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

An Opto-Iontronic Cholesteric Liquid Crystalline Retina for Multimodal Circularly Polarized Neuromorphic Vision

Circularly polarized light (CPL) is fundamental to phase-controlled imaging, quantum optics, and optical computing. Conventional CPL detection, relying on polarizers and quarter-wave plates, complicates device design and reduces sensitivity. Among emerging CPL detectors, organic field-effect transistors (OFET) with helical organic semiconductors are highly promising due to their compact structures but suffer tedious synthesis, low dissymmetric factors (gph < 0.1), and high operating voltages (> 50 V). To address these issues, an opto-iontronic cholesteric liquid crystalline (i-CLC) film is developed that is both electrically and photonically active, serving as the dielectric in phototransistors. The well-defined cholesteric structure and broadly tunable pitches of the i-CLC film enable it to detect CPL with an excellent "handedness" selectivity across a broad spectrum. Moreover, its ionic nature provides a high capacitance (up to 580 nF cm- 2 @20 Hz). The resulting flexible CPL detectors achieve an unprecedentedly high dissymmetry factor (gph = 1.33) at low operating voltages (< 5 V), showcasing significant potential in optical communication and data encryption. Furthermore, leveraging high gph, they can perform in-sensor computing for highly accurate semantic segmentation using fused multimodal visual inputs (e.g., circularly polarized and ordinary light), achieving an accuracy of 75.73% and a mean intersection over the union of 0.3982, surpassing the performance of non-CPL photodetectors. Additionally, it optimizes power consumption by a factor of 102 compared to most conventional visual processing systems, offering a groundbreaking hardware solution for high-performance neuromorphic CPL vision.

Bionic Olfactory Neuron with In-Sensor Reservoir Computing for Intelligent Gas Recognition

Gas sensing and recognition are closely related to the sustainable development of human society, current electronic noses (e-noses) typically focus on detecting specific gases, with only a few capable of recognizing complex odor mixtures. Further, these sensors often struggle to distinguish between isomers and homologs, as these compounds usually have similar physical and chemical properties, yielding nearly identical sensor responses. Even the mammalian olfactory systems consisting of a large variety of receptor cells and efficient neuron networks sometimes fail in this task. The bottleneck stems from the inability to extract the fingerprints of these compounds and the inefficiency of signal processing. To address these limitations, a material-device-algorithm co-design strategy is proposed that integrates an organic field-effect transistor (OFET) array with in-sensor reservoir computing (RC) and the k-nearest neighbors (KNN) algorithm. Organic semiconductors diversify responses to different gases, while RC efficiently extracts spatiotemporal features with lower training costs and reduced energy overhead. This synergy achieves 100% classification accuracy for eight gases and 99.04% accuracy for a library of 26 gases, including mixtures, isomers, and homologs-among the highest reported accuracies. This work provides a groundbreaking hardware solution for bionic olfactory neurons with edge artificial intelligence (AI) functions, surpassing traditional e-noses.

Frontier applications of retinal nanomedicine: progress, challenges and perspectives

The human retina is a fragile and sophisticated light-sensitive tissue in the central nervous system. Unhealthy retinas can cause irreversible visual deterioration and permanent vision loss. Effective therapeutic strategies are restricted to the treatment or reversal of these conditions. In recent years, nanoscience and nanotechnology have revolutionized targeted management of retinal diseases. Pharmaceuticals, theranostics, regenerative medicine, gene therapy, and retinal prostheses are indispensable for retinal interventions and have been significantly advanced by nanomedical innovations. Hence, this review presents novel insights into the use of versatile nanomaterial-based nanocomposites for frontier retinal applications, including non-invasive drug delivery, theranostic contrast agents, therapeutic nanoagents, gene therapy, stem cell-based therapy, retinal optogenetics and retinal prostheses, which have mainly been reported within the last 5 years. Furthermore, recent progress, potential challenges, and future perspectives in this field are highlighted and discussed in detail, which may shed light on future clinical translations and ultimately, benefit patients with retinal disorders.

