An Artificial Reflex Arc That Perceives Afferent Visual and Tactile Information and Controls Efferent Muscular Actions

Antiferromagnetic artificial neuron modeling of the withdrawal reflex

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex.

Dual-Adaptive Heterojunction Synaptic Transistors for Efficient Machine Vision in Harsh Lighting Conditions

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes.

Thermally and Mechanically Stable Perovskite Artificial Synapse as Tuned by Phase Engineering for Efferent Neuromuscular Control

The doping of perovskites with mixed cations and mixed halides is an effective strategy to optimize phase stability. In this study, we introduce a cubic black phase perovskite CsyFA(1-y)Pb(BrxI(1-x))3 artificial synapse, using phase engineering by adjusting the cesium-bromide content. Low-bromine mixed perovskites are suitable to improve the electric pulse excitation sensitivity and stability of the device. Specifically, the low-bromine and low-cesium mixed perovskite (x = 0.15, y = 0.22) annealed at 373 K allows the device to maintain logic response even after 1000 mechanical flex/flat cycles. The device also shows good thermal stability up to temperatures of 333 K. We have demonstrated reflex-arc behavior with MCMHP synaptic units, capable of making sensory warnings at high frequency. This compositionally engineered, dual-mixed perovskite synaptic device provides significant potential for perceptual soft neurorobotic systems and prostheses.

Enhancement-Mode Carbon Nanotube Optoelectronic Synaptic Transistors with Large and Controllable Threshold Voltage Modulation Window for Broadband Flexible Vision Systems

The development of large-scale integration of optoelectronic neuromorphic devices with ultralow power consumption and broadband responses is essential for high-performance bionics vision systems. In this work, we developed a strategy to construct large-scale (40 × 30) enhancement-mode carbon nanotube optoelectronic synaptic transistors with ultralow power consumption (33.9 aJ per pulse) and broadband responses (from 365 to 620 nm) using low-work function yttrium (Y)-gate electrodes and the mixture of eco-friendly photosensitive Ag2S quantum dots (QDs) and ionic liquids (ILs)-cross-linking-poly(4-vinylphenol) (PVP) (ILs-c-PVP) as the dielectric layers. Solution-processable carbon nanotube thin-film transistors (TFTs) showed enhancement-mode characteristics with the wide and controllable threshold voltage window (-1 V∼0 V) owing to use of the low-work-function Y-gate electrodes. It is noted that carbon nanotube optoelectronic synaptic transistors exhibited high on/off ratios (>106), small hysteresis and low operating voltage (≤2 V), and enhancement mode even under the illumination of ultraviolet (UV, 365 nm), blue (450 nm), and green (550 nm) to red (620 nm) pulse lights when introducing eco-friendly Ag2S QDs in dielectric layers, demonstrating that they have the strong fault-tolerant ability for the threshold voltage drifts caused by various manufacturing scenarios. Furthermore, some important bionic functions including a high paired pulse facilitation index (PPF index, up to 290%), learning and memory function with the long duration (200 s), and rapid recovery (2 s). Pavlov's dog experiment (retention time up to 20 min) and visual memory forgetting experiments (the duration of high current for 180 s) are also demonstrated. Significantly, the optoelectronic synaptic transistors can be used to simulate the adaptive process of vision in varying light conditions, and we demonstrated the dynamic transition of light adaptation to dark adaptation based on light-induced conditional behavior. This work undoubtedly provides valuable insights for the future development of artificial vision systems.

An Ultrasensitive Biomimetic Optic Afferent Nervous System with Circadian Learnability

The optic afferent nervous system (OANS) plays a significant role in generating vision and circadian behaviors based on light detection and signals from the endocrine system. However, the bionic simulation of this photochemically mediated behavior is still a challenge for neuromorphic devices. Herein, stimuli of neurotransmitters at ultralow concentrations and illumination are coupled to artificial synapses with the aid of biofunctionalized heterojunction and tunneling to successfully simulate a circadian neural response. Furthermore, the mechanisms underlying the photosensitive synaptic current in response to stimuli are described. Interestingly, this OANS is demonstrated to be capable of mimicking normal and abnormal circadian learnability by combining the measured synaptic current with a three-layer spike neural network. Strong theoretical and experimental evidence, as well as applications, are provided for the proposed biomimetic OANS to demonstrate that it can reproduce biological circadian behavior, thus establishing it as a promising candidate for future neuromorphic intelligent robots.

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.

Synaptic Characteristics of Atomic Layer-Deposited Ferroelectric Lanthanum-Doped HfO2 (La:HfO2) and TaN-Based Artificial Synapses

Analog synaptic devices have made significant advances based on various electronic materials that can realize the biological synapse properties of neuromorphic computing. Ferroelectric (FE) HfO2-based materials with nonvolatile and low power consumption characteristics are being studied as promising materials for application to analog synaptic devices. The gradual reversal of FE multilevel polarization results in precise changes in the channel conductance and allows analogue synaptic weight updates. However, there have been few studies of FE synaptic devices doped with La, Y, and Gd. Furthermore, an investigation of interface quality is also crucial to enhance the remnant polarization (Pr), synaptic conductance linearity, and reliability characteristics. In this study, we demonstrate improved FE and artificial synaptic characteristics using an atomic layer-deposited (ALD) lanthanum-doped HfO2 (La:HfO2) and TaN electrode in the structure of an FE thin-film transistor (ITO/IGZO/La:HfO2/TaN), where indium-tin oxide (ITO) and indium-gallium-zinc oxide (IGZO) were used as source/drain and channel materials, respectively. Improved Pr and lower surface roughness were achieved by doped HfO2 and ALD TaN thin films. This synaptic transistor shows long-term potentiation and long-term depression with 200 levels of conductance states, high linearity (Ap, 0.97; Ad, 0.86), high Gmax/Gmin (∼6.1), and low cycle-to-cycle variability. In addition, a pattern recognition accuracy higher than 90% was achieved in an artificial neural network simulation.

