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

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.

Bistable organic electrochemical transistors: enthalpy vs. entropy

Organic electrochemical transistors (OECTs) underpin a range of emerging technologies, from bioelectronics to neuromorphic computing, owing to their unique coupling of electronic and ionic charge carriers. In this context, various OECT systems exhibit significant hysteresis in their transfer curve, which is frequently leveraged to achieve non-volatility. Meanwhile, a general understanding of its physical origin is missing. Here, we introduce a thermodynamic framework that readily explains the emergence of bistable OECT operation via the interplay of enthalpy and entropy. We validate this model through temperature-resolved characterizations, material manipulation, and thermal imaging. Further, we reveal deviations from Boltzmann statistics for the subthreshold swing and reinterpret existing literature. Capitalizing on these findings, we finally demonstrate a single-OECT Schmitt trigger, thus compacting a multi-component circuit into a single device. These insights provide a fundamental advance for OECT physics and its application in non-conventional computing, where symmetry-breaking phenomena are pivotal to unlock new paradigms of information processing.

Stable n-Type Perylene Derivative Ladder Polymer with Antiambipolarity for Electrically Reconfigurable Organic Logic Gates

Organic electrochemical transistors (OECTs) are one of the promising building blocks to realize next-generation bioelectronics. To date, however, the performance and signal processing capabilities of these devices remain limited by their stability and speed. Herein, the authors demonstrate stable and fast n-type organic electrochemical transistors based on a side-chain-free ladder polymer, poly(benzimidazoanthradiisoquinolinedione). The device demonstrated fast normalized transient speed of 0.56 ± 0.17 ms um-2 and excellent long-term stability in aqueous electrolytes, with no significant drop in its doping current after 50 000 successive doping/dedoping cycles and 2-month storage at ambient conditions. These unique characteristics make this polymer especially suitable for bioelectronics, such as being used as a pull-down channel in a complementary inverter for long-term stable detection of electrophysiological signals. Moreover, the developed device shows a reversible anti-ambipolar behavior, enabling reconfigurable electronics to be realized using a single material. These results go beyond the conventional OECT and demonstrate the potential of OECTs to exhibit dynamically configurable functionalities for next-generation reconfigurable electronics.

SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature

1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.

A Photoelectrochemical Retinomorphic Synapse

Reproducing human visual functions with artificial devices is a long-standing goal of the neuromorphic domain. However, emulating the chemical language communication of the visual system in fluids remains a grand challenge. Here, a "multi-color" hydrogel-based photoelectrochemical retinomorphic synapse is reported with unique chemical-ionic-electrical signaling in an aqueous electrolyte that enables, e.g., color perception and biomolecule-mediated synaptic plasticity. Based on the specific enzyme-catalyzed chromogenic reactions, three multifunctional colored hydrogels are developed, which can not only synergize with the Bi2S3 photogate to recognize the primary colors but also synergize with a given polymeric channel to promote the long-term memory of the system. A synaptic array is further constructed for sensing color images and biomolecule-coded information communication. Taking advantage of the versatile biochemistry, the biochemical-driven reversible photoelectric response of the cone cell is further mimicked. This work introduces rich chemical designs into retinomorphic devices, providing a perspective for replicating the human visual system in fluids.

Tunable anti-ambipolar vertical bilayer organic electrochemical transistor enable neuromorphic retinal pathway

Increasing demand for bio-interfaced human-machine interfaces propels the development of organic neuromorphic electronics with small form factors leveraging both ionic and electronic processes. Ion-based organic electrochemical transistors (OECTs) showing anti-ambipolarity (OFF-ON-OFF states) reduce the complexity and size of bio-realistic Hodgkin-Huxley(HH) spiking circuits and logic circuits. However, limited stable anti-ambipolar organic materials prevent the design of integrated, tunable, and multifunctional neuromorphic and logic-based systems. In this work, a general approach for tuning anti-ambipolar characteristics is presented through assembly of a p-n bilayer in a vertical OECT (vOECT) architecture. The vertical OECT design reduces device footprint, while the bilayer material tuning controls the anti-ambipolarity characteristics, allowing control of the device's on and off threshold voltages, and peak position, while reducing size thereby enabling tunable threshold spiking neurons and logic gates. Combining these components, a mimic of the retinal pathway reproducing the wavelength and light intensity encoding of horizontal cells to spiking retinal ganglion cells is demonstrated. This work enables further incorporation of conformable and adaptive OECT electronics into biointegrated devices featuring sensory coding through parallel processing for diverse artificial intelligence and computing applications.

