Organic neuromorphic electronics for sensorimotor integration and learning in robotics

Increasing the stability of electrolyte-gated organic synaptic transistors for neuromorphic implants

Electrolyte-gated organic synaptic transistors (EGOSTs) can have versatile synaptic plasticity in a single device, so they are promising as components of neuromorphic implants that are intended for use in neuroprosthetic electronic nerves that are energy-efficient and have simple system structure. With the advancement in transistor properties of EGOSTs, the commercialization of neuromorphic implants for practical long-term use requires consistent operation, so they must be stable in vivo. This requirement demands strategies that maintain electronic and ionic transport in the devices while implanted in the human body, and that are mechanically, environmentally, and operationally stable. Here, we cover the structure, working mechanisms, and electrical responses of EGOSTs. We then focus on strategies to ensure their stability to maintain these characteristics and prevent adverse effects on biological tissues. We also highlight state-of-the-art neuromorphic implants that incorporate these strategies. We conclude by presenting a perspective on improvements that are needed in EGOSTs to develop practical, neuromorphic implants that are long-term useable.

Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot

The emulation of tactile sensory nerves to achieve advanced sensory functions in robotics with artificial intelligence is of great interest. However, such devices remain bulky and lack reliable competence to functionalize further synaptic devices with proprioceptive feedback. Here, we report an artificial organic afferent nerve with low operating bias (-0.6 V) achieved by integrating a pressure-activated organic electrochemical synaptic transistor and artificial mechanoreceptors. The dendritic integration function for neurorobotics is achieved to perceive directional movement of object, further reducing the control complexity by exploiting the distributed and parallel networks. An intelligent robot assembled with artificial afferent nerve, coupled with a closed-loop feedback program is demonstrated to rapidly implement slip recognition and prevention actions upon occurrence of object slippage. The spatiotemporal features of tactile patterns are well differentiated with a high recognition accuracy after processing spike-encoded signals with deep learning model. This work represents a breakthrough in mimicking synaptic behaviors, which is essential for next-generation intelligent neurorobotics and low-power biomimetic electronics.

Bio-Inspired Organic Synaptor with In Situ Ion-Doped Ultrathin Polyelectrolyte Containing Acetylcholine-Like Cation

An ion-based synaptic transistor (synaptor) is designed to emulate a biological synapse using controlled ion movements. However, developing a solid-state electrolyte that can facilitate ion movement while achieving large-scale integration remains challenging. Here, a bio-inspired organic synaptor (BioSyn) with an in situ ion-doped polyelectrolyte (i-IDOPE) is demonstrated. At the molecular scale, a polyelectrolyte containing the tert-amine cation, inspired by the neurotransmitter acetylcholine is synthesized using initiated chemical vapor deposition (iCVD) with in situ doping, a one-step vapor-phase deposition used to fabricate solid-state electrolytes. This method results in an ultrathin, but highly uniform and conformal solid-state electrolyte layer compatible with large-scale integration, a form that is not previously attainable. At a synapse scale, synapse functionality is replicated, including short-term and long-term synaptic plasticity (STSP and LTSP), along with a transformation from STSP to LTSP regulated by pre-synaptic voltage spikes. On a system scale, a reflex in a peripheral nervous system is mimicked by mounting the BioSyns on various substrates such as rigid glass, flexible polyethylene naphthalate, and stretchable poly(styrene-ethylene-butylene-styrene) for a decentralized processing unit. Finally, a classification accuracy of 90.6% is achieved through semi-empirical simulations of MNIST pattern recognition, incorporating the measured LTSP characteristics from the BioSyns.

Predicting Blood Glucose Levels with Organic Neuromorphic Micro-Networks

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low-power hardware. Organic polymer-based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software-based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in-body computation of glucose prediction is investigated.

Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics

Organic artificial neurons operating in liquid environments are crucial components in neuromorphic bioelectronics. However, the current understanding of these neurons is limited, hindering their rational design and development for realistic neuronal emulation in biological settings. Here we combine experiments, numerical non-linear simulations, and analytical tools to unravel the operation of organic artificial neurons. This comprehensive approach elucidates a broad spectrum of biorealistic behaviors, including firing properties, excitability, wetware operation, and biohybrid integration. The non-linear simulations are grounded in a physics-based framework, accounting for ion type and ion concentration in the electrolytic medium, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link the neurons spiking features with material and physical parameters, bridging closer the domains of artificial neurons and neuroscience. This work provides streamlined and transferable guidelines for the design, development, engineering, and optimization of organic artificial neurons, advancing next generation neuronal networks, neuromorphic electronics, and bioelectronics.

An organic brain-inspired platform with neurotransmitter closed-loop control, actuation and reinforcement learning

Organic neuromorphic platforms have recently received growing interest for the implementation and integration of artificial and hybrid neuronal networks. Here, achieving closed-loop and learning/training processes as in the human brain is still a major challenge especially exploiting time-dependent biosignalling such as neurotransmitter release. Here, we present an integrated organic platform capable of cooperating with standard silicon technologies, to achieve brain-inspired computing via adaptive synaptic potentiation and depression, in a closed-loop fashion. The microfabricated platform could be interfaced and control a robotic hand which ultimately was able to learn the grasping of differently sized objects, autonomously.

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.

Toward Intelligent Display with Neuromorphic Technology

In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.

Broadband Photodetectors and Imagers in Stretchable Electronics Packaging

The integration of flexible electronics with optics can help realize a powerful tool that facilitates the creation of a smart society wherein internal evaluations can be easily performed nondestructively from the surface of various objects that is used or encountered in daily lives. Here, organic-material-based stretchable optical sensors and imagers that possess both bending capability and rubber-like elasticity are reviewed. The latest trends in nondestructive evaluation equipment that enable simple on-site evaluations of health conditions and abnormalities are discussed without subjecting the targeted living bodies and various objects to mechanical stress. Real-time performance under real-life conditions is becoming increasingly important for creating smart societies interwoven with optical technologies. In particular, the terahertz (THz)-wave region offers a substance- and state-specific fingerprint spectrum that enables instantaneous analyses. However, to make THz sensors accessible, the following issues must be addressed: broadband and high-sensitivity at room temperature, stretchability to follow the surface movements of targets, and digital transformation compatibility. The materials, electronics packaging, and remote imaging systems used to overcome these issues are discussed in detail. Ultimately, stretchable optical sensors and imagers with highly sensitive and broadband THz sensors can facilitate the multifaceted on-site evaluation of solids, liquids, and gases.

Tuning Gate Potential Profiles and Current-Voltage Characteristics of Polymer Electrolyte-Gated Transistors by Capacitance Engineering

We demonstrate that the transfer characteristics of electrolyte-gated transistors (EGTs) with polythiophene semiconductor channels are a strong function of gate/electrolyte interfacial contact area, i.e., gate size. Polythiophene EGTs with gate/electrolyte areas much larger than the channel/electrolyte areas show a clear peak in the drain current vs gate voltage (ID-VG) behavior, as well as peak voltage hysteresis between the forward and reverse VG sweeps. Polythiophene EGTs with small gate/electrolyte areas, on the other hand, exhibit current plateaus in the ID-VG behavior and a gate-size-dependent hysteresis loop between turn on and off. The qualitatively different transport behaviors are attributed to the relative sizes of the gate/electrolyte and channel/electrolyte interface capacitances, which are proportional to interfacial area. These interfacial capacitances are in series with each other such that the total capacitance of the full gate/electrolyte/channel stack is dominated by the interface with the smallest capacitance or area. For EGTs with large gates, most of the applied VG is dropped at the channel/electrolyte interface, leading to very high charge accumulations, up to ∼0.3 holes per ring (hpr) in the case of polythiophene semiconductors. The large charge density results in sub-band-filling and a marked decrease in hole mobility, giving rise to the peak in ID-VG. For EGTs with small gates, hole accumulation saturates near 0.15 hpr, band-filling does not occur, and hole mobility is maintained at a fixed value, which leads to the ID plateau. Potential drops at the interfaces are confirmed by in situ potential measurements inside a gate/electrolyte/polymer semiconductor stack. Hole accumulations are measured with gate current-gate voltage (IG-VG) measurements acquired simultaneously with the ID-VG characteristics. Overall, our measurements demonstrate that remarkably different ID behavior can be obtained for polythiophene EGTs by controlling the magnitude of the gate-electrolyte interfacial capacitance.

