glycol-Terminated Quaterthiophene Oligomers: A Computational and Experimental Study

This work represents a joint computational and experimental study on a series of n-ethylene glycol (PEOn)-terminated quaterthiophene (4T) oligomers for 1 < n < 10 to elucidate their self-assembly behavior into a smectic-like lamellar phase. This study builds on an earlier study for n = 4 that showed that our model predictions were consistent with experimental data on the melting behavior and structure of the lamellar phase, with the latter consisting of crystal-like 4T domains and liquid-like PEO4 domains. The present study aims to understand how the length of the terminal PEOn chains modulates the disordering temperature of the lamellar phase and hence the relative stability of the ordered structure. A simplified bilayer model, where the 4T domains are not explicitly described, is put forward to efficiently estimate the disordering effect of the PEO domains with increasing n; this method is first validated by correctly predicting that layers of alkyl (PE)-capped 4T oligomers (for 1 < n < 10) stay ordered at room temperature. Both 4T-domain implicit and explicit model simulations reveal that the order-disorder temperature decreases with the length of the PEO capping chains, as the associated increase in conformational entropy drives a tendency toward disorder that overtakes the cohesive energy, keeping the ordered packing of the 4T domains.