Artificial Multimodal Neuron with Associative Learning Capabilities: Acquisition, Extinction, and Spontaneous Recovery

Associative multimodal artificial intelligence (AMAI) has gained significant attention across various fields, yet its implementation poses challenges due to the burden on computing and memory resources. To address these challenges, researchers have paid increasing attention to neuromorphic devices based on novel materials and structures, which can implement classical conditioning behaviors with simplified circuitry. Herein, we introduce an artificial multimodal neuron device that shows not only the acquisition behavior but also the extinction and the spontaneous recovery behaviors for the first time. Being composed of an ovonic threshold switch (OTS)-based neuron device, a conductive bridge memristor (CBM)-based synapse device, and a few passive electrical elements, such observed behaviors of this neuron device are explained in terms of the electroforming and the diffusion of metallic ions in the CBM. We believe that the proposed associative learning neuron device will shed light on the way of developing large-scale AMAI systems by providing inspiration to devise an associative learning network with improved energy efficiency.

Transient Response and Ionic Dynamics in Organic Electrochemical Transistors

The rapid development of organic electrochemical transistors (OECTs) has ushered in a new era in organic electronics, distinguishing itself through its application in a variety of domains, from high-speed logic circuits to sensitive biosensors, and neuromorphic devices like artificial synapses and organic electrochemical random-access memories. Despite recent strides in enhancing OECT performance, driven by the demand for superior transient response capabilities, a comprehensive understanding of the complex interplay between charge and ion transport, alongside electron-ion interactions, as well as the optimization strategies, remains elusive. This review aims to bridge this gap by providing a systematic overview on the fundamental working principles of OECT transient responses, emphasizing advancements in device physics and optimization approaches. We review the critical aspect of transient ion dynamics in both volatile and non-volatile applications, as well as the impact of materials, morphology, device structure strategies on optimizing transient responses. This paper not only offers a detailed overview of the current state of the art, but also identifies promising avenues for future research, aiming to drive future performance advancements in diversified applications.

Advanced Neuromorphic Applications Enabled by Synaptic Ion-Gating Vertical Transistors

Bioinspired synaptic devices have shown great potential in artificial intelligence and neuromorphic electronics. Low energy consumption, multi-modal sensing and recording, and multifunctional integration are critical aspects limiting their applications. Recently, a new synaptic device architecture, the ion-gating vertical transistor (IGVT), has been successfully realized and timely applied to perform brain-like perception, such as artificial vision, touch, taste, and hearing. In this short time, IGVTs have already achieved faster data processing speeds and more promising memory capabilities than many conventional neuromorphic devices, even while operating at lower voltages and consuming less power. This work focuses on the cutting-edge progress of IGVT technology, from outstanding fabrication strategies to the design and realization of low-voltage multi-sensing IGVTs for artificial-synapse applications. The fundamental concepts of artificial synaptic IGVTs, such as signal processing, transduction, plasticity, and multi-stimulus perception are discussed comprehensively. The contribution draws special attention to the development and optimization of multi-modal flexible sensor technologies and presents a roadmap for future high-end theoretical and experimental advancements in neuromorphic research that are mostly achievable by the synaptic IGVTs.

PEDOT:PSS-Based Prolonged Long-Term Decay Synaptic OECT with Proton-Permeable Material, Nafion

Poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), a conductive polymer, has gained popularity as the channel layer in organic electrochemical transistors (OECTs) due to its high conductivity and straightforward processing. However, difficulties arise in controlling its conductivity through gate voltage, presenting a challenge. To address this issue, aromatic amidine base, diazabicyclo[4.3.0]non-5-ene (DBN), is used to stabilize the doping state of the PEDOT chain through a reliable chemical de-doping process. Furthermore, the addition of the proton-penetrable material Nafion to the PEDOT:PSS channel layer induces phase separation between the substances. By utilizing a solution containing both PEDOT:PSS and Nafion as the channel layer of OECTs, the efficiency of ion movement into the channel from the electrolyte is enhanced, resulting in improved OECT performance. The inclusion of Nafion in the OECTs' channel layer modifies ion movement dynamics, allowing for the adjustment of synaptic properties such as pulse-paired facilitation, memory level, short-term plasticity, and long-term plasticity. This research aims to introduce new possibilities in the field of neuromorphic computing and contribute to biomimetic technology through the enhancement of electronic component performance.