A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways

Signal communication mechanisms within the human body rely on the transmission and modulation of action potentials. Replicating the interdependent functions of receptors, neurons and synapses with organic artificial neurons and biohybrid synapses is an essential first step towards merging neuromorphic circuits and biological systems, crucial for computing at the biological interface. However, most organic neuromorphic systems are based on simple circuits which exhibit limited adaptability to both external and internal biological cues, and are restricted to emulate only specific the functions of an individual neuron/synapse. Here, we present a modular neuromorphic system which combines organic spiking neurons and biohybrid synapses to replicate a neural pathway. The spiking neuron mimics the sensory coding function of afferent neurons from light stimuli, while the neuromodulatory activity of interneurons is emulated by neurotransmitters-mediated biohybrid synapses. Combining these functions, we create a modular connection between multiple neurons to establish a pre-processing retinal pathway primitive.

A Study of the Drift Phenomena of Gate-Functionalized Biosensors and Dual-Gate-Functionalized Biosensors in Human Serum

In this paper, we study the drift behavior of organic electrochemical transistor (OECT) biosensors in a phosphate-buffered saline (PBS) buffer solution and human serum. Theoretical and experimental methods are illustrated in this paper to understand the origin of the drift phenomenon and the mechanism of ion diffusion in the sensing layer. The drift phenomenon is explained using a first-order kinetic model of ion adsorption into the gate material and shows very good agreement with experimental data on drift in OECTs. We show that the temporal current drift can be largely mitigated using a dual-gate OECT architecture and that dual-gate-based biosensors can increase the accuracy and sensitivity of immuno-biosensors compared to a standard single-gate design. Specific binding can be detected at a relatively low limit of detection, even in human serum.

Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents

The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the software-hardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a brain-inspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction.

Spiers Memorial Lecture: Challenges and prospects in organic photonics and electronics

While a substantial amount of research activity has been conducted in fields related to organic photonics and electronics, including the development of devices such as organic field-effect transistors, organic photovoltaics, and organic light-emitting diodes for applications encompassing organic thermoelectrics, organic batteries, excitonic organic materials for photochemical and optoelectronic applications, and organic thermoelectrics, this perspective review will primarily concentrate on the emerging and rapidly expanding domain of organic bioelectronics and neuromorphics. Here we present the most recent research findings on organic transistors capable of sensing biological biomarkers down at the single-molecule level (i.e., oncoproteins, genomes, etc.) for the early diagnosis of pathological states and to mimic biological synapses, paving the way to neuromorphic applications that surpass the limitations of the traditional von Neumann computing architecture. Both organic bioelectronics and neuromorphics exhibit several challenges but will revolutionize human life, considering the development of artificial synapses to counteract neurodegenerative disorders and the development of ultrasensitive biosensors for the early diagnosis of cancer to prevent its development. Moreover, organic bioelectronics for sensing applications have also triggered the development of several wearable, flexible and stretchable biodevices for continuous biomarker monitoring.

A retinomorphic neuron for artificial vision and iris accommodation

The iris of an eye automatically optimizes the amount of light that strikes the retina by accommodating the intensity of ambient light. Here, we describe a retinomorphic neuron using neuromorphic photoreceptors for artificial vision and iris accommodation that mimics the biological structure and processing functions of retinal neurons for light sensing and signal transduction. The system consists of a neuromorphic photoreceptor, an electrochromic device as a light filter, and a spike-generation unit. In particular, the Au nanoparticle (NP) decorated ITO fiber photoreceptor with a well-aligned array structure is able to rely on its own light-tunable synaptic plasticity and the plasmon-enhanced light absorption. Therefore, it allows real-time feedback about light intensity, emits a higher-frequency electrical stimulus to stronger light, flash, or prolonged light illumination time, and drives the electrochromic filter to work, allowing mild light to pass through. Compared with traditional artificial irises or artificial photoreceptors, our design introduces neural pathways and neuromorphic devices, which are closer to biological functions in simulation. To our knowledge, this is the first time that a retinal neuron with neuromorphic photoreceptors has been used for artificial iris vision. Furthermore, we demonstrate direct and consensual pupillary light reflexes. The design of artificial iris vision has potential applications in biomimetic engineering, smart interaction, and visual prostheses.