Biointerface engineering of flexible and wearable electronics

Biointerface sensing is a cutting-edge interdisciplinary field that merges conceptual and practical aspects. Wearable bioelectronics enable efficient interaction and close contact with biological components such as tissues and organs, paving the way for a wide range of medical applications, including personal health monitoring and medical intervention. To be applicable in real-world settings, the patches must be stable and adhere to the skin without causing discomfort or allergies in both wet and dry conditions, as well as other desirable features such as being ultra-soft, thin, flexible, and stretchable. Biosensors have emerged as promising tools primarily used to directly detect biological and electrophysiological signals, enhancing the efficacy of personalized medical treatments and enabling accurate tracking of human well-being. This review highlights the engineering of skin-tissue surfaces/interfaces and their interactions with wearable patches, aiming for both a broad and in-depth understanding of the mechanical and physicochemical properties required for the advancement of flexible and wearable skin patches. Specifically, the advantages of flexible bioelectronics and sensors with optimized surface geometry for long-term diagnosis are discussed. This insight aims to guide the future development of functional materials that can interact with human tissue in a controlled manner. Finally, we provide perspectives on the challenges and potential applications of biointerface engineering in wearable devices.

Molecular-dipole oriented universal growth of conjugated polymers into semiconducting single-crystal thin films

Precise control over crystallinity and morphology of conjugated polymers (CPs) is essential for progressing organic electronics. However, manufacturing single-crystal thin films of CPs presents substantial challenges due to their complex molecular structures, distorted chain conformations, and unbalanced crystallization kinetics. In this work, we demonstrate a universal nanoconfined molecular-dipole orientating strategy to craft high-quality single-crystal thin films for a variety of CPs, spanning from traditional thiophene- and theinothiophene-based homopolymers to diketopyrrolopyrrole- (i.e., p-type) and naphthalene-based (i.e., n-type) donor-acceptor copolymers. Central to this strategy is the synergetic manipulations of molecular dipoles, π-π stackings, and alkyl-alkyl interactions of CPs within our rationally-designed spatial-electrostatic confinement capacitor, which facilitates the rotation of conjugated backbones and the alignment of π-π stackings into microscale-sized single-crystal thin films. A minimal energetic disorder of 25 meV that below the thermal fluctuation energy kBT at room temperature, as well as an excellent transistor mobility of 15.5 cm2V-1s-1 are achieved, marking a significant step towards controllable growths of conjugated-polymer single-crystal thin films that hold a cornerstone for high-performance organic electronic devices.

Seizure detection via reservoir computing in MoS2-based charge trap memory devices

Neurological disorders are a substantial global health burden, affecting millions of people worldwide. A key challenge in developing effective treatments and preventive measures is the realization of low-power wearable systems with early detection capabilities. Traditional strategies rely on machine learning algorithms, but their computational demands often exceed what miniaturized systems can provide. Neuromorphic computing, inspired by the human brain, demonstrated capabilities of on-chip computing with low power consumption. In this context, bidimensional (2D) semiconductors hold notable promise, thanks to their unique electronic properties, atomic-scale thickness, and scalability, making them ideal for low-power applications. This work presents a neuromorphic reservoir computing system exploiting MoS2-based charge trap memories (CTMs) for processing of electrophysiological signals. Real-time seizures detection is achieved, thanks to the nonlinear integration of local-field potential (LFP) recorded from in vitro rodent models of ictogenesis. The results support MoS2-based CTMs for low-power biomedical devices in clinical diagnosis and treatment of epilepsy.

In situ training of an in-sensor artificial neural network based on ferroelectric photosensors

In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor computing systems remains challenging due to the demands for both high-performance devices and efficient programming schemes. Here, we experimentally demonstrate the in situ training of an in-sensor artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast (<30 μs), and multilevel (>4 bits) photoresponses, as well as long retention (50 days), high endurance (109), high write speed (100 ns), and small cycle-to-cycle and device-to-device variations (~0.66% and ~2.72%, respectively), all of which are desirable for the in situ training. Additionally, a bi-directional closed-loop programming scheme is developed, achieving a precise and efficient weight update for the FE-PS. Using this programming scheme, an in-sensor ANN based on the FE-PSs is trained in situ to recognize traffic signs for commanding a prototype autonomous vehicle. Moreover, this in-sensor ANN operates 50 times faster than a von Neumann machine vision system. This study paves the way for the development of in-sensor computing systems with in situ training capability, which may find applications in new data-streaming machine vision tasks.