Information Transmission through Biotic-Abiotic Interfaces to Restore or Enhance Human Function

Advancements in reliable information transfer across biotic-abiotic interfaces have enabled the restoration of lost human function. For example, communication between neuronal cells and electrical devices restores the ability to walk to a tetraplegic patient and vision to patients blinded by retinal disease. These impactful medical achievements are aided by tailored biotic-abiotic interfaces that maximize information transfer fidelity by considering the physical properties of the underlying biological and synthetic components. This Review develops a modular framework to define and describe the engineering of biotic and abiotic components as well as the design of interfaces to facilitate biotic-abiotic information transfer using light or electricity. Delineating the properties of the biotic, interface, and abiotic components that enable communication can serve as a guide for future research in this highly interdisciplinary field. Application of synthetic biology to engineer light-sensitive proteins has facilitated the control of neural signaling and the restoration of rudimentary vision after retinal blindness. Electrophysiological methodologies that use brain-computer interfaces and stimulating implants to bypass spinal column injuries have led to the rehabilitation of limb movement and walking ability. Cellular interfacing methodologies and on-chip learning capability have been made possible by organic transistors that mimic the information processing capacity of neurons. The collaboration of molecular biologists, material scientists, and electrical engineers in the emerging field of biotic-abiotic interfacing will lead to the development of prosthetics capable of responding to thought and experiencing touch sensation via direct integration into the human nervous system. Further interdisciplinary research will improve electrical and optical interfacing technologies for the restoration of vision, offering greater visual acuity and potentially color vision in the near future.

A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices

Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices.

One-Shot Remote Integration of Macromolecular Synaptic Elements on a Chip for Ultrathin Flexible Neural Network System

The field of biomimetic electronics that mimic synaptic functions has expanded significantly to overcome the limitations of the von Neumann bottleneck. However, the scaling down of the technology has led to an increasingly intricate manufacturing process. To address the issue, this work presents a one-shot integrable electropolymerization (OSIEP) method with remote controllability for the deposition of synaptic elements on a chip by exploiting bipolar electrochemistry. Condensing synthesis, deposition, and patterning into a single fabrication step is achieved by combining alternating-current voltage superimposed on direct-current voltage-bipolar electropolymerization and a specially designed dual source/drain bipolar electrodes. As a result, uniform 6 × 5 arrays of poly(3,4-ethylenedioxythiophene) channels are successfully fabricated on flexible ultrathin parylene substrates in one-shot process. The channels exhibited highly uniform characteristics and are directly used as electrochemical synaptic transistor with synaptic plasticity over 100 s. The synaptic transistors have demonstrated promising performance in an artificial neural network (NN) simulation, achieving a high recognition accuracy of 95.20%. Additionally, the array of synaptic transistor is easily reconfigured to a multi-gate synaptic circuit to implement the principles of operant conditioning. These results provide a compelling fabrication strategy for realizing cost-effective and disposable NN systems with high integration density.

All-Photolithography Fabrication of Ion-Gated Flexible Organic Transistor Array for Multimode Neuromorphic Computing

Organic ion-gated transistors (OIGTs) demonstrate commendable performance for versatile neuromorphic systems. However, due to the fragility of organic materials to organic solvents, efficient and reliable all-photolithography methods for scalable manufacturing of high-density OIGT arrays with multimode neuromorphic functions are still missing, especially when all active layers are patterned in high-density. Here, a flexible high-density (9662 devices per cm2) OIGT array with high yield and minimal device-to-device variation is fabricated by a modified all-photolithography method. The unencapsulated flexible array can withstand 1000 times' bending at a radius of 1 mm, and 3 months' storage test in air, without obvious performance degradation. More interesting, the OIGTs can be configured between volatile and nonvolatile modes, suitable for constructing reservoir computing systems to achieve high accuracy in classifying handwritten digits with low training costs. This work proposes a promising design of organic and flexible electronics for affordable neuromorphic systems, encompassing both array and algorithm aspects.

Bridging length scales in organic mixed ionic-electronic conductors through internal strain and mesoscale dynamics

Understanding the structural and dynamic properties of disordered systems at the mesoscale is crucial. This is particularly important in organic mixed ionic-electronic conductors (OMIECs), which undergo significant and complex structural changes when operated in an electrolyte. In this study, we investigate the mesoscale strain, reversibility and dynamics of a model OMIEC material under external electrochemical potential using operando X-ray photon correlation spectroscopy. Our results reveal that strain and structural hysteresis depend on the sample's cycling history, establishing a comprehensive kinetic sequence bridging the macroscopic and microscopic behaviours of OMIECs. Furthermore, we uncover the equilibrium and non-equilibrium dynamics of charge carriers and material-doping states, highlighting the unexpected coupling between charge carrier dynamics and mesoscale order. These findings advance our understanding of the structure-dynamics-function relationships in OMIECs, opening pathways for designing and engineering materials with improved performance and functionality in non-equilibrium states during device operation.

Direct quantification of ion composition and mobility in organic mixed ionic-electronic conductors

Ion transport in organic mixed ionic-electronic conductors (OMIECs) is crucial due to its direct impact on device response time and operating mechanisms but is often assessed indirectly or necessitates extra assumptions. Operando x-ray fluorescence (XRF) is a powerful, direct probe for elemental characterization of bulk OMIECs and was used to directly quantify ion composition and mobility in a model OMIEC, poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (PEDOT:PSS), during device operation. The first cycle revealed slow electrowetting and cation-proton exchange. Subsequent cycles showed rapid response with minor cation fluctuation (~5%). Comparison with optical-tracked electrochromic fronts revealed mesoscale structure-dependent proton transport. The calculated effective ion mobility demonstrated thickness-dependent behavior, emphasizing an interfacial ion transport pathway with a higher mobile ion density. The decoupling of interfacial effects on bulk ion mobility and the decoupling of cation and proton migration elucidate ion transport in conventional and emerging OMIEC-based devices and has broader implications for other ionic conductors writ large.