Unveiling the capabilities of bipolar conical channels in neuromorphic iontronics

Conical channels filled with an aqueous electrolyte have been proposed as promising candidates for iontronic neuromorphic circuits. This is facilitated by a novel analytical model for the internal channel dynamics [T. M. Kamsma, W. Q. Boon, T. ter Rele, C. Spitoni and R. van Roij, Phys. Rev. Lett., 2023, 130(26), 268401], the relative ease of fabrication of conical channels, and the wide range of achievable memory retention times by varying the channel lengths. In this work, we demonstrate that the analytical model for conical channels can be generalized to channels with an inhomogeneous surface charge distribution, which we predict to exhibit significantly stronger current rectification and more pronounced memristive properties in the case of bipolar channels, i.e. channels where the tip and base carry a surface charge of opposite sign. Additionally, we show that the use of bipolar conical channels in a previously proposed iontronic circuit features hallmarks of neuronal communication, such as all-or-none action potentials and spike train generation. Bipolar channels allow, however, for circuit parameters in the range of their biological analogues, and exhibit membrane potentials that match well with biological mammalian action potentials, further supporting their potential biocompatibility.

Physically intelligent autonomous soft robotic maze escaper

Autonomous maze navigation is appealing yet challenging in soft robotics for exploring priori unknown unstructured environments, as it often requires human-like brain that integrates onboard power, sensors, and control for computational intelligence. Here, we report harnessing both geometric and materials intelligence in liquid crystal elastomer-based self-rolling robots for autonomous escaping from complex multichannel mazes without the need for human-like brain. The soft robot powered by environmental thermal energy has asymmetric geometry with hybrid twisted and helical shapes on two ends. Such geometric asymmetry enables built-in active and sustained self-turning capabilities, unlike its symmetric counterparts in either twisted or helical shapes that only demonstrate transient self-turning through untwisting. Combining self-snapping for motion reflection, it shows unique curved zigzag paths to avoid entrapment in its counterparts, which allows for successful self-escaping from various challenging mazes, including mazes on granular terrains, mazes with narrow gaps, and even mazes with in situ changing layouts.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

ECRAM Materials, Devices, Circuits and Architectures: A Perspective

Non-von-Neumann computing using neuromorphic systems based on two-terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the "write" and "read" operations using a "gate" electrode to tune the conductance state through charge-transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in-depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low-power neuromorphic computing are discussed.

Bio-inspired artificial synaptic transistors: evolution from innovative basic units to system integration

The investigation of transistor-based artificial synapses in bioinspired information processing is undergoing booming exploration, and is the stable building block for brain-like computing. Given that the storage and computing separation architecture of von Neumann construction is not conducive to the current explosive information processing, it is critical to accelerate the connection between hardware systems and software simulations of intelligent synapses. So far, various works based on a transistor-based synaptic system successfully simulated functions similar to biological nerves in the human brain. However, the influence of the semiconductor and the device structural design on synaptic properties is still poorly linked. This review concretely emphasizes the recent advances in the novel structure design of semiconductor materials and devices used in synaptic transistors, not only from a single multifunction synaptic device but also to system application with various connected routes and related working mechanisms. Finally, crises and opportunities in transistor-based synaptic interconnection are discussed and predicted.

Iontronic Neuromorphic Signaling with Conical Microfluidic Memristors

Experiments have shown that the conductance of conical channels, filled with an aqueous electrolyte, can strongly depend on the history of the applied voltage. These channels hence have a memory and are promising elements in brain-inspired (iontronic) circuits. We show here that the memory of such channels stems from transient concentration polarization over the ionic diffusion time. We derive an analytic approximation for these dynamics which shows good agreement with full finite-element calculations. Using our analytic approximation, we propose an experimentally realizable Hodgkin-Huxley iontronic circuit where micrometer cones take on the role of sodium and potassium channels. Our proposed circuit exhibits key features of neuronal communication such as all-or-none action potentials upon a pulse stimulus and a spike train upon a sustained stimulus.

