Bioinspired Multifunctional Organic Transistors Based on Natural Chlorophyll/Organic Semiconductors

Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception

Neuromorphic visual systems can emulate biological retinal systems to perceive visual information under different levels of illumination, making them have considerable potential for future intelligent vehicles and vision automation. However, the complex circuits and high operating voltages of conventional artificial vision systems present great challenges for device integration and power consumption. Here, bioinspired synaptic transistors based on organic single crystal phototransistors are reported, which exhibit excitation and inhibition synaptic plasticity with time-varying. By manipulating the charge dynamics of the trapping centers of organic crystal-electret vertical stacks, organic transistors can operate below 1 V with record high on/off ratios close to 108 and sharp switching with a subthreshold swing of 59.8 mV dec-1. Moreover, the approach offers visual adaptation with highly localized modulation and over 98.2% recognition accuracy under different illumination levels. These bioinspired visual adaptation transistors offer great potential for simplifying the circuitry of artificial vision systems and will contribute to the development of machine vision applications.

Ultra-low power carbon nanotube/porphyrin synaptic arrays for persistent photoconductivity and neuromorphic computing

Developing devices with a wide-temperature range persistent photoconductivity (PPC) and ultra-low power consumption remains a significant challenge for optical synaptic devices used in neuromorphic computing. By harnessing the PPC properties in materials, it can achieve optical storage and neuromorphic computing, surpassing the von Neuman architecture-based systems. However, previous research implemented PPC required additional gate voltages and low temperatures, which need additional energy consumption and PPC cannot be achieved across a wide temperature range. Here, we fabricated a simple heterojunctions using zinc(II)-meso-tetraphenyl porphyrin (ZnTPP) and single-walled carbon nanotubes (SWCNTs). By leveraging the strong binding energy at the heterojunction interface and the unique band structure, the heterojunction achieved PPC over an exceptionally wide temperature range (77 K-400 K). Remarkably, it demonstrated nonvolatile storage for up to 2×104 s, without additional gate voltage. The minimum energy consumption for each synaptic event is as low as 6.5 aJ. Furthermore, we successfully demonstrate the feasibility to manufacture a flexible wafer-scale array utilizing this heterojunction. We applied it to autonomous driving under extreme temperatures and achieved as a high impressive accuracy rate as 94.5%. This tunable and stable wide-temperature PPC capability holds promise for ultra-low-power neuromorphic computing.

Photovoltaic nanocells for high-performance large-scale-integrated organic phototransistors

A high-performance large-scale-integrated organic phototransistor needs a semiconductor layer that maintains its photoelectric conversion ability well during high-resolution pixelization. However, lacking a precise design for the nanoscale structure, a trade-off between photoelectric performance and device miniaturization greatly limits the success in commercial application. Here we demonstrate a photovoltaic-nanocell enhancement strategy, which overcomes the trade-off and enables high-performance organic phototransistors at a level beyond large-scale integration. Embedding a core-shell photovoltaic nanocell based on perovskite quantum dots in a photocrosslinkable organic semiconductor, ultralarge-scale-integrated (>221 units) imaging chips are manufactured using photolithography. 27 million pixels are interconnected and the pixel density is 3.1 × 106 units cm-2, at least two orders of magnitude higher than in existing organic imaging chips and equivalent to the latest commercial full-frame complementary metal-oxide-semiconductor camera chips. The embedded photovoltaic nanocells induce an in situ photogating modulation and enable photoresponsivity and detectivity of 6.8 × 106 A W-1 and 1.1 × 1013 Jones (at 1 Hz), respectively, achieving the highest values of organic imaging chips at large-scale or higher integration. In addition, a very-large-scale-integrated (>216 units) stretchable biomimetic retina based on photovoltaic nanocells is manufactured for neuromorphic imaging recognition with not only resolution but also photoresponsivity and power consumption approaching those of the biological counterpart.

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.

Spacer Engineering Enables Fine-Tuned Thin Film Microstructure and Efficient Charge Transport for Ultrasensitive 2D Perovskite-Based Heterojunction Phototransistors and Optoelectronic Synapses

2D Ruddlesden-Popper phase layered perovskites (RPLPs) hold great promise for optoelectronic applications. In this study, a series of high-performance heterojunction phototransistors (HPTs) based on RPLPs with different organic spacer cations (namely butylammonium (BA+), cyclohexylammonium (CyHA+), phenethylammonium (PEA+), p-fluorophenylethylammonium (p-F-PEA+), and 2-thiophenethylammonium (2-ThEA+)) are fabricated successfully, in which high-mobility organic semiconductor 2,7-dioctyl[1]benzothieno[3,2-b]benzothiophene is adopted to form type II heterojunction channels with RPLPs. The 2-ThEA+-RPLP-based HPTs show the highest photosensitivity of 3.18 × 107 and the best detectivity of 9.00 × 1018 Jones, while the p-F-PEA+-RPLP-based ones exhibit the highest photoresponsivity of 5.51 × 106 A W-1 and external quantum efficiency of 1.32 × 109%, all of which are among the highest reported values to date. These heterojunction systems also mimicked several optically controllable fundamental characteristics of biological synapses, including excitatory postsynaptic current, paired-pulse facilitation, and the transition from short-term memory to long-term memory states. The device based on 2-ThEA+-RPLP film shows an ultra-high PPF index of 234%. Moreover, spacer engineering brought fine-tuned thin film microstructures and efficient charge transport/transfer, which contributes to the superior photodetection performance and synaptic functions of these RPLP-based HPTs. In-depth structure-property correlations between the organic spacer cations/RPLPs and thin film microstructure/device performance are systematically investigated.