Reconfigurable optoelectronic transistors for multimodal recognition

Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device's superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.

Ferroelectric 2D SnS2 Analog Synaptic FET

In this study, the development and characterization of 2D ferroelectric field-effect transistor (2D FeFET) devices are presented, utilizing nanoscale ferroelectric HfZrO2 (HZO) and 2D semiconductors. The fabricated device demonstrated multi-level data storage capabilities. It successfully emulated essential biological characteristics, including excitatory/inhibitory postsynaptic currents (EPSC/IPSC), Pair-Pulse Facilitation (PPF), and Spike-Timing Dependent Plasticity (STDP). Extensive endurance tests ensured robust stability (107 switching cycles, 105 s (extrapolated to 10 years)), excellent linearity, and high Gmax/Gmin ratio (>105), all of which are essential for realizing multi-level data states (>7-bit operation). Beyond mimicking synaptic functionalities, the device achieved a pattern recognition accuracy of ≈94% on the Modified National Institute of Standards and Technology (MNIST) handwritten dataset when incorporated into a neural network, demonstrating its potential as an effective component in neuromorphic systems. The successful implementation of the 2D FeFET device paves the way for the development of high-efficiency, ultralow-power neuromorphic hardware which is in sub-femtojoule (48 aJ/spike) and fast response (1 µs), which is 104 folds faster than human synapse (≈10 ms). The results of the research underline the potential of nanoscale ferroelectric and 2D materials in building the next generation of artificial intelligence technologies.

Highly Light-Harvesting MOF-on-MOF Heterostructure: Cascading Functionality to Flexible Photogating of Organic Photoelectrochemical Transistor and Bienzyme Cascade Detection

Recently, organic photoelectrochemical transistor (OPECT) bioanalysis has become a prominent technique for the high-performance detection of biomolecules. However, as a sensitive index of the OPECT, the dynamic regulation transconductance (gm) is still severely deficient. Herein, this work reports a new photosensitive metal-organic framework (MOF-on-MOF) heterostructure for the effective modulation of maximum gm and natural bienzyme interfacing toward choline detection. Specifically, the bidentate ligand MOF (b-MOF) was assembled onto the UiO-66 MOF (u-MOF) by a modular assembly method, which could facilitate the charge separation and generate enhanced photocurrents and offer a biophilic environment for the immobilization of choline oxidase (ChOx) and horseradish peroxidase (HRP) through hydrogen-bonded bridges. The transconductance of the OPECT could be flexibly altered by increased light intensity to maximal value at zero gate bias, and sensitive choline detection was achieved with a detection limit of 0.2 μM. This work reveals the potential of MOF-on-MOF heterostructures for futuristic optobioelectronics.

A hybrid transistor with transcriptionally controlled computation and plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring

Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.

Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor

The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104  s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.

Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing

Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.

Multi-Layered Triboelectric Nanogenerators with Controllable Multiple Spikes for Low-Power Artificial Synaptic Devices

In the domains of wearable electronics, robotics, and the Internet of Things, there is a demand for devices with low power consumption and the capability of multiplex sensing, memory, and learning. Triboelectric nanogenerators (TENGs) offer remarkable versatility in this regard, particularly when integrated with synaptic transistors that mimic biological synapses. However, conventional TENGs, generating only two spikes per cycle, have limitations when used in synaptic devices requiring repetitive high-frequency gating signals to perform various synaptic plasticity functions. Herein, a multi-layered micropatterned TENG (M-TENG) consisting of a polydimethylsiloxane (PDMS) film and a composite film that includes 1H,1H,2H,2H-perfluorooctyltrichlorosilane/BaTiO3 /PDMS are proposed. The M-TENG generates multiple spikes from a single touch by utilizing separate triboelectric charges at the multiple friction layers, along with a contact/separation delay achieved by distinct spacers between layers. This configuration allows the maximum triboelectric output charge of M-TENG to reach up to 7.52 nC, compared to 3.69 nC for a single-layered TENG. Furthermore, by integrating M-TENGs with an organic electrochemical transistor, the spike number multiplication property of M-TENGs is leveraged to demonstrate an artificial synaptic device with low energy consumption. As a proof-of-concept application, a robotic hand is operated through continuous memory training under repeated stimulations, successfully emulating long-term plasticity.