Molecularly Hybridized Conduction in DPP-Based Donor-Acceptor Copolymers toward High-Performance Iono-Electronics

Iono-electronics, that is, transducing devices able to translate ionic injection into electrical output, continue to demand a variety of mixed ionic-electronic conductors (MIECs). Though polar sidechains are widely used in designing novel polymer MIECs, it remains unclear to chemists how much balance is needed between the two antagonistic modes of transport (ion permeability and electronic charge transport) to yield high-performance materials. Here, the impact of molecularly hybridizing ion permeability and charge mobility in semiconducting polymers on their performance in electrochemical and synaptic transistors is investigated. A series of diketopyrrolopyrrole (DPP)-based copolymers are employed to demonstrate the multifunctionality attained by controlling the density of polar sidechains along the backbone. Notably, efficient electrochemical signal transduction and reliable synaptic plasticity are demonstrated via controlled ion insertion and retention. The newly designed DPP-based copolymers further demonstrate unprecedented thermal tolerance among organic mixed ionic-electronic conductors, a key property in the manufacturing of organic electronics.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Pseudo-transistors for emerging neuromorphic electronics

Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics. Although two-terminal memristors and three-terminal synaptic transistors have exhibited significant capabilities in the artificial synapse, more stable devices and simpler integration are needed in practical applications. Combining the configuration advantages of memristors and transistors, a novel pseudo-transistor is proposed. Here, recent advances in the development of pseudo-transistor-based neuromorphic electronics in recent years are reviewed. The working mechanisms, device structures and materials of three typical pseudo-transistors, including tunneling random access memory (TRAM), memflash and memtransistor, are comprehensively discussed. Finally, the future development and challenges in this field are emphasized.

In-Memory Tactile Sensor with Tunable Steep-Slope Region for Low-Artifact and Real-Time Perception of Mechanical Signals

A tactile sensor needs to perceive static pressures and dynamic forces in real-time with high accuracy for early diagnosis of diseases and development of intelligent medical prosthetics. However, biomechanical and external mechanical signals are always aliased (including variable physiological and pathological events and motion artifacts), bringing great challenges to precise identification of the signals of interest (SOI). Although the existing signal segmentation methods can extract SOI and remove artifacts by blind source separation and/or additional filters, they may restrict the recognizable patterns of the device, and even cause signal distortion. Herein, an in-memory tactile sensor (IMT) with a dynamically adjustable steep-slope region (SSR) and nanocavity-induced nonvolatility (retention time >1000 s) is proposed on the basis of a machano-gated transistor, which directly transduces the tactile stimuli to various dope states of the channel. The programmable SSR endows the sensor with a critical window of responsiveness, realizing the perception of signals on demand. Owing to the nonvolatility of the sensor, the mapping of mechanical cues with high spatiotemporal accuracy and associative learning between two physical inputs are realized, contributing to the accurate assessment of the tissue health status and ultralow-power (about 25.1 μW) identification of an occasionally occurring tremor.

Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning

In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.

Large-scale, closed-loop interrogation of neural circuits underlying cognition

Cognitive functions are increasingly understood to involve coordinated activity patterns between multiple brain regions, and their disruption by neuropsychiatric disorders is similarly complex. Closed-loop neurostimulation can directly modulate neural signals with temporal and spatial precision. How to leverage such an approach to effectively identify and target distributed neural networks implicated in mediating cognition remains unclear. We review current conceptual and technical advances in this area, proposing that devices that enable large-scale acquisition, integrated processing, and multiregion, arbitrary waveform stimulation will be critical for mechanistically driven manipulation of cognitive processes in physiological and pathological brain networks.

The present and future of neural interfaces

The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.