Hardware-Feasible and Efficient N-Type Organic Neuromorphic Signal Recognition via Reservoir Computing

Organic electrochemical synaptic transistors (OESTs), inspired by the biological nervous system, have garnered increasing attention due to their multifunctional applications in neuromorphic computing. However, the practical implementation of OESTs for signal recognition-particularly those utilizing n-type organic mixed ionic-electronic conductors (OMIECs)-still faces significant challenges at the hardware level. Here, a state-of-the-art small-molecule n-type OEST integrated within a physically simple and hardware feasible reservoir-computing (RC) framework for practical temporal signal recognition is presented. This integration is achieved by leveraging the adjustable synaptic properties of the n-OEST, which exhibits tunable nonlinear short-term memory, transitioning from volatility to nonvolatility, and demonstrating adaptive temporal specificity. Additionally, the nonvolatile OEST offers 256 conductance levels and a wide dynamic range (≈147) in long-term potentiation/depression (LTP/LTD), surpassing previously reported n-OESTs. By combining volatile n-OESTs as reservoirs with a single-layer perceptron readout composed of nonvolatile n-OEST networks, this physical RC system achieves substantial recognition accuracy for both handwritten-digit images (94.9%) and spoken digit (90.7%), along with ultrahigh weight efficiency. Furthermore, this system demonstrates outstanding accuracy (98.0%) by grouped RC in practical sleep monitoring, specifically in snoring recognition. Here, a reliable pathway for OMIEC-driven computing is presented to advance bioinspired hardware-based neuromorphic computing in the physical world.

Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing

In-sensor computing, which integrates sensing, memory and processing functions, has shown substantial potential in artificial vision systems. However, large-scale monolithic integration of in-sensor computing based on emerging devices with complementary metal-oxide-semiconductor (CMOS) circuits remains challenging, lacking functional demonstrations at the hardware level. Here we report a fully integrated 1-kb array with 128 × 8 one-transistor one-optoelectronic memristor (OEM) cells and silicon CMOS circuits, which features configurable multi-mode functionality encompassing three different modes of electronic memristor, dynamic OEM and non-volatile OEM (NV-OEM). These modes are configured by modulating the charge density within the oxygen vacancies via synergistic optical and electrical operations, as confirmed by differential phase-contrast scanning transmission electron microscopy. Using this OEM system, three visual processing tasks are demonstrated: image sensory pre-processing with a recognition accuracy enhanced from 85.7% to 96.1% by the NV-OEM mode, more advanced object tracking with 96.1% accuracy using both dynamic OEM and NV-OEM modes and human motion recognition with a fully OEM-based in-sensor reservoir computing system achieving 91.2% accuracy. A system-level benchmark further shows that it consumes over 20 times less energy than graphics processing units. By monolithically integrating the multi-functional OEMs with Si CMOS, this work provides a cost-effective platform for diverse in-sensor computing applications.

Organic Nonvolatile 2T Memory Cell Employing a NOT-Gate-Like Architecture Toward Binary Output Level With Enhanced Noise Tolerance

Organic nonvolatile memory has been considered a low-cost memory technology for flexible electronics and Internet-of-things (IoT). However, a major concern is the nonuniformity of memory units, which is primarily caused by random grain boundaries, interface defects, and charge traps, making it difficult to develop high-density reliable memory arrays. This nonuniformity problem would induce read error, which is directly caused by the narrow distribution margin of memory states and low noise tolerance in conventional organic memory cells. To break this limitation, a novel 2T memory cell employing a NOT-gate-like architecture achieving self-enhancing noise tolerance is presented. This unique cell consists of a pair of commonly-gated memory transistors with contradictory "write-and-erase" features. It functions as a voltage divider, producing a well-distinguished binary voltage output capability. The concept and design model of this brand-new 2T memory cell is thoroughly discussed. It is originally characterized by noise-tolerant memory cells irrespective of device nonuniformity. The noise tolerance range of this 2T memory cell is also investigated. The binary voltage-readable memory state with a large noise tolerance range is obtained. Moreover, the conceptual design of the 1T2T FeRAM cell is further developed for low-cost voltage-readable memory technology in wearable electronic applications.