Surface Functionalization with (3-Glycidyloxypropyl)trimethoxysilane (GOPS) as an Alternative to Blending for Enhancing the Aqueous Stability and Electronic Performance of PEDOT:PSS Thin Films

Organic mixed ionic-electronic conductors, such as poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), are essential materials for the fabrication of bioelectronic devices due to their unique ability to couple and transport ionic and electronic charges. The growing interest in bioelectronic devices has led to the development of organic electrochemical transistors (OECTs) that can operate in aqueous solutions and transduce ionic signals of biological origin into measurable electronic signals. A common challenge with OECTs is maintaining the stability and performance of the PEDOT:PSS films operating under aqueous conditions. Although the conventional approach of blending the PEDOT:PSS dispersions with a cross-linker such as (3-glycidyloxypropyl)trimethoxysilane (GOPS) helps to ensure strong adhesion of the films to device substrates, it also impacts the morphology and thus electrical properties of the PEDOT:PSS films, which leads to a significant reduction in the performance of OECTs. In this study, we instead functionalize only the surface of the device substrates with GOPS to introduce a silane monolayer before spin-coating the PEDOT:PSS dispersion on the substrate. In all cases, having a GOPS monolayer instead of a blend leads to increased electronic performance metrics, such as three times higher electronic conductivity, volumetric capacitance, and mobility-capacitance product [μC*] value in OECT devices, ultimately leading to a record value of 406 ± 39 F cm-1 V-1 s-1 for amorphous PEDOT:PSS. This increased performance does not come at the expense of operational stability, as both the blend and surface functionalization show similar performance when subjected to pulsed gate bias stress, long-term electrochemical cycling tests, and aging over 150 days. Overall, this study establishes a novel approach to using GOPS as a surface monolayer instead of a blended cross-linker, for achieving high-performance organic mixed ionic-electronic conductors that are stable in water for bioelectronics.

Peculiar transient behaviors of organic electrochemical transistors governed by ion injection directionality

Despite the growing interest in dynamic behaviors at the frequency domain, there exist very few studies on molecular orientation-dependent transient responses of organic mixed ionic-electronic conductors. In this research, we investigated the effect of ion injection directionality on transient electrochemical transistor behaviors by developing a model mixed conductor system. Two polymers with similar electrical, ionic, and electrochemical characteristics but distinct backbone planarities and molecular orientations were successfully synthesized by varying the co-monomer unit (2,2'-bithiophene or phenylene) in conjunction with a novel 1,4-dithienylphenylene-based monomer. The comprehensive electrochemical analysis suggests that the molecular orientation affects the length of the ion-drift pathway, which is directly correlated with ion mobility, resulting in peculiar OECT transient responses. These results provide the general insight into molecular orientation-dependent ion movement characteristics as well as high-performance device design principles with fine-tuned transient responses.

High-Endurance Long-Term Potentiation in Neuromorphic Organic Electrochemical Transistors by PEDOT:PSS Electrochemical Polymerization on the Gate Electrode

The brain exhibits extraordinary information processing capabilities thanks to neural networks that can operate in parallel with minimal energy consumption. Memory and learning require the creation of new neural networks through the long-term modification of the structure of the synapses, a phenomenon called long-term plasticity. Here, we use an organic electrochemical transistor to simulate long-term potentiation and depotentiation processes. Similarly to what happens in a synapse, the polymerization of the 3,4-ethylenedioxythiophene (EDOT) on the gate electrode modifies the structure of the device and boosts the ability of the gate potential to modify the conductivity of the channel. Operando AFM measurements were carried out to demonstrate the correlation between neuromorphic behavior and modification of the gate electrode. Long-term enhancement depends on both the number of pulses used and the gate potential, which generates long-term potentiation when a threshold of +0.7 V is overcome. Long-term depotentiation occurs by applying a +3.0 V potential and exploits the overoxidation of the deposited PEDOT:PSS. The induced states are stable for at least 2 months. The developed device shows very interesting characteristics in the field of neuromorphic electronics.

Wide-Range Synaptic Current Responses with a Liquid Ga Electrode via a Surface Redox Reaction in a NaOH Solution at Different Molar Concentrations

A liquid Ga-based synaptic device with two-terminal electrodes is demonstrated in NaOH solutions at 50 °C. The proposed electrochemical redox device using the liquid Ga electrode in the NaOH solution can emulate various biological synapses that require different decay constants. The device exhibits a wide range of current decay times from 60 to 320 ms at different NaOH mole concentrations from 0.2 to 1.6 M. This research marks a step forward in the development of flexible and biocompatible neuromorphic devices that can be utilized for a range of applications where different synaptic strengths are required lasting from a few milliseconds to seconds.

Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction

Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS2/MoS2 hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS2/MoS2 phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.