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.

Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review

Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.

Overcoming the Unfavorable Effects of "Boltzmann Tyranny:" Ultra-Low Subthreshold Swing in Organic Phototransistors via One-Transistor-One-Memristor Architecture

Organic phototransistors (OPTs), as photosensitive organic field-effect transistors (OFETs), have gained significant attention due to their pivotal roles in imaging, optical communication, and night vision. However, their performance is fundamentally limited by the Boltzmann distribution of charge carriers, which constrains the average subthreshold swing (SSave ) to a minimum of 60 mV/decade at room temperature. In this study, an innovative one-transistor-one-memristor (1T1R) architecture is proposed to overcome the Boltzmann limit in conventional OFETs. By replacing the source electrode in an OFET with a memristor, the 1T1R device exploits the memristor's sharp resistance state transitions to achieve an ultra-low SSave of 18 mV/decade. Consequently, the 1T1R devices demonstrate remarkable sensitivity to photo illumination, with a high specific detectivity of 3.9 × 109  cm W-1 Hz1/2 , outperforming conventional OPTs (4.9 × 104  cm W-1 Hz1/2 ) by more than four orders of magnitude. The 1T1R architecture presents a potentially universal solution for overcoming the detrimental effects of "Boltzmann tyranny," setting the stage for the development of ultra-low SSave devices in various optoelectronic applications.

Towards Artificial Visual Sensory System: Organic Optoelectronic Synaptic Materials and Devices

Developing an artificial visual sensory system requires optoelectronic materials and devices that can mimic the behavior of biological synapses. Organic/polymeric semiconductors have emerged as promising candidates for optoelectronic synapses due to their tunable optoelectronic properties, mechanic flexibility, and biological compatibility. In this review, we discuss the recent progress in organic optoelectronic synaptic materials and devices, including their design principles, working mechanisms, and applications. We also highlight the challenges and opportunities in this field and provide insights into potential applications of these materials and devices in next-generation artificial visual systems. By leveraging the advances in organic optoelectronic materials and devices, we can envision its future development in artificial intelligence.

Retina-inspired organic neuromorphic vision sensor with polarity modulation for decoding light information

The neuromorphic vision sensor (NeuVS), which is based on organic field-effect transistors (OFETs), uses polar functional groups (PFGs) in polymer dielectrics as interfacial units to control charge carriers. However, the mechanism of modulating charge transport on basis of PFGs in devices is unclear. Here, the carboxyl group is introduced into polymer dielectrics in this study, and it can induce the charge transfer process at the semiconductor/dielectric interfaces for effective carrier transport, giving rise to the best device mobility up to 20 cm2 V-1 s-1 at a low operating voltage of -1 V. Furthermore, the polarity modulation effect could further increase the optical figures of merit in NeuVS devices by at least an order of magnitude more than the devices using carboxyl group-free polymer dielectrics. Additionally, devices containing carboxyl groups improved image sensing for light information decoding with 52 grayscale signals and memory capabilities at an incredibly low power consumption of 1.25 fJ/spike. Our findings provide insight into the production of high-performance polymer dielectrics for NeuVS devices.

Unraveling the Effect of Stereoisomerism on Mobility-Stretchability Properties of n-Type Semiconducting Polymers with Biobased Epimers as Conjugation Break Spacers

The development of intrinsically stretchable n-type semiconducting polymers has garnered much interest in recent years. In this study, three biobased dianhydrohexitol epimers of isosorbide (ISB), isomannide (IMN), and isoidide (IID), derived from cellulose, were incorporated into the backbone of a naphthalenediimide (NDI)-based n-type semiconducting polymer as conjugation break spacers (CBSs). Accordingly, three polymers were synthesized through the Migita-Kosugi-Stille coupling polymerization with NDI, bithiophene, and CBSs, and the mobility-stretchability properties of these polymers were investigated and compared with those of their analogues with conventional alkyl-based CBSs. Experimental results showed that the different configurations of these epimers in CBSs sufficiently modulate the melt entropies, surface aggregation, crystallographic parameters, chain entanglements, and mobility-stretchability properties. Comparable ductility and edge-on preferred stacking were observed in polymers with endo- or exo-configurations in IMN- and IID-based polymers. By contrast, ISB with endo-/exo-configurations exhibits an excellent chain-realigning capability, a reduced crack density, and a proceeding bimodal orientation under tensile strain. Therefore, the ISB-based polymer exhibits high orthogonal electron mobility retention of (53 and 56)% at 100% strain. This study is one of the few examples where biobased moieties are incorporated into semiconducting polymers as stress-relaxation units. Additionally, this is the first study to report on the effect of stereoisomerism of epimers on the morphology and mobility-stretchability properties of semiconducting polymers.