Nanofloating gate modulated synaptic organic light-emitting transistors for reconfigurable displays

The use of postsynaptic current to drive long-lasting luminescence holds a disruptive potential for harnessing the next-generation of smart displays. Multiresponsive long afterglow emission can be achieved by integrating light-emitting polymers in electric spiked transistors trigged by distinct presynaptic signals inputs. Here, we report a highly effective electric spiked long afterglow organic light-emitting transistor (LAOLET), whose operation relies on a nanofloating gate architecture. Long afterglow emission with reconfigurable brightness and retention time is observed upon applying specific positive gate voltage spiked. Conversely, when negative gate voltage stimulus is applied, these LAOLETs function as click-on display. Interestingly, upon endowing the device with force sensing capabilities, it can operate as a long afterglow pressure sensor that emits long-lasting green light subsequently to a controlled extrusion action.

High-Performance Bioelectronic Circuits Integrated on Biodegradable and Compostable Substrates with Fully Printed Mask-Less Organic Electrochemical Transistors

Organic electrochemical transistors (OECTs) rely on volumetric ion-modulation of the electronic current to provide low-voltage operation, large signal amplification, enhanced sensing capabilities, and seamless integration with biology. The majority of current OECT technologies require multistep photolithographic microfabrication methods on glass or plastic substrates, which do not provide an ideal path toward ultralow cost ubiquitous and sustainable electronics and bioelectronics. At the same time, the development of advanced bioelectronic circuits combining bio-detection, amplification, and local processing functionalities urgently demand for OECT technology platforms with a monolithic integration of high-performance iontronic circuits and sensors. Here, fully printed mask-less OECTs fabricated on thin-film biodegradable and compostable substrates are proposed. The dispensing and capillary printing methods are used for depositing both high- and low-viscosity OECT materials. Fully printed OECT unipolar inverter circuits with a gain normalized to the supply voltage as high as 136.6 V^-1 , and current-driven sensors for ion detection and real-time monitoring with a sensitivity of up to 506 mV dec^-1 , are integrated on biodegradable and compostable substrates. These universal building blocks with the top-performance ever reported demonstrate the effectiveness of the proposed approach and can open opportunities for next-generation high-performance sustainable bioelectronics.

Neuro-inspired electronic skin for robots

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

Sub-3 V, MHz-Class Electrolyte-Gated Transistors and Inverters

Electrolyte-gated transistors (EGTs) have emerging applications in physiological recording, neuromorphic computing, sensing, and flexible printed electronics. A challenge for these devices is their slow switching speed, which has several causes. Here, we report the fabrication and characterization of n-type ZnO-based EGTs with signal propagation delays as short as 70 ns. Propagation delays are assessed in dynamically operating inverters and five-stage ring oscillators as a function of channel dimensions and supply voltages up to 3 V. Substantial decreases in switching time are realized by minimizing parasitic resistances and capacitances that are associated with the electrolyte in these devices. Stable switching at 1-10 MHz is achieved in individual inverter stages with 10-40 μm channel lengths, and analysis suggests that further improvements are possible.

Adaptive Biosensing and Neuromorphic Classification Based on an Ambipolar Organic Mixed Ionic-Electronic Conductor

Organic mixed ionic-electronic conductors (OMIECs) are central to bioelectronic applications such as biosensors, health-monitoring devices, and neural interfaces, and have facilitated efficient next-generation brain-inspired computing and biohybrid systems. Despite these examples, smart and adaptive circuits that can locally process and optimize biosignals have not yet been realized. Here, a tunable sensing circuit is shown that can locally modulate biologically relevant signals like electromyograms (EMGs) and electrocardiograms (ECGs), that is based on a complementary logic inverter combined with a neuromorphic memory element, and that is constructed from a single polymer mixed conductor. It is demonstrated that a small neuromorphic array based on this material effects high classification accuracy in heartbeat anomaly detection. This high-performance material allows for straightforward monolithic integration, which reduces fabrication complexity while also achieving high on/off ratios with excellent ambient p- and n-type stability in transistor performance. This material opens a route toward simple and straightforward fabrication and integration of more sophisticated adaptive circuits for future smart bioelectronics.