High-Hole-Mobility Fiber Organic Electrochemical Transistors for Next-Generation Adaptive Neuromorphic Bio-Hybrid Technologies

The latest developments in fiber design and materials science are paving the way for fibers to evolve from parts in passive components to functional parts in active fabrics. Designing conformable, organic electrochemical transistor (OECT) structures using poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) fibers has excellent potential for low-cost wearable bioelectronics, bio-hybrid devices, and adaptive neuromorphic technologies. However, to achieve high-performance, stable devices from PEDOT:PSS fibers, approaches are required to form electrodes on fibers with small diameters and poor wettability, that leads to irregular coatings. Additionally, PEDOT:PSS-fiber fabrication needs to move away from small batch processing to roll-to-roll or continuous processing. Here, it is shown that synergistic effects from a superior electrode/organic interface, and exceptional fiber alignment from continuous processing, enable PEDOT:PSS fiber-OECTs with stable contacts, high µC* product (1570.5 F cm-1 V-1 s-1 ), and high hole mobility over 45 cm2 V-1 s-1 . Fiber-electrochemical neuromorphic organic devices (fiber-ENODes) are developed to demonstrate that the high mobility fibers are promising building blocks for future bio-hybrid technologies. The fiber-ENODes demonstrate synaptic weight update in response to dopamine, as well as a form factor closely matching the neuronal axon terminal.

Stable organic electrochemical neurons based on p-type and n-type ladder polymers

Organic electrochemical transistors (OECTs) are a rapidly advancing technology that plays a crucial role in the development of next-generation bioelectronic devices. Recent advances in p-type/n-type organic mixed ionic-electronic conductors (OMIECs) have enabled power-efficient complementary OECT technologies for various applications, such as chemical/biological sensing, large-scale logic gates, and neuromorphic computing. However, ensuring long-term operational stability remains a significant challenge that hinders their widespread adoption. While p-type OMIECs are generally more stable than n-type OMIECs, they still face limitations, especially during prolonged operations. Here, we demonstrate that simple methylation of the pyrrole-benzothiazine-based (PBBT) ladder polymer backbone results in stable and high-performance p-type OECTs. The methylated PBBT (PBBT-Me) exhibits a 25-fold increase in OECT mobility and an impressive 36-fold increase in μC* (mobility × volumetric capacitance) compared to the non-methylated PBBT-H polymer. Combining the newly developed PBBT-Me with the ladder n-type poly(benzimidazobenzophenanthroline) (BBL), we developed complementary inverters with a record-high DC gain of 194 V V-1 and excellent stability. These state-of-the-art complementary inverters were used to demonstrate leaky integrate-and-fire type organic electrochemical neurons (LIF-OECNs) capable of biologically relevant firing frequencies of about 2 Hz and of operating continuously for up to 6.5 h. This achievement represents a significant improvement over previous results and holds great potential for developing stable bioelectronic circuits capable of in-sensor computing.

An artificial neuromuscular junction for enhanced reflexes and oculomotor dynamics based on a ferroelectric CuInP2S6/GaN HEMT

A neuromuscular junction (NMJ) is a particularized synapse that activates muscle fibers for macro-motions, requiring more energy than computation. Emulating the NMJ is thus challenging owing to the need for both synaptic plasticity and high driving power to trigger motions. Here, we present an artificial NMJ using CuInP2S6 (CIPS) as a gate dielectric integrated with an AlGaN/GaN-based high-electron mobility transistor (HEMT). The ferroelectricity of the CIPS is coupled with the two-dimensional electron gas channel in the HEMT, providing a wide programmable current range of 6 picoampere per millimeter to 5 milliampere per millimeter. The large output current window of the CIPS/GaN ferroelectric HEMT (FeHEMT) allows for amplifier-less actuation, emulating the biological NMJ functions of actuation and synaptic plasticity. We also demonstrate the emulation of biological oculomotor dynamics, including in situ object tracking and enhanced stimulus responses, using the fabricated artificial NMJ. We believe that the CIPS/GaN FeHEMT offers a promising pathway for bioinspired robotics and neuromorphic vision.

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.

Intermolecular 3D-MoRSE Descriptors for Fast and Accurate Prediction of Electronic Couplings in Organic Semiconductors

The theoretical rational design of organic semiconductors faces an obstacle in that the performance of organic semiconductors depends very much on their stacking and local morphology (for example, phase domains), which involves numerous molecules. Simulation becomes computationally expensive as intermolecular electronic couplings have to be calculated from density functional theory. Therefore, developing fast and accurate methods for intermolecular electronic coupling estimation is essential. In this work, by developing a series of new intermolecular 3D descriptors, we achieved fast and accurate prediction of electronic couplings in both crystalline and amorphous thin films. Three groups of developed descriptors could perform faster and higher accuracy prediction on electronic couplings than the most advanced state-of-the-art descriptors. This work paves the way for large-scale simulations, high-throughput calculations, and screening of organic semiconductors.