Enhanced Multiwavelength Response of Flexible Synaptic Transistors for Human Sunburned Skin Simulation and Neuromorphic Computation

In biological species, optogenetics and bioimaging work together to regulate the function of neurons. Similarly, the light-controlled artificial synaptic system not only enhances computational speed but also simulates complex synaptic functions. However, reported synaptic properties are mainly limited to mimicking simple biological functions and single-wavelength responses. Therefore, the development of flexible synaptic devices with multiwavelength optical signal response and multifunctional simulation remains a challenge. Here, flexible organic light-stimulated synaptic transistors (LSSTs) enabled by alumina oxide (AlOX ), with a simple fabrication process, are reported. By embedding AlOX nanoparticles, the excitons separation efficiency is improved, allowing for multiple wavelength responses. Optimized LSSTs can respond to multiple optical and electrical signals in a highly synaptic manner. Multiwavelength optical synaptic plasticity, electrical synaptic plasticity, sunburned skin simulation, learning efficiency model controlled by photoelectric cooperative stimulation, neural network computing, "deer" picture learning and memory functions are successfully proposed, which promote the development for future artificial intelligent systems. Furthermore, as prepared flexible transistors exhibit mechanical flexibility with bending radius down to 2.5 mm and improved photosynaptic plasticity, which facilitating development of neuromorphic computing and multifunction integration systems at the device-level.

Helical Nanofiber Photoelectric Synaptic Devices for an Artificial Vision Nervous System

Inspired by the helical structure and the resultant exquisite functions of biomolecules, helical polymers have received increasing attention. Here, a series of poly(3-hexylthiophene)-block-poly(phenyl isocyanide) (P3HT-b-PPI) copolymers were prepared using a simple one-pot living polymerization method. Interestingly, the P3HT80-b-PPI30 films were found to have a helical nanofiber structure. The corresponding device has superior optoelectronic properties, such as a broadened spectral response range from the visible band to the deep ultraviolet (DUV) and an approximately 5-fold longer carrier decay time after DUV light stimulation. An energy consumption of 1.44 fJ per synaptic event was obtained, which is the lowest energy consumption achieved so far with DUV light stimulation. The encryption and decryption of images are implemented using an array of devices. Finally, a photoreceptor neural pathway was constructed to achieve early warning for the recognition of the display of harmful light. This research provides an effective strategy for the development of a novel optoelectronic synaptic device.

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.

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.

Multifunctional Crystalline InGaSnO Phototransistor Exhibiting Photosensing and Photosynaptic Behavior Using Oxygen Vacancy Engineering

A multifunctional optoelectronic device implementing photodetector, photosynapse, and photomemory is of increasing attention for neuromorphic system. This enables multiple devices to be replaced with a single device, which simplifies the structure of complex, highly integrated electronics. Here, a multifunctional c-axis-aligned crystalline indium gallium tin oxide thin-film transistor (TFT) optoelectronic device is demonstrated. The photodetecting and photosynaptic behaviors could be demonstrated by tuning of gate pulse. The device shows a high responsivity of 1.1 × 106  A W-1 to blue light (467 nm) and cutoff frequency (f-3dB ) of 2400 Hz exhibiting high frequency switching using a gate reset pulse. It is possible to implement photosynaptic behavior using persistent photoconductivity effect by applying a gate bias to make the TFT depletion mode. When potentiation and depression of synaptic weight are implemented with light pulse and gate voltage pulse, respectively, 64-state potentiation-depression curves are demonstrated with excellent nonlinearity of 1.13 and 2.03, respectively. When an artificial neural network is constructed with this device for the Modified National Institute of Standards and Technology training pattern recognition simulation, it shows a high pattern recognition accuracy of 90.4%.

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.

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.

Organic Optoelectronic Synapses for Sound Perception

The neuromorphic systems for sound perception is under highly demanding for the future bioinspired electronics and humanoid robots. However, the sound perception based on volume, tone and timbre remains unknown. Herein, organic optoelectronic synapses (OOSs) are constructed for unprecedented sound recognition. The volume, tone and timbre of sound can be regulated appropriately by the input signal of voltages, frequencies and light intensities of OOSs, according to the amplitude, frequency, and waveform of the sound. The quantitative relation between recognition factor (ζ) and postsynaptic current (I = Ilight - Idark) is established to achieve sound perception. Interestingly, the bell sound for University of Chinese Academy of Sciences is recognized with an accuracy of 99.8%. The mechanism studies reveal that the impedance of the interfacial layers play a critical role in the synaptic performances. This contribution presents unprecedented artificial synapses for sound perception at hardware levels.

Molecular template growth of organic heterojunctions to tailor visual neuroplasticity for high performance phototransistors with ultralow energy consumption

The optical and charge transport properties of organic semiconductors are strongly influenced by their morphology and molecular structures. Here we report the influence of a molecular template strategy on anisotropic control via weak epitaxial growth of a semiconducting channel for a dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DNTT)/para-sexiphenyl (p-6P) heterojunction. The aim is to improve charge transport and trapping, to enable tailoring of visual neuroplasticity. The proposed phototransistor devices, comprising a molecular heterojunction with optimized molecular template thickness, exhibited an excellent memory ratio (I_ON/I_OFF) and retention characteristics in response to light stimulation, owing to the enhanced orientation/packing of DNTT molecules and a favorable match between the LUMO/HOMO levels of p-6P and DNTT. The best performing heterojunction exhibits visual synaptic functionalities, including an extremely high pair-pulse facilitation index of ∼206%, ultralow energy consumption of 0.54 fJ, and zero-gate operation, under ultrashort pulse light stimulation to mimic human-like sensing, computing, and memory functions. An array of heterojunction photosynapses possess a high degree of visual pattern recognition and learning, to mimic the neuroplasticity of human brain activities through a rehearsal learning process. This study provides a guide to the design of molecular heterojunctions for tailoring high-performance photonic memory and synapses for neuromorphic computing and artificial intelligence systems.