A neuromorphic event data interpretation approach with hardware reservoir

Event cameras have shown unprecedented success in various computer vision applications due to their unique ability to capture dynamic scenes with high temporal resolution and low latency. However, many existing approaches for event data representation are typically algorithm-based, limiting their utilization and hardware deployment. This study explores a hardware event representation approach for event data utilizing a reservoir encoder implemented with analog memristor. The inherent stochastic and non-linear characteristics of the memristors enable the effective and low-cost feature extraction of temporal information from event streams as a reservoir encoder. We propose a simplified memristor model and memristor-based reservoir circuit specifically for processing dynamic visual information and extracting feature in event data. Experimental results with four event datasets demonstrate that our approach achieves superior accuracy over other methods, highlighting the potential of memristor-based event processing system.

Advanced Applications of Polymer Hydrogels in Electronics and Signal Processing

The current state of affairs in the field of using polymer hydrogels for the creation of innovative systems for signal and image processing, of which computing is a special case, is analyzed. Both of these specific examples of systems capable of forming an alternative to the existing semiconductor-based computing technology, but assuming preservation of the used algorithmic basis, and non-trivial signal converters, the nature of which requires transition to fundamentally different algorithms of data processing, are considered. It is shown that the variability of currently developed information processing systems based on the use of polymers, including polymer hydrogels, leads to the need to search for complementary algorithms. Moreover, the well-known thesis that modern polymer science allows for the realization of functional materials with predetermined properties, at the present stage, receives a new sounding: it is acceptable to raise the question of creating systems built on a quasi-biological basis and realizing predetermined algorithms of information or image processing. Specific examples that meet this thesis are considered, in particular, promising information protection systems for UAV groups, as well as systems based on the coupling of neural networks with holograms that solve various applied problems. These and other case studies demonstrate the importance of interdisciplinary cooperation for solving problems arising from the need for further modernization of signal processing systems.

A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression

Physical reservoir-based reservoir computing (RC) systems for intelligent perception have recently gained attention because they require fewer computing resources. However, the system remains limited in infrared (IR) machine vision, including materials and physical reservoir expression power. Inspired by biological visual perception systems, the study proposes a near-infrared (NIR) retinomorphic device that simultaneously perceives and encodes narrow IR spectral information (at ≈980 nm). The proposed device, featuring core-shell upconversion nanoparticle/poly (3-hexylthiophene) (P3HT) nanocomposite channels, enables the absorption and conversion of NIR into high-energy photons to excite more photo carriers in P3HT. The photon-electron-coupled dynamics under the synergy of photovoltaic and photogating effects influence the nonlinearity and high dimensionality of the RC system under narrow-band NIR irradiation. The device also exhibits multilevel data storage capability (≥8 levels), excellent stability (≥2000 s), and durability (≥100 cycles). The system accurately identifies NIR static and dynamic handwritten digit images, achieving recognition accuracies of 91.13% and 90.07%, respectively. Thus, the device tackles intricate computations like solving second-order nonlinear dynamic equations with minimal errors (normalized mean squared error of 1.06 × 10⁻3 during prediction).

Ionic Potential Relaxation Effect in a Hydrogel Enabling Synapse-Like Information Processing

The next-generation brain-like intelligence based on neuromorphic architectures emphasizes learning the ionic language of the brain, aiming for efficient brain-like computation and seamless human-computer interaction. Ionic neuromorphic devices, with ions serving as information carriers, provide possibilities to achieve this goal. Soft and biocompatible ionic conductive hydrogels are an ideal substrate for constructing ionic neuromorphic devices, but it remains a challenge to modulate the ion transport behavior in hydrogels to mimic neuroelectric signals. Here, we describe an ionic potential relaxation effect in a hydrogel device prepared by sandwiching a layer of polycationic hydrogel (CH) between two layers of neutral hydrogel (NH), allowing this device to simulate various electrical signal patterns observed in biological synapses, including short- and long-term plasticity patterns. Theoretical and experimental results show that the selective permeation and hysteretic diffusion of ions caused by the anion selectivity of the CH layer are responsible for potential relaxation. Such an effect allows us with hydrogels to enable synapse-like information processing functions, including tactile perception, learning, memory, and neuromorphic computing. Additionally, the hydrogel device can operate stably even under 180° bending and 50% tensile strain, expanding the pathway for implementing advanced brain-like intelligent systems.

Birdlike broadband neuromorphic visual sensor arrays for fusion imaging

Wearable visual bionic devices, fueled by advancements in artificial intelligence, are making remarkable progress. However, traditional silicon vision chips often grapple with high energy losses and challenges in emulating complex biological behaviors. In this study, we constructed a van der Waals P3HT/GaAs nanowires P-N junction by carefully directing the arrangement of organic molecules. Combined with a Schottky junction, this facilitated multi-faceted birdlike visual enhancement, including broadband non-volatile storage, low-light perception, and a near-zero power consumption operating mode in both individual devices and 5 × 5 arrays on arbitrary substrates. Specifically, we realized over 5 bits of in-memory sensing and computing with both negative and positive photoconductivity. When paired with two imaging modes (visible and UV), our reservoir computing system demonstrated up to 94% accuracy for color recognition. It achieved motion and UV grayscale information extraction (displayed with sunscreen), leading to fusion visual imaging. This work provides a promising co-design of material and device for a broadband and highly biomimetic optoelectronic neuromorphic system.

Optoelectronic synapses with chemical-electric behaviors in gallium nitride semiconductors for biorealistic neuromorphic functionality

Optoelectronic synapses, leveraging the integration of classic photo-electric effect with synaptic plasticity, are emerging as building blocks for artificial vision and photonic neuromorphic computing. However, the fundamental working principles of most optoelectronic synapses mainly rely on physical behaviors while missing chemical-electric synaptic processes critical for mimicking biorealistic neuromorphic functionality. Herein, we report a photoelectrochemical synaptic device based on p-AlGaN/n-GaN semiconductor nanowires to incorporate chemical-electric synaptic behaviors into optoelectronic synapses, demonstrating unparalleled dual-modal plasticity and chemically-regulated neuromorphic functions through the interplay of internal photo-electric and external electrolyte-mediated chemical-electric processes. Electrical modulation by implementing closed or open-circuit enables switching of optoelectronic synaptic operation between short-term and long-term plasticity. Furthermore, inspired by transmembrane receptors that connect extracellular and intracellular events, synaptic responses can also be effectively amplified by applying chemical modifications to nanowire surfaces, which tune external and internal charge behaviors. Notably, under varied external electrolyte environments (ion/molecule species and concentrations), our device successfully mimics chemically-regulated synaptic activities and emulates intricate oxidative stress-induced biological phenomena. Essentially, we demonstrate that through the nanowire photoelectrochemical synapse configuration, optoelectronic synapses can be incorporated with chemical-electric behaviors to bridge the gap between classic optoelectronic synapses and biological synapses, providing a promising platform for multifunctional neuromorphic applications.

Polymer Dielectric-Based Emerging Devices: Advancements in Memory, Field-Effect Transistor, and Nanogenerator Technologies

Polymer dielectric materials have recently attracted attention for their versatile applications in emerging electronic devices such as memory, field-effect transistors (FETs), and triboelectric nanogenerators (TENGs). This review highlights the advances in polymer dielectric materials and their integration into these devices, emphasizing their unique electrical, mechanical, and thermal properties that enable high performance and flexibility. By exploring their roles in self-sustaining technologies (e.g., artificial intelligence (AI) and Internet of Everything (IoE)), this review emphasizes the importance of polymer dielectric materials in enabling low-power, flexible, and sustainable electronic devices. The discussion covers design strategies to improve the dielectric constant, charge trapping, and overall device stability. Specific challenges, such as optimizing electrical properties, ensuring process scalability, and enhancing environmental stability, are also addressed. In addition, the review explores the synergistic integration of memory devices, FETs, and TENGs, focusing on their potential in flexible and wearable electronics, self-powered systems, and sustainable technologies. This review provides a comprehensive overview of the current state and prospects of polymer dielectric-based devices in advanced electronic applications by examining recent research breakthroughs and identifying future opportunities.

Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel Machine Vision

In-sensor and near-sensor computing architectures enable multiply accumulate operations to be carried out directly at the point of sensing. In-sensor architectures offer dramatic power and speed improvements over traditional von Neumann architectures by eliminating multiple analog-to-digital conversions, data storage, and data movement operations. Current in-sensor processing approaches rely on tunable sensors or additional weighting elements to perform linear functions such as multiply accumulate operations as the sensor acquires data. This work implements in-sensor computing with an oscillatory retinal neuron device that converts incident optical signals into voltage oscillations. A computing scheme is introduced based on the frequency shift of coupled oscillators that enables parallel, frequency multiplexed, nonlinear operations on the inputs. An experimentally implemented 3 × 3 focal plane array of coupled neurons shows that functions approximating edge detection, thresholding, and segmentation occur in parallel. An example of inference on handwritten digits from the MNIST database is also experimentally demonstrated with a 3 × 3 array of coupled neurons feeding into a single hidden layer neural network, approximating a liquid-state machine. Finally, the equivalent energy consumption to carry out image processing operations, including peripherals such as the Fourier transform circuits, is projected to be <20 fJ/OP, possibly reaching as low as 15 aJ/OP.

Dynamic memristor for physical reservoir computing

Reservoir computing (RC) has attracted considerable attention for its efficient handling of temporal signals and lower training costs. As a nonlinear dynamic system, RC can map low-dimensional inputs into high-dimensional spaces and implement classification using a simple linear readout layer. The memristor exhibits complex dynamic characteristics due to its internal physical processes, which renders them an ideal choice for the implementation of physical reservoir computing (PRC) systems. This review focuses on PRC systems based on memristors, explaining the resistive switching mechanism at the device level and emphasizing the tunability of their dynamic behavior. The development of memristor-based reservoir computing systems is highlighted, along with discussions on the challenges faced by this field and potential future research directions.

Optically Tunable Electrical Oscillations in Oxide-Based Memristors for Neuromorphic Computing

The application of hardware-based neural networks can be enhanced by integrating sensory neurons and synapses that enable direct input from external stimuli. This work reports direct optical control of an oscillatory neuron based on volatile threshold switching in V3O5. The devices exhibit electroforming-free operation with switching parameters that can be tuned by optical illumination. Using temperature-dependent electrical measurements, conductive atomic force microscopy (C-AFM), in situ thermal imaging, and lumped element modelling, it is shown that the changes in switching parameters, including threshold and hold voltages, arise from overall conductivity increase of the oxide film due to the contribution of both photoconductive and bolometric characteristics of V3O5, which eventually affects the oscillation dynamics. Furthermore, V3O5 is identified as a new bolometric material with a temperature coefficient of resistance (TCR) as high as -4.6% K-1 at 423 K. The utility of these devices is illustrated by demonstrating in-sensor reservoir computing with reduced computational effort and an optical encoding layer for spiking neural network (SNN), respectively, using a simulated array of devices.

A Reconfigurable Bipolar Image Sensor for High-Efficiency Dynamic Vision Recognition

Dynamic vision perception and processing (DVPP) is in high demand by booming edge artificial intelligence. However, existing imaging systems suffer from low efficiency or low compatibility with advanced machine vision techniques. Here, we propose a reconfigurable bipolar image sensor (RBIS) for in-sensor DVPP based on a two-dimensional WSe2/GeSe heterostructure device. Owing to the gate-tunable and reversible built-in electric field, its photoresponse shows bipolarity as being positive or negative. High-efficiency DVPP incorporating front-end RBIS and back-end CNN is then demonstrated. It shows a high recognition accuracy of over 94.9% on the derived DVS128 data set and requires much fewer neural network parameters than that without RBIS. Moreover, we demonstrate an optimized device with a vertically stacked structure and a stable nonvolatile bipolarity, which enables more efficient DVPP hardware. Our work demonstrates the potential of fabricating DVPP devices with a simple structure, high efficiency, and outputs compatible with advanced algorithms.

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.

Ultralow-power optoelectronic synaptic transistors based on polyzwitterion dielectrics for in-sensor reservoir computing

Bio-inspired transistor synapses use solid electrolytes to achieve low-power operation and rich synaptic behaviors via ion diffusion and trapping. While these neuromorphic devices hold great promise, they still suffer from challenges such as high leakage currents and power consumption, electrolysis risk, and irreversible conductance changes due to long-range ion migrations and permanent ion trapping. In addition, their response to light is generally limited because of "exciton-polaron quenching", which restricts their potential in in-sensor neuromorphic visions. To address these issues, we propose replacing solid electrolytes with polyzwitterions, where the cation and anion are covalently concatenated via a flexible alkyl chain, thus preventing long-range ion migrations while inducing good photoresponses to the transistors via interfacial charge trapping. Our detailed studies reveal that polyzwitterion-based transistors exhibit optoelectronic synaptic behavior with ultralow-power consumption (~250 aJ per spike) and enable high-performance in-sensor reservoir computing, achieving 95.56% accuracy in perceiving the trajectory of moving basketballs.