A Hybrid Transistor with Transcriptionally Controlled Computation and Plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Quantitative Composition and Mesoscale Ion Distribution in p-Type Organic Mixed Ionic-Electronic Conductors

Understanding the ionic composition and distribution in organic mixed ionic-electronic conductors (OMIECs) is crucial for understanding their structure-property relationships. Despite this, direct measurements of OMIEC ionic composition and distribution are not common. In this work, we investigated the ionic composition and mesoscopic structure of three typical p-type OMIEC materials: an ethylene glycol-treated crosslinked OMIEC with a large excess fixed anionic charge (EG/GOPS-PEDOT:PSS), an acid-treated OMIEC with a tunable fixed anionic charge (crys-PEDOT:PSS), and a single-component OMIEC without any fixed anionic charge (pg2T-TT). A combination of X-ray fluorescence (XRF) and X-ray photoelectron spectroscopies, gravimetry, coulometry, and grazing incidence small-angle X-ray scattering (GISAXS) techniques was employed to characterize these OMIECs following electrolyte exposure and electrochemical cycling. In particular, XRF provided quantitative ion-to-monomer compositions for these OMIECs from passive ion uptake following aqueous electrolyte exposure and potential-driven ion uptake/expulsion following electrochemical doping and dedoping. Single-ion (cation) transport in EG/GOPS-PEDOT:PSS due to Donnan exclusion was directly confirmed, while significant fixed anion concentrations in crys-PEDOT:PSS doping and dedoping were shown to occur through mixed anion and cation transport. Controlling the fixed anionic (PSS^-) charge density in crys-PEDOT:PSS mapped the strength of Donnan exclusion in OMIEC systems following a Donnan-Gibbs model. Anion transport dominated pg2T-TT doping and dedoping, but a surprising degree of anionic charge trapping (∼10²⁰ cm^-3) was observed. GISAXS revealed minimal ion segregation both between PEDOT- and PSS-rich domains in EG/GOPS-PEDOT:PSS and between amorphous and semicrystalline domains in pg2T-TT but showed significant ion segregation in crys-PEDOT:PSS at length scales of tens of nm, ascribed to inter-nanofibril void space. These results bring new clarity to the ionic composition and distribution of OMIECs which are crucial for accurately connecting the structure and properties of these materials.

Polymeric Memristor Based Artificial Synapses with Ultra-Wide Operating Temperature

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

Optically Readable Organic Electrochemical Synaptic Transistors for Neuromorphic Photonic Image Processing

Optically readable organic synaptic devices have great potential in both artificial intelligence and photonic neuromorphic computing. Herein, a novel optically readable organic electrochemical synaptic transistor (OR-OEST) strategy is first proposed. The electrochemical doping mechanism of the device was systematically investigated, and the basic biological synaptic behaviors that can be read by optical means are successfully achieved. Furthermore, the flexible OR-OESTs are capable of electrically switching the transparency of semiconductor channel materials in a nonvolatile manner, and thus the multilevel memory can be achieved through optical readout. Finally, the OR-OESTs are developed for the preprocessing of photonic images, such as contrast enhancement and denoising, and feeding the processed images into an artificial neural network, achieving a recognition rate of over 90%. Overall, this work provides a new strategy for the implementation of photonic neuromorphic systems.

Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin

Artificial skin that simultaneously mimics sensory feedback and mechanical properties of natural skin holds substantial promise for next-generation robotic and medical devices. However, achieving such a biomimetic system that can seamlessly integrate with the human body remains a challenge. Through rational design and engineering of material properties, device structures, and system architectures, we realized a monolithic soft prosthetic electronic skin (e-skin). It is capable of multimodal perception, neuromorphic pulse-train signal generation, and closed-loop actuation. With a trilayer, high-permittivity elastomeric dielectric, we achieved a low subthreshold swing comparable to that of polycrystalline silicon transistors, a low operation voltage, low power consumption, and medium-scale circuit integration complexity for stretchable organic devices. Our e-skin mimics the biological sensorimotor loop, whereby a solid-state synaptic transistor elicits stronger actuation when a stimulus of increasing pressure is applied.

A Biologically Interfaced Evolvable Organic Pattern Classifier

Future brain-computer interfaces will require local and highly individualized signal processing of fully integrated electronic circuits within the nervous system and other living tissue. New devices will need to be developed that can receive data from a sensor array, process these data into meaningful information, and translate that information into a format that can be interpreted by living systems. Here, the first example of interfacing a hardware-based pattern classifier with a biological nerve is reported. The classifier implements the Widrow-Hoff learning algorithm on an array of evolvable organic electrochemical transistors (EOECTs). The EOECTs' channel conductance is modulated in situ by electropolymerizing the semiconductor material within the channel, allowing for low voltage operation, high reproducibility, and an improvement in state retention by two orders of magnitude over state-of-the-art OECT devices. The organic classifier is interfaced with a biological nerve using an organic electrochemical spiking neuron to translate the classifier's output to a simulated action potential. The latter is then used to stimulate muscle contraction selectively based on the input pattern, thus paving the way for the development of adaptive neural interfaces for closed-loop therapeutic systems.