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.

Retina-Inspired Organic Photonic Synapses for Selective Detection of SWIR Light

Photonic synapses with the dual function of optical signal detection and information processing can simulate human visual system. However, photonic synapses with selective detection of short-wavelength infrared (SWIR) light have never been reported, which can not only broaden the human vision region but also integrate neuromorphic computation and infrared optical communication. Here, organic photonic synapses based on a new donor-acceptor copolymer P1 are fabricated, which exhibit excellent synaptic characteristics with selective detection for SWIR and extremely low energy consumption (2.85 fJ). The working mechanism is rooted in energy level barriers and unbalanced charge transportation. Moreover, these photonic synapses demonstrate excellent performance in multi-signal logic editing, letter imaging and memory with noise reduction function. This contribution provides ideas of constructing selective-response synapses for artificial visual system and neuromorphic computing.

Multimodal-Synergistic-Modulation Neuromorphic Imaging Systems for Simulating Dry Eye Imaging

Inspired by human eyes, the neuromorphic visual system employs a highly efficient imaging and recognition process, which offers tremendous advantages in image acquisition, data pre-processing, and dynamic storage. However, it is still an enormous challenge to simultaneously simulate the structure, function, and environmental adaptive behavior of the human eye based on one device. Here, a multimodal-synergistic-modulation neuromorphic imaging system based on ultraflexible synaptic transistors is successfully presented and firstly simulates the dry eye imaging behavior at the device level. Moreover, important functions of the human visual system in relation to optoelectronic synaptic plasticity, image erasure and enhancement, real-time preprocessing, and dynamic storage are simulated by versatile devices. This work not only simplifies the complexity of traditional neuromorphic visual systems, but also plays a positive role in the publicity of biomedical eye care.

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.

Artificial Tactile Recognition Enabled by Flexible Low-Voltage Organic Transistors and Low-Power Synaptic Electronics

The advancement of self-powered intelligent strain systems for human-computer interaction is crucial toward wearable and energy-saving applications. Simultaneously, lowering operating voltage and thus reducing power consumption are of particular interests. A brain-like smart synaptic hardware system is considered as a promising candidate for low-power, parallel computing and learning processes. However, the combination of low-voltage organic transistors and energy efficient smart synapse hardware systems driven by a tactile signal has been hindered by the limited materials and technology. Here, by employing an elastomeric copolymer poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) with a high HFP content of 25 mol %, flexible, low-voltage transistors (|V_G| ≤ 3 V) and a low energy consumption synapse ≤ 9.2 × 10^-17 J are devised simultaneously, along with the lowest quality factor (R = P_w × V_G, 2.76 × 10^-16 J V). Furthermore, based on the low voltage and low power consumption characteristics, flexible artificial tactile recognition system and Morse code recognition are established without any computing supporting. Mechanical flexibility, cycling stability, image contrast enhancement functions, and simulated pattern recognition accuracy of the multilayer perceptron neural network are also simulated. This work recommends a route of exploiting low voltage, low power consumption synaptic systems and smart human-machine interfaces with low energy loss based on flexible organic synaptic transistors.

Tuning Ambipolarity of the Conjugated Polymer Channel Layers of Floating-Gate Free Transistors: From Volatile Memories to Artificial Synapses

Three-terminal synaptic transistor has drawn significant research interests for neuromorphic computation due to its advantage of facile device integrability. Lately, bulk-heterojunction-based synaptic transistors with bipolar modulation are proposed to exempt the use of an additional floating gate. However, the actual correlation between the channel's ambipolarity, memory characteristic, and synaptic behavior for a floating-gate free transistor has not been investigated yet. Herein, by studying five diketopyrrolopyrrole-benzotriazole dual-acceptor random conjugated polymers, a clear correlation among the hole/electron ratio, the memory retention characteristic, and the synaptic behavior for the polymer channel layer in a floating-gate free transistor is described. It reveals that the polymers with balanced ambipolarity possess better charge trapping capabilities and larger memory windows; however, the high ambipolarity results in higher volatility of the memory characteristics, namely poor memory retention capability. In contrast, the polymer with a reduced ambipolarity possesses an enhanced memory retention capability despite showing a reduced memory window. It is further manifested that this enhanced charge retention capability enables the device to present artificial synaptic characteristics. The results highlight the importance of the channel's ambipolarity of floating-gate free transistors on the resultant volatile memory characteristics and synaptic behaviors.