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.

Few- and single-molecule reservoir computing experimentally demonstrated with surface-enhanced Raman scattering and ion gating

Molecule-based reservoir computing (RC) is promising for achieving low power consumption neuromorphic computing, although the information-processing capability of small numbers of molecules is not clear. Here, we report a few- and single-molecule RC that uses the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface-enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles. The Raman signals of the pMBA molecules, adsorbed at the SERS active site of the nanorod, were reversibly perturbated by the application of voltage-induced local pH changes near the molecules, and then used to perform time-series analysis tasks. Despite the small number of molecules used, our system achieved good performance, including >95% accuracy in various nonlinear waveform transformations, 94.3% accuracy in solving a second-order nonlinear dynamic system, and a prediction error of 25.0 milligrams per deciliter in a 15-minute-ahead blood glucose level prediction. Our work provides a concept of few-molecular computing with practical computation capabilities.

Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction

Reservoir computing has attracted considerable attention due to its low training cost. However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, faces challenges in providing adequate spatial and temporal scales characteristic for effective computing. Here, we report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states. The carrier dynamics used to map signals are enriched by coupled multivariate physics mechanisms, while the vertical architecture employed greatly increases the feedback intensity of the device. Consequently, the device as a reservoir, effectively mapping sequential signals into distributed reservoir state space with 1152 reservoir states, and the range ratio of temporal and spatial characteristics can simultaneously reach 2640 and 650, respectively. The grouped-reservoir computing based on the device can simultaneously adapt to different spatiotemporal task, achieving recognition accuracy over 94% and prediction correlation over 95%. This work proposes a new strategy for developing high-performance reservoir computing networks.

Parallel perception of visual motion using light-tunable memory matrix

Parallel perception of visual motion is of crucial significance to the development of an intelligent machine vision system. However, implementing in-sensor parallel visual motion perception using conventional complementary metal-oxide semiconductor technology is challenging, because the temporal and spatial information embedded in motion cannot be simultaneously encoded and perceived at the sensory level. Here, we demonstrate the parallel perception of diverse motion modes at the sensor level by exploiting light-tunable memory matrix in a van der Waals (vdW) heterostructure array. The optoelectronic characteristics of gate-tunable photoconductivity and light-tunable memory matrix enable devices in the array to realize simultaneous encoding and processing of incoming spatiotemporal light pattern. Furthermore, we implement a visual motion perceptron with the array capable of deciphering multiple motion parameters in parallel, including direction, velocity, acceleration, and angular velocity. Our work opens up a promising venue for the realization of an intelligent machine vision system based on in-sensor motion perception.

Breaking the Trade-Off Between Polymer Dielectric Constant and Loss via Aluminum Oxo Macrocycle Dopants for High-Performance Neuromorphic Electronics

The dielectric layer is crucial in regulating the overall performance of field-effect transistors (FETs), the key component in central processing units, sensors, and displays. Despite considerable efforts being devoted to developing high-permittivity (k) dielectrics, limited progress is made due to the inherent trade-off between dielectric constant and loss. Here, a solution is presented by designing a monodispersed disk-shaped Ce-Al-O-macrocycle as a dopant in polymer dielectrics. The molecule features a central Ce(III) core connected with eight Al atoms through sixteen bridging hydroxyls and eight 3-aminophenyl peripheries. The incorporation of this macrocycle in polymer dielectrics results in an up to sevenfold increase in dielectric constants and up to 89% reduction in dielectric loss at low frequencies. Moreover, the leakage-current densities decrease, and the breakdown strengths are improved by 63%. Relying on the above merits, FETs bearing cluster-doped polymer dielectrics give near three-orders source-drain current increments while maintaining low-level leakage/off currents, resulting in much higher charge-carrier mobilities (up to 2.45 cm2  V-1  s-1 ) and on/off ratios. This cluster-doping strategy is generalizable and shows great promise for ultralow-power photoelectric synapses and neuromorphic retinas. This work successfully breaks the trade-off between dielectric constant and loss and offers a unique design for polymer composite dielectrics.