Toward a Brain-Neuromorphics Interface.

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.

Ga2O3 Bipolar Heterojunction-Based Optoelectronic Synapse Array with Visual Attention.

The human brain efficiently processes only a fraction of visual information, a phenomenon termed attentional control, resulting in energy savings and heightened adaptability. Translating this mechanism into artificial visual neurons holds promise for constructing energy-efficient, bioinspired visual systems. Here, we propose a self-rectifying artificial visual neuron (SEVN) based on a NiO/Ga2O3 bipolar heterojunction with attentional control on patterns with a target color. The device exhibits short-term potentiation (STP) with quantum point contact (QPC) traits at low bias and transitions to long-term potentiation (LTP) at high bias, particularly facilitated by electron capture in deep defects upon ultraviolet (UV) exposure. With the utilization of two wavelengths of light upon the target and interference part of CAPTCHA to simulate top-down attentional control, the recognition accuracy is enhanced from 74 to 84%. These findings have the potential to augment the visual capability of neuromorphic systems with implications for diverse applications, including cybersecurity, healthcare, and machine vision.

An Antidehydration Hydrogel Based on Zwitterionic Oligomers for Bioelectronic Interfacing.

Hydrogels are ideal interfacing materials for on-skin healthcare devices, yet their susceptibility to dehydration hinders their practical use. While incorporating hygroscopic metal salts can prevent dehydration and maintain ionic conductivity, concerns arise regarding metal toxicity due to the passage of small ions through the skin barrier. Herein, an antidehydration hydrogel enabled by the incorporation of zwitterionic oligomers into its network is reported. This hydrogel exhibits exceptional water retention properties, maintaining ≈88% of its weight at 40% relative humidity, 25 °C for 50 days and about 84% after being heated at 50 °C for 3 h. Crucially, the molecular weight design of the embedded oligomers prevents their penetration into the epidermis, as evidenced by experimental and molecular simulation results. The hydrogel allows stable signal acquisition in electrophysiological monitoring of humans and plants under low-humidity conditions. This research provides a promising strategy for the development of epidermis-safe and biocompatible antidehydration hydrogel interfaces for on-skin devices.

In Situ Etching of Multifunctional Three-Dimensional Interfacial Layers for the Construction of Porous Zn Anodes with Enhanced Surface Textures.

Aqueous Zn-ion batteries offer the advantages of greater security and lower fabrication costs over their lithium-ion counterparts. However, their further advancement and practical application are hindered by the drastic decay in their performance due to the uncontrollable dendrite growth on Zn anodes. In this study, we fabricated a versatile three-dimensional (3D) interfacial layer (3D PVDF-Zn(TFO)2 (PVDF: poly(vinylidene fluoride); TFO: trifluoromethanesulfonate), which simultaneously formed porous Zn-metal anodes (PZn) with an enhanced (002) texture, via a in situ etching scheme. The 3D PVDF-Zn(TFO)2@PZn symmetrical cells leverage the advantages of surface coating and 3D porous architectures to yield extra-long cyclic lifetimes of over 5300 h (0.1 mA cm-2). The fabricated anodes were found to be compatible with MnO2 cathodes, and the resulting full batteries delivered an outstanding capacity of 336 mAh g-1 at 0.1 A g-1 and exhibited impressive long-term reversibility with a capacity retention of 78.7% for 2000 cycles. The proposed coating strategy is viable for developing porous structures with cutting-edge designs and for textured surface engineering.

Power-Efficient Multisensory Reservoir Computing Based on Zr-Doped HfO2 Memcapacitive Synapse Arrays.

Hardware implementation tailored to requirements in reservoir computing would facilitate lightweight and powerful temporal processing. Capacitive reservoirs would boost power efficiency due to their ultralow static power consumption but have not been experimentally exploited yet. Here, this work reports an oxide-based memcapacitive synapse (OMC) based on Zr-doped HfO2 (HZO) for a power-efficient and multisensory processing reservoir computing system. The nonlinearity and state richness required for reservoir computing could originate from the capacitively coupled polarization switching and charge trapping of hafnium-oxide-based devices. The power consumption (≈113.4 fJ per spike) and temporal processing versatility outperform most resistive reservoirs. This system is verified by common benchmark tasks, and it exhibits high accuracy (>94%) in recognizing multisensory information, including acoustic, electrophysiological, and mechanic modalities. As a proof-of-concept, a touchless user interface for virtual shopping based on the OMC-based reservoir computing system is demonstrated, benefiting from its interference-robust acoustic and electrophysiological perception. These results shed light on the development of highly power-efficient human-machine interfaces and machine-learning platforms.

Vertically integrated spiking cone photoreceptor arrays for color perception.

The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a metal oxide-based vertically integrated spiking cone photoreceptor array, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. Such spiking cone photoreceptors have an ultralow power consumption of less than 400 picowatts per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo-three-primary colors to form 'colorful' images for recognition tasks, and the device with the ability to discriminate mixed colors shows better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.

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.

Strain-Driven Auto-Detachable Patterning of Flexible Electrodes.

Flexible electrodes that are multilayer, multimaterial, and conformal are pivotal for multifunctional wearable electronics. Traditional electronic circuits manufacturing requires substrate-supported transfer printing, which limits their multilayer integrity and device conformability on arbitrary surfaces. Herein, a "shrinkage-assisted patterning by evaporation" (SHAPE) method is reported, by employing evaporation-induced interfacial strain mismatch, to fabricate auto-detachable, freestanding, and patternable electrodes. The SHAPE method utilizes vacuum-filtration of polyaniline/bacterial cellulose (PANI/BC) ink through a masked filtration membrane to print high-resolution, patterned, and multilayer electrodes. The strong interlayer hydrogen bonding ensures robust multilayer integrity, while the controllable evaporative shrinking property of PANI/BC induces mismatch between the strains of the electrode and filtration membrane at the interface and thus autodetachment of electrodes. Notably, a 500-layer substrateless micro-supercapacitor fabricated using the SHAPE method exhibits an energy density of 350 mWh cm-2 at a power density of 40 mW cm-2 , 100 times higher than reported substrate-confined counterparts. Moreover, a digital circuit fabricated using the SHAPE method functions stably on a deformed glove, highlighting the broad wearable applications of the SHAPE method.

Artificial Neural Pathway Based on a Memristor Synapse for Optically Mediated Motion Learning.

Animals execute intelligent and efficient interactions with their surroundings through neural pathways, exhibiting learning, memory, and cognition. Artificial autonomous devices that generate self-optimizing feedback mimicking biological systems are essential in pursuing future intelligent robots. Here, we report an artificial neural pathway (ANP) based on a memristor synapse to emulate neuromorphic learning behaviors. In our ANP, optical stimulations are detected and converted into electrical signals through a flexible perovskite photoreceptor. The acquired electrical signals are further processed in a zeolitic imidazolate frameworks-8 (ZIF-8)-based memristor device. By controlling the growth of the ZIF-8 nanoparticles, the conductance of the memristor can be finely modulated with electrical stimulations to mimic the modulation of synaptic plasticity. The device is employed in the ANP to implement synaptic functions of learning and memory. Subsequently, the synaptic feedbacks are used to direct a robotic arm to perform responding motions. Upon repeatedly "reviewing" the optical stimulation, the ANP is able to learn, memorize, and complete the specific motions. This work provides a promising strategy toward the design of intelligent autonomous devices and bioinspired robots through memristor-based systems.

A Photoelectric Spiking Neuron for Visual Depth Perception.

The biological visual system encodes optical information into spikes and processes them by the neural network, which enables the perception with high throughput of visual processing with ultralow energy budget. This has inspired a wide spectrum of devices to imitate such neural process, while precise mimicking such procedure is still highly required. Here, a highly bio-realistic photoelectric spiking neuron for visual depth perception is presented. The firing spikes generated by the TaOX memristive spiking encoders have a biologically similar frequency range of 1-200 Hz and sub-micro watts power. Such spiking encoder is integrated with a photodetector and a network of neuromorphic transistors, for information collection and recognition tasks, respectively. The distance-dependent response and eye fatigue of biological visual systems have been mimicked based on such photoelectric spiking neuron. The simulated depth perception shows a recognition improvement by adapting to sights at different distances. The results can advance the technologies in bioinspired or robotic systems that may be endowed with depth perception and power efficiency at the same time.

Mechanically Durable Memristor Arrays Based on a Discrete Structure Design.

Memristors constitute a promising functional component for information storage and in-memory computing in flexible and stretchable electronics including wearable devices, prosthetics, and soft robotics. Despite tremendous efforts made to adapt conventional rigid memristors to flexible and stretchable scenarios, stretchable and mechanical-damage-endurable memristors, which are critical for maintaining reliable functions under unexpected mechanical attack, have never been achieved. Here, the development of stretchable memristors with mechanical damage endurance based on a discrete structure design is reported. The memristors possess large stretchability (40%) and excellent deformability (half-fold), and retain stable performances under dynamic stretching and releasing. It is shown that the memristors maintain reliable functions and preserve information after extreme mechanical damage, including puncture (up to 100 times) and serious tearing situations (fully diagonally cut). The structural strategy offers new opportunities for next-generation stretchable memristors with mechanical damage endurance, which is vital to achieve reliable functions for flexible and stretchable electronics even in extreme and highly dynamic environments.

Spontaneous Polarity Flipping in a 2D Heterobilayer Induced by Fluctuating Interfacial Carrier Flows.

Polarity often refers to the charge carrier type of a semiconductor or the charging state of a functional group, generally dominating their functionality and performance. Herein we uncover a spontaneous and stochastic polarity-flipping phenomenon in monolayer WSe2, which randomly switches between the n-type and p-type states and is essentially triggered by fluctuating carrier flows from or to the adjacent WS2 monolayer. We have traced such fluctuating carrier flows by interfacial photocurrent measurements in a zero-bias two-terminal device. Such polarity flipping results in switching between the negative and positive correlations between the emission intensities of WS2 and WSe2 in the heterobilayer, which is further well-controlled by the electrostatic gate-tuning experiments in a capacitor-structure device. Our work not only demonstrates giant and intermittent carrier flows through long-range coupling in 2D heterostructures and a consequent spontaneous polarity flipping phenomenon but also provides a two-emitter system with a switchable correlation sign that could project future applications in optical logic devices.

Highly Thermal-Wet Comfortable and Conformal Silk-Based Electrodes for On-Skin Sensors with Sweat Tolerance.

Noninvasive and seamless interfacing between the sensors and human skin is highly desired for wearable healthcare. Thin-film-based soft and stretchable sensors can to some extent form conformal contact with skin even under dynamic movements for high-fidelity signals acquisition. However, sweat accumulation underneath these sensors for long-term monitoring would compromise the thermal-wet comfort, electrode adherence to the skin, and signal fidelity. Here, we report the fabrication of a highly thermal-wet comfortable and conformal silk-based electrode, which can be used for on-skin electrophysiological measurement under sweaty conditions. It is realized through incorporating conducting polymers poly(3,4-ethylenedioxythiophene): polystyrenesulfonate (PEDOT:PSS) into glycerol-plasticized silk fiber mats. Glycerol plays the role of tuning the mechanical properties of silk fiber mats and enhancing the conductivity of PEDOT:PSS. Our silk-based electrodes show high stretchability (>250%), low thermal insulation (∼0.13 °C·m2·W-1), low evaporative resistance (∼23 Pa·m2·W-1, 10 times lower than ∼1.3 mm thick commercial gel electrodes), and high water-vapor transmission rate (∼117 g·m-2·h-1 under sweaty conditions, 2 times higher than skin water loss). These features enable a better electrocardiography signal quality than that of commercial gel electrodes without disturbing the heat dissipation during sweat evaporation and provide possibilities for textile integration to monitor the muscle activities under large deformation. Our glycerol-plasticized silk-based electrodes possessing superior physiological comfortability may further engage progress in on-skin electronics with sweat tolerance.

Artificial Skin Perception.

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

A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem.

Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals' adaptability. However, enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment, ideally in a single device, is challenging. Here, we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative learning. This approach couples the photogating effect at the interface with ferroelectric polarization switching, enabling highly tunable optical modulation of charge carriers. Our device acts as a smarter Pavlovian dog exhibiting adjustable associative learning with the training cycles tuned from thirteen to two. In particular, we obtain a large output difference (>103), which is very similar to the all-or-nothing biological sensory/motor neuron spiking with decrementless conduction. As proof-of-concept demonstrations, photoferroelectric coupling-based applications in cryptography and logic gates are achieved in a single device, indicating compatibility with biological and digital data processing.

An artificial sensory neuron with visual-haptic fusion.

Human behaviors are extremely sophisticated, relying on the adaptive, plastic and event-driven network of sensory neurons. Such neuronal system analyzes multiple sensory cues efficiently to establish accurate depiction of the environment. Here, we develop a bimodal artificial sensory neuron to implement the sensory fusion processes. Such a bimodal artificial sensory neuron collects optic and pressure information from the photodetector and pressure sensors respectively, transmits the bimodal information through an ionic cable, and integrates them into post-synaptic currents by a synaptic transistor. The sensory neuron can be excited in multiple levels by synchronizing the two sensory cues, which enables the manipulating of skeletal myotubes and a robotic hand. Furthermore, enhanced recognition capability achieved on fused visual/haptic cues is confirmed by simulation of a multi-transparency pattern recognition task. Our biomimetic design has the potential to advance technologies in cyborg and neuromorphic systems by endowing them with supramodal perceptual capabilities.

Devising Materials Manufacturing Toward Lab-to-Fab Translation of Flexible Electronics.

Flexible electronics have witnessed exciting progress in academia over the past decade, but most of the research outcomes have yet to be translated into products or gain much market share. For mass production and commercialization, industrial adoption of newly developed functional materials and fabrication techniques is a prerequisite. However, due to the disparate features of academic laboratories and industrial plants, translating materials and manufacturing technologies from labs to fabs is notoriously difficult. Therefore, herein, key challenges in the materials manufacturing of flexible electronics are identified and discussed for its lab-to-fab translation, along the four stages in product manufacturing: design, materials supply, processing, and integration. Perspectives on industry-oriented strategies to overcome some of these obstacles are also proposed. Priorities for action are outlined, including standardization, iteration between basic and applied research, and adoption of smart manufacturing. With concerted efforts from academia and industry, flexible electronics will bring a bigger impact to society as promised.

Bioinspired Ionic Sensory Systems: The Successor of Electronics.

All biological systems, including animals and plants, communicate in a language of ions and small molecules, while the modern information infrastructures and technologies rely on a language of electrons. Although electronics and bioelectronics have made great progress in the past several decades, they still face the disadvantage of signal transformation when communicating with biology. To narrow the gap between biological systems and artificial-intelligence systems, bioinspired ion-transport-based sensory systems should be developed as successor of electronics, since they can emulate biological functionality more directly and communicate with biology seamlessly. Herein, the essential principles of (accurate) ion transport are introduced, and the recent progress in the development of three elements of an ionic sensory system is reviewed: ionic sensors, ionic processors, and ionic interfaces. The current challenges and future developments of ion-transport-based sensory systems are also discussed.

An On-Skin Electrode with Anti-Epidermal-Surface-Lipid Function Based on a Zwitterionic Polymer Brush.

On-skin flexible devices provide a noninvasive approach for continuous and real-time acquisition of biological signals from the skin, which is essential for future chronic disease diagnosis and smart health monitoring. Great progress has been achieved in flexible devices to resolve the mechanical mismatching between conventional rigid devices and human skin. However, common materials used for flexible devices including silicon-based elastomers and various metals exhibit no resistance to epidermal surface lipids (skin oil and grease), which restricts the long-term and household usability. Herein, an on-skin electrode with anti-epidermal-surface-lipid function is reported, which is based on the grafting of a zwitterionic poly(2-methacryl-oyloxyethyl, methacryloyl-oxyethyl, or meth-acryloyloxyethyl phosphorylcholine) (PMPC) brush on top of gold-coated poly(dimethylsiloxane) (Au/PDMS). Such an electrode allows the skin-lipids-fouled surface to be cleaned by simple water rinsing owing to the superhydrophilic zwitterionic groups. As a proof-of-concept, the PMPC-Au/PDMS electrodes are employed for both electrocardiography (ECG) and electromyography (EMG) recording. The electrodes are able to maintain stable skin-electrode impedance and good signal-to noise ratio (SNR) by water rinsing alone. This work provides a material-based solution to improve the long-term reusability of on-skin electronics and offers a unique prospective on developing next generation wearable healthcare devices.

Locally coupled electromechanical interfaces based on cytoadhesion-inspired hybrids to identify muscular excitation-contraction signatures.

Coupling myoelectric and mechanical signals during voluntary muscle contraction is paramount in human-machine interactions. Spatiotemporal differences in the two signals intrinsically arise from the muscular excitation-contraction process; however, current methods fail to deliver local electromechanical coupling of the process. Here we present the locally coupled electromechanical interface based on a quadra-layered ionotronic hybrid (named as CoupOn) that mimics the transmembrane cytoadhesion architecture. CoupOn simultaneously monitors mechanical strains with a gauge factor of ~34 and surface electromyogram with a signal-to-noise ratio of 32.2 dB. The resolved excitation-contraction signatures of forearm flexor muscles can recognize flexions of different fingers, hand grips of varying strength, and nervous and metabolic muscle fatigue. The orthogonal correlation of hand grip strength with speed is further exploited to manipulate robotic hands for recapitulating corresponding gesture dynamics. It can be envisioned that such locally coupled electromechanical interfaces would endow cyber-human interactions with unprecedented robustness and dexterity.