A Fully Solution-Printed Photosynaptic Transistor Array with Ultralow Energy Consumption for Artificial-Vision Neural Networks

Photosynaptic organic field-effect transistors (OFETs) represent a viable pathway to develop bionic optoelectronics. However, the high operating voltage and current of traditional photosynaptic OFETs lead to huge energy consumption greater than that of the real biological synapses, hindering their further development in new-generation visual prosthetics and artificial perception systems. Here, a fully solution-printed photosynaptic OFET (FSP-OFET) with substantial energy consumption reduction is reported, where a source Schottky barrier is introduced to regulate charge-carrier injection, and which operates with a fundamentally different mechanism from traditional devices. The FSP-OFET not only significantly lowers the working voltage and current but also provides extraordinary neuromorphic light-perception capabilities. Consequently, the FSP-OFET successfully emulates visual nervous responses to external light stimuli with ultralow energy consumption of 0.07-34 fJ per spike in short-term plasticity and 0.41-19.87 fJ per spike in long-term plasticity, both approaching the energy efficiency of biological synapses (1-100 fJ). Moreover, an artificial optic-neural network made from an 8 × 8 FSP-OFET array on a flexible substrate shows excellent image recognition and reinforcement abilities at a low energy cost. The designed FSP-OFET offers an opportunity to realize photonic neuromorphic functionality with extremely low energy consumption dissipation.

Fast Photoresponsive Phototransistor Memory Using Star-Shaped Conjugated Rod-Coil Molecules as a Floating Gate

With the explosive growth in data generation, photomemory capable of multibit data storage is highly desired to enhance the capacity of storage media. To improve the performance of phototransistor memory, an organic-molecule-based electret with an elaborate nanostructure is of great importance because it can enable multibit data storage in a memory device with high stability. In this study, a series of star-shaped rod-coil molecules consisting of perylenediimide (PDI) and biobased solanesol were synthesized in two-armed (PDI-Sol2), four-armed (PDI-Sol4), and six-armed (PDI-Sol6) architectures. Their molecular architecture-morphology relationships were investigated, and phototransistor memory was fabricated and characterized to evaluate the structure-performance relationship of these rod-coil molecules. Accordingly, the memory devices were enabled by photowriting with panchromatic light (405-650 nm) and electrical erasing using a gate bias. The PDI-Sol4-based memory device showed high memory ratios of 10 000 over 10 000 s and a rapid multilevel photoresponse of 50 ms. This achievement is related to the favorable energy-level alignment, isolated nanostructure, and face-on orientation of PDI-Sol4, which eliminated the charge tunneling barrier. The results of this study provide a new strategy for tailoring nanostructures in organic-molecule-based electrets by using a star-shaped rod-coil architecture for high-performance phototransistor memory.

Thermally Stable Organic Field-Effect Transistors Based on Asymmetric BTBT Derivatives for High Performance Solar-Blind Photodetectors

High-performance solar-blind photodetectors are widely studied due to their unique significance in military and industrial applications. Yet the rational molecular design for materials to possess strong absorption in solar-blind region is rarely addressed. Here, an organic solar-blind photodetector is reported by designing a novel asymmetric molecule integrated strong solar-blind absorption with high charge transport property. Such alkyl substituted [1]benzothieno[3,2-b][1]-benzothiophene (BTBT) derivatives Cn-BTBTN (n = 6, 8, and 10) can be easily assembled into 2D molecular crystals and perform high mobility up to 3.28 cm² V^-1 s^-1 , which is two orders of magnitude higher than the non-substituted core BTBTN. Cn-BTBTNs also exhibit dramatically higher thermal stability than the symmetric alkyl substituted C8-BTBT. Moreover, C10-BTBTN films with the highest mobility and strongest solar-blind absorption among the Cn-BTBTNs are applied for solar-blind photodetectors, which reveal record-high photosensitivity and detectivity up to 1.60 × 10⁷ and 7.70 × 10¹⁴ Jones. Photodetector arrays and flexible devices are also successfully fabricated. The design strategy can provide guidelines for developing materials featuring high thermal stability and stimulating such materials in solar-blind photodetector application.

Design of Functionally Stacked Channels of Oxide Thin-Film Transistors to Mimic Precise Ultralow-Light-Irradiated Synaptic Weight Modulation

To utilize continuous ultralow intensity signals from oxide synaptic transistors as artificial synapses that mimic human visual perception, we propose strategic oxide channels that optimally utilize their advantageous functions by stacking two oxide semiconductors with different conductivities. The bottom amorphous indium–gallium–zinc oxide (a-IGZO) layer with a relatively low conductivity was designed for an extremely low initial postsynaptic current (PSCi) by achieving full depletion at a low negative gate voltage, and the stacked top amorphous indium–zinc oxide (a-IZO) layer improved the amplitude of the synaptic current and memory retention owing to the enhancement in the persistent photoconductivity characteristics. We demonstrated an excellent photonic synapse thin-film transistor (TFT) with a precise synaptic weight change even in the range of ultralow light intensity by adapting this stacking IGZO/IZO channel. The proposed device exhibited distinct ∆PSC values of 3.1 and 18.1 nA under ultralow ultraviolet light (350 nm, 50 ms) of 1.6 and 8.0 μW/cm2. In addition, while the lowest light input exhibited short-term plasticity characteristics similar to the “volatile-like” behavior of the human brain with a current recovery close to the initial value, the increase in light intensity caused long-term plasticity characteristics, thus achieving synaptic memory transition in the IGZO/IZO TFTs.