Short-term synaptic plasticity in emerging devices for neuromorphic computing

Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing. Compared to long-term plasticity (LTP) forming the foundation of learning and memory, short-term plasticity (STP) is essential for critical computational functions. So far, the practical applications of LTP have been widely investigated, whereas the implementation of STP in hardware is still elusive. Here, we review the development of STP by bridging the physics in emerging devices and biological behaviors. We explore the computational functions of various STP in biology and review their recent progress. Finally, we discuss the main challenges of introducing STP into synaptic devices and offer the potential approaches to utilize STP to enrich systems' capabilities. This review is expected to provide prospective ideas for implementing STP in emerging devices and may promote the construction of high-level neuromorphic machines.

Organic electrochemical transistors as on-site signal amplifiers for electrochemical aptamer-based sensing

Electrochemical aptamer-based sensors are typically deployed as individual, passive, surface-functionalized electrodes, but they exhibit limited sensitivity especially when the area of the electrode is reduced for miniaturization purposes. We demonstrate that organic electrochemical transistors (electrolyte gated transistors with volumetric gating) can serve as on-site amplifiers to improve the sensitivity of electrochemical aptamer-based sensors. By monolithically integrating an Au working/sensing electrode, on-chip Ag/AgCl reference electrode, and Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) counter electrode - also serving as the channel of an organic electrochemical transistor- we can simultaneously perform testing of organic electrochemical transistors and traditional electroanalytical measurement on electrochemical aptamer-based sensors including cyclic voltammetry and square-wave voltammetry. This device can directly amplify the current from the electrochemical aptamer-based sensor via the in-plane current modulation in the counter electrode/transistor channel. The integrated sensor can sense transforming growth factor beta 1 with 3 to 4 orders of magnitude enhancement in sensitivity compared to that in an electrochemical aptamer-based sensor (292 μA/dec vs. 85 nA/dec). This approach is believed to be universal, and can be applied to a wide range of tethered electrochemical reporter-based sensors to enhance sensitivity, aiding in sensor miniaturization and easing the burden on backend signal processing.

Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement

Perceptual enhancement of neural and behavioral response due to combinations of multisensory stimuli are found in many animal species across different sensory modalities. By mimicking the multisensory integration of ocular-vestibular cues for enhanced spatial perception in macaques, a bioinspired motion-cognition nerve based on a flexible multisensory neuromorphic device is demonstrated. A fast, scalable and solution-processed fabrication strategy is developed to prepare a nanoparticle-doped two-dimensional (2D)-nanoflake thin film, exhibiting superior electrostatic gating capability and charge-carrier mobility. The multi-input neuromorphic device fabricated using this thin film shows history-dependent plasticity, stable linear modulation, and spatiotemporal integration capability. These characteristics ensure parallel, efficient processing of bimodal motion signals encoded as spikes and assigned with different perceptual weights. Motion-cognition function is realized by classifying the motion types using mean firing rates of encoded spikes and postsynaptic current of the device. Demonstrations of recognition of human activity types and drone flight modes reveal that the motion-cognition performance match the bio-plausible principles of perceptual enhancement by multisensory integration. Our system can be potentially applied in sensory robotics and smart wearables.

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.

Perovskite Solar Cell-Gated Organic Electrochemical Transistors for Flexible Photodetectors with Ultrahigh Sensitivity and Fast Response

Photodetectors (PDs) are the building block of various imaging and sensing applications. However, commercially available PDs based on crystalline inorganic semiconductors cannot meet the requirements of emerging wearable/implantable applications due to their rigidity and fragility, which creates the need for flexible devices. Here, a high-performance flexible PD is presented by gating an organic electrochemical transistor (OECT) with a perovskite solar cell. Due to the ultrahigh transconductance of the OECT, the device demonstrates a high gain of ≈10⁶ , a fast response time of 67 µs and an ultrahigh detectivity of 6.7 × 10¹⁷ Jones to light signals under a low working voltage (≤0.6 V). Thanks to the ultrahigh sensitivity and fast response, the device can track photoplethysmogram signals and peripheral oxygen saturation under ambient light and even provide contactless remote sensing, offering a low-power and convenient way for continuous vital signs monitoring. This work offers a novel strategy for realizing high-performance flexible PDs that are promising for low-power, user-friendly and wearable optoelectronics.

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.