A bio-inspired visuotactile neuron for multisensory integration

Multisensory integration is a salient feature of the brain which enables better and faster responses in comparison to unisensory integration, especially when the unisensory cues are weak. Specialized neurons that receive convergent input from two or more sensory modalities are responsible for such multisensory integration. Solid-state devices that can emulate the response of these multisensory neurons can advance neuromorphic computing and bridge the gap between artificial and natural intelligence. Here, we introduce an artificial visuotactile neuron based on the integration of a photosensitive monolayer MoS2 memtransistor and a triboelectric tactile sensor which minutely captures the three essential features of multisensory integration, namely, super-additive response, inverse effectiveness effect, and temporal congruency. We have also realized a circuit which can encode visuotactile information into digital spiking events, with probability of spiking determined by the strength of the visual and tactile cues. We believe that our comprehensive demonstration of bio-inspired and multisensory visuotactile neuron and spike encoding circuitry will advance the field of neuromorphic computing, which has thus far primarily focused on unisensory intelligence and information processing.

Technology Roadmap for Flexible Sensors

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

Flexible Organic-Inorganic Halide Perovskite-Based Diffusive Memristor for Artificial Nociceptors

With the current evolution in the artificial intelligence technology, more biomimetic functions are essential to execute increasingly complicated tasks and respond to challenging work environments. Therefore, an artificial nociceptor plays a significant role in the advancement of humanoid robots. Organic-inorganic halide perovskites (OHPs) have the potential to mimic the biological neurons due to their inherent ion migration. Herein, a versatile and reliable diffusive memristor built on an OHP is reported as an artificial nociceptor. This OHP diffusive memristor showed threshold switching properties with excellent uniformity, forming-free behavior, a high I_ON/I_OFF ratio (10⁴), and bending endurance over >10² cycles. To emulate the biological nociceptor functionalities, four significant characteristics of the artificial nociceptor, such as threshold, no adaptation, relaxation, and sensitization, are demonstrated. Further, the feasibility of OHP nociceptors in artificial intelligence is being investigated by fabricating a thermoreceptor system. These findings suggest a prospective application of an OHP-based diffusive memristor in the future neuromorphic intelligence platform.

Neuromorphic Gustatory System with Salt-Taste Perception, Information Processing, and Excessive-Intake Warning Capabilities

Emulation of the process of a biological gustatory system could benefit the reconstruction of sense of taste. Here we demonstrate the first neuromorphic gustatory system that emulates the ability of taste perception, information processing, and excessive-intake warning functions. The system integrates a chitosan-derived ion-gel sensor, SnO₂ nanowire artificial synapses, and an effect-executive unit. The system accomplish perception and encoding behaviors for taste stimulation without using complex circuits and multivariate analysis, showing short response delay (<1 s), long taste memory duration (>2 h), and a wide perceptive concentration range (0.02-6 wt % salt solution). Especially, SnO₂ NW artificial synapses have extremely small response voltage (1 mV), exceeding the biological level by orders of magnitude, representing so-far the highest sensitivity record. This work provides a promising strategy to develop bioinspired and biointegrated electronics with the intention of mimicking and restoring the functions of biological sensory systems.

Recent Advances in Transistor-Based Bionic Perceptual Devices for Artificial Sensory Systems

The sensory nervous system serves as the window for human beings to perceive the outside world by converting external stimuli into distinctive spiking trains. The sensory neurons in this system can process multimodal sensory signals with extremely low power consumption. Therefore, new-concept devices inspired by the sensory neuron are promising candidates to address energy issues in nowadays’ robotics, prosthetics and even computing systems. Recent years have witnessed rapid development in transistor-based bionic perceptual devices, and it is urgent to summarize the research and development of these devices. In this review, the latest progress of transistor-based bionic perceptual devices for artificial sense is reviewed and summarized in five aspects, i.e., vision, touch, hearing, smell, and pain. Finally, the opportunities and challenges related to these areas are also discussed. It would have bright prospects in the fields of artificial intelligence, prosthetics, brain-computer interface, robotics, and medical testing.

Artificial Vision Adaption Mimicked by an Optoelectrical In2O3 Transistor Array

Simulation of biological visual perception has gained considerable attention. In this paper, an optoelectrical In₂O₃ transistor array with a negative photoconductivity behavior is designed using a side-gate structure and a screen-printed ion-gel as the gate insulator. This paper is the first to observe a negative photoconductivity in electrolyte-gated oxide devices. Furthermore, an artificial visual perception system capable of self-adapting to environmental lightness is mimicked using the proposed device array. The transistor device array shows a self-adaptive behavior of light under different levels of light intensity, successfully demonstrating the visual adaption with an adjustable threshold range to the external environment. This study provides a new way to create an environmentally adaptive artificial visual perception system and has far-reaching significance for the future of neuromorphic electronics.