Deep Ultraviolet Light Stimulated Synaptic Transistors Based on Poly(3-hexylthiophene) Ultrathin Films

Deep ultraviolet (DUV)-light-stimulated artificial synaptic devices exhibit potential applications in various disciplines including intelligent military monitoring, biological and medical analysis, flame detection, etc. Along these lines, we report here a DUV-light-stimulated synaptic transistor fabricated on a poly(3-hexylthiophene) (P3HT) ultrathin film that responds selectively to DUV light. Significantly, our devices have the ability to successfully simulate various synapse-like behaviors including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), short-term memory (STM), long-term memory (LTM), STM-to-LTM transition, and learning and forgetting behaviors. Moreover, the proposed artificial synaptic structures were also fabricated on flexible poly(ethylene terephthalate) (PET) substrates and also successfully simulated typical synaptic behaviors, which could be of great importance for wearable applications.

Low-Energy-Consumption and Electret-Free Photosynaptic Transistor Utilizing Poly(3-hexylthiophene)-Based Conjugated Block Copolymers

Neuromorphic computation possesses the advantages of self-learning, highly parallel computation, and low energy consumption, and is of great promise to overcome the bottleneck of von Neumann computation. In this work, a series of poly(3-hexylthiophene) (P3HT)-based block copolymers (BCPs) with different coil segments, including polystyrene, poly(2-vinylpyridine) (P2VP), poly(2-vinylnaphthalene), and poly(butyl acrylate), are utilized in photosynaptic transistor to emulate paired-pulse facilitation, spike time/rate-dependent plasticity, short/long-term neuroplasticity, and learning-forgetting-relearning processes. P3HT serves as a carrier transport channel and a photogate, while the insulating coils with electrophilic groups are for charge trapping and preservation. Three main factors are unveiled to govern the properties of these P3HT-based BCPs: i) rigidity of the insulating coil, ii) energy levels between the constituent polymers, and iii) electrophilicity of the insulating coil. Accordingly, P3HT-b-P2VP-based photosynaptic transistor with a sought-after BCP combination demonstrates long-term memory behavior with current contrast up to 10⁵ , short-term memory behavior with high paired-pulse facilitation ratio of 1.38, and an ultralow energy consumption of 0.56 fJ at an operating voltage of -0.0003 V. As far as it is known, this is the first work to utilize conjugated BCPs in an electret-free photosynaptic transistor showing great potential to the artificial intelligence technology.

Retina-Inspired Self-Powered Artificial Optoelectronic Synapses with Selective Detection in Organic Asymmetric Heterojunctions

The retina, the most crucial unit of the human visual perception system, combines sensing with wavelength selectivity and signal preprocessing. Incorporating energy conversion into these superior neurobiological features to generate core visual signals directly from incoming light under various conditions is essential for artificial optoelectronic synapses to emulate biological processing in the real retina. Herein, self-powered optoelectronic synapses that can selectively detect and preprocess the ultraviolet (UV) light are presented, which benefit from high-quality organic asymmetric heterojunctions with ultrathin molecular semiconducting crystalline films, intrinsic heterogeneous interfaces, and typical photovoltaic properties. These devices exhibit diverse synaptic behaviors, such as excitatory postsynaptic current, paired-pulse facilitation, and high-pass filtering characteristics, which successfully reproduce the unique connectivity among sensory neurons. These zero-power optical-sensing synaptic operations further facilitate a demonstration of image sharpening. Additionally, the charge transfer at the heterojunction interface can be modulated by tuning the gate voltage to achieve multispectral sensing ranging from the UV to near-infrared region. Therefore, this work sheds new light on more advanced retinomorphic visual systems in the post-Moore era.

A Camel Nose-Inspired Highly Durable Neuromorphic Humidity Sensor with Water Source Locating Capability

Numerous emerging applications in modern society require humidity sensors that are not only sensitive and specific but also durable and intelligent. However, conventional humidity sensors do not have all of these simultaneously because they require very different or even contradictory design principles. Here, inspired by camel noses, we develop a porous zwitterionic capacitive humidity sensor. Relying on the synergistic effect of a porous structure and good chemical and thermal stabilities of hygroscopic zwitterions, this sensor simultaneously exhibits high sensitivity, discriminability, excellent durability, and, in particular, the highest respond speed among reported capacitive humidity sensors, with demonstrated applications in the fast discrimination between fresh, stale, and dry leaves, high-resolution touchless human-machine interactive input devices, and the real-time monitoring humidity level of a hot industrial exhaust. More importantly, this sensor exhibits typical synapse behaviors such as paired-pulse facilitation due to the strong binding interactions between water and zwitterions. This leads to learning and forgetting features with a tunable memory, thus giving the sensor artificial intelligence and enabling the location of water sources. This work offers a general design principle expected to be applied to develop other high-performance biochemical sensors and the next-generation intelligent sensors with much broader applications.

Advances in materials and devices for mimicking sensory adaptation

Adaptive devices, which aim to adjust electrical behaviors autonomically to external stimuli, are considered to be attractive candidates for next-generation artificial perception systems. Compared with typical electronic devices with stable signal output, adaptive devices possess unique features in exhibiting dynamic fitness to varying environments. To meet this requirement, increasing efforts have been made focusing on developing new materials, functional interfaces and novel device geometry for sensory perception applications. In this review, we summarize the recent advances in materials and devices for mimicking sensory adaptation. Keeping this in mind, we first introduce the fundamentals of biological sensory adaptation. Thereafter, the recent progress in mimicking sensory adaptation, such as tactile and visual adaptive systems, is overviewed. Moreover, we suggest five strategies to construct adaptive devices. Finally, challenges and perspectives are proposed to highlight the directions that deserve focused attention in this flourishing field.

Bifunctional homologous alkali-metal artificial synapse with regenerative ability and mechanism imitation of voltage-gated ion channels

As a key component responsible for information processing in the brain, the development of a bionic synapse possessing digital and analog bifunctionality is vital for the hardware implementation of a neuro-system. Here, inspired by the key role of sodium and potassium in synaptic transmission, the alkali metal element lithium (Li) belonging to the same family is adopted in designing a bifunctional artificial synapse. The incorporation of Li endows the electronic devices with versatile synaptic functions. An artificial neural network based on experimental data exhibits a high performance approaching near-ideal accuracy. In addition, the regenerative ability allows synaptic functional recovery through low-frequency stimuli to be emulated, facilitating the prevention of permanent damage due to intensive neural activities and ensuring the long-term stability of the entire neural system. What is more striking for an Li-based bionic synapse is that it can not only emulate a biological synapse at a behavioral level but realize mechanism emulation based on artificial voltage-gated "ion channels". Concurrent digital and analog features lead to versatile synaptic functions in Li-doped artificial synapses, which operate in a mode similar to the human brain with its two hemispheres excelling at processing imaginative and analytical information, respectively.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

Biocompatible artificial synapses based on a zein active layer obtained from maize for neuromorphic computing

Artificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

We present a review of the field of high-throughput virtual screening for organic electronics materials focusing on the sequence of methodological choices that determine each virtual screening protocol. These choices are present in all high-throughput virtual screenings and addressing them systematically will lead to optimised workflows and improve their applicability. We consider the range of properties that can be computed and illustrate how their accuracy can be determined depending on the quality and size of the experimental datasets. The approaches to generate candidates for virtual screening are also extremely varied and their relative strengths and weaknesses are discussed. The analysis of high-throughput virtual screening is almost never limited to the identification of top candidates and often new patterns and structure-property relations are the most interesting findings of such searches. The review reveals a very dynamic field constantly adapting to match an evolving landscape of applications, methodologies and datasets.

Bi-mode electrolyte-gated synaptic transistor via additional ion doping and its application to artificial nociceptors

Multiple types of synaptic transistors that are capable of processing electrical signals similar to the biological neural system hold enormous potential for application in parallel computing, logic circuits and peripheral detection. However, most of these presented synaptic transistors are confined to a single mode of synaptic plasticity under an electrical stimulus, which tremendously limits efficient memory formation and the multifunctional integration of synaptic transistors. Here, we proposed a bi-mode electrolyte-gated synaptic transistor (BEST) with two dynamic processes, the formation of an electrical double layer (EDL) and electrochemical doping (ECD) by tuning the applied voltages, thereby allowing volatile and non-volatile behavior, which is associated with additional ion doping and nanoscale ionic transport. Benefiting from two controllable dynamic processes, we surprisingly found a third state in the transfer curves besides the "off" and "on" states. Moreover, utilizing this unique property, an artificial nociceptor with multilevel modulation of sensitivity was realized based on our bi-mode device. Finally, a haptic sensory system was constructed to exhibit robotic motion that revealed a unique threshold switching behavior, indicating the applicability to peripheral sensing circuits. Hence, the presented bi-mode synaptic transistor provides promising prospects in achieving multiple-mode integrated devices and simplifying neural circuits, which shows great potential in the development of artificial intelligence.

Low-power consumption light-stimulated synaptic transistors based on natural carotene and organic semiconductors

Developing synaptic devices with environment-friendly materials is a promising research direction. Here, light-stimulated synaptic transistors based on natural carotene and organic semiconductors were developed. Several important functions similar to biological synapses were realized and an ultra-low power consumption of 3.4 × 10^-18 J was achieved.

Inorganic Perovskite Quantum Dot-Based Strain Sensors for Data Storage and In-Sensor Computing

Although remarkable improvement has been achieved in stretchable strain sensors, challenges still exist in aspects including intelligent sensing, simultaneous data processing, and scalable fabrication techniques. In this work, a strain-sensitive device is presented by fabricating a CsPbBr₃ quantum dots (QDs) floating-gate field-effect transistor (FET) sensing array on thin polyimide (PI) films. The FET exhibits an excellent on/off ratio (>10³) and a large memory window (>2 V). With the introduction of CsPbBr₃ QDs as the trapping layer, an additional UV response is obtained because of the photogenerated charge carriers that significantly enhance the source-drain current (I_DS) of the device. At each electrical state, the I_DS varies with the strains and the sensing range is from compressive +12.5% to tensile -10.8%. Excellent data retainability and mechanical durability demonstrate the high quality and reliability of the fabricated sensors. Furthermore, synapse functions including long-term potentiation (LTP), long-term depression (LTD), etc., are emulated at the device level. Linearity factor changes of LTP/LTD in different sensing scenarios demonstrate the reliability of the device and further confirm the different sensing mechanisms with/without UV illumination. Our results exhibit the potential of transistor-based devices for multifunctional intelligent sensing.

V2C Nanosheets as Dual-Functional Antibacterial Agents

Antibiotic-resistant bacterial strains have been continuously increasing and becoming a supreme threat to public health globally. The nanoparticle-based photothermal treatment has emerged as a powerful tool to combat toxic bacteria. Photothermal agents (PTAs) with cost-effective and high photothermal conversion efficiency are highly desirable. Herein, we unite the green process for delamination of V2AlC to produce a high yield mass of two-dimensional (2D) V2C nanosheets (NSs) by using algae extracts and demonstrate their high antibacterial efficiency. The resultant V2C NSs present decent structural reliability and intrinsic antibacterial ability. Powerful near-infrared (NIR) absorption and extraordinary photothermal conversion proficiency make it a good PTA for the photothermal treatment of bacteria. The antibacterial efficiency evaluation indicated that V2C NSs could effectively kill both Gram-positive S. aureus and Gram-negative E. coli. About 99.5% of both types of bacteria could be killed with low-dose of V2C NSs suspension (40 μg/mL) with 5 min NIR irradiation due to the intrinsic antibacterial ability and photothermal effect of V2C NSs, which is much higher than previous reports on Ta4C3, Ti3C2, MoSe2, and Nb2C. This work expands the application of MXene V2C NSs for rapid bacteria-killing and would gain promising attention for applications in the sterilization industry.

Highly sensitive 2D organic field-effect transistors for the detection of lithium-ion battery electrolyte leakage

Lithium ion batteries (LIBs) have become indispensable in daily life. Here, we fabricated a broad range of polymer semiconductor films as thin as 2 nm via a standard spin-coating method, and utilized the resulting organic transistors for the detection of LIB electrolytes, which can give an early warning signal before LIB failure.

Retina-Inspired Organic Heterojunction-Based Optoelectronic Synapses for Artificial Visual Systems

For the realization of retina-inspired neuromorphic visual systems which simulate basic functions of human visual systems, optoelectronic synapses capable of combining perceiving, processing, and memorizing in a single device have attracted immense interests. Here, optoelectronic synaptic transistors based on tris(2-phenylpyridine) iridium (Ir(ppy)₃) and poly(3,3-didodecylquarterthiophene) (PQT-12) heterojunction structure are presented. The organic heterojunction serves as a basis for distinctive synaptic characteristics under different wavelengths of light. Furthermore, synaptic transistor arrays are fabricated to demonstrate their optical perception efficiency and color recognition capability under multiple illuminating conditions. The wavelength-tunability of synaptic behaviors further enables the mimicry of mood-modulated visual learning and memorizing processes of humans. More significantly, the computational dynamics of neurons of synaptic outputs including associated learning and optical logic functions can be successfully demonstrated on the presented devices. This work may locate the stage for future studies on optoelectronic synaptic devices toward the implementation of artificial visual systems.

A Monochloro Copper Phthalocyanine Memristor with High-Temperature Resilience for Electronic Synapse Applications

Memristors are considered to be one of the most promising device concepts for neuromorphic computing, in particular thanks to their highly tunable resistive states. To realize neuromorphic computing architectures, the assembly of large memristive crossbar arrays is necessary, but is often accompanied by severe heat dispassion. Organic materials can be tailored with on-demand electronic properties in the context of neuromorphic applications. However, such materials are more susceptible to heat, and detrimental effects such as thermally induced degradation directly lead to failure of device operation. Here, an organic memristive synapse formed of monochloro copper phthalocyanine, which remains operational and capable of memristive switching at temperatures as high as 300 °C in ambient air without any encapsulation, is demonstrated. The change in the electrical conductance is found to be a result of ion movement, closely resembling what takes place in biological neurons. Furthermore, the high viability of this approach is showcased by demonstrating flexible memristors with stable switching behaviors after repeated mechanical bending as well as organic synapses capable of emulating a trainable and reconfigurable memristor array for image information processing. The results set a precedent for thermally resilient organic synapses to impact organic neuromorphic devices in progressing their practicality.

Self-Assembled Peptide Nanofibers with Voltage-Regulated Inverse Photoconductance

Inverse photoconductance is an uncommon phenomenon observed in selective low-dimensional materials, in which the electrical conductivity of the materials decreases under light illumination. The unique material property holds great promise for biomedical applications in photodetectors, photoelectric logic gates, and low-power nonvolatile memory, which remains a daunting challenge. Especially, tunable photoconductivity for biocompatible materials is highly desired for interfacing with biological systems but is less explored in organic materials. Here, we report nanofibers self-assembled with cyclo-tyrosine-tyrosine (cyclo-YY) having voltage-regulated inverse photoconductance and photoconductance. The peptide nanofibers can be switched back and forth by a bias voltage for imitating biological sensing in artificial vision and memory devices. A peptide optoelectronic resistive random access memory (PORRAM) device has also been fabricated using the nanofibers that can be electrically switched between long-term and short-term memory. The underlying mechanism of the reversible photoconductance is discussed in this paper. Due to the inherent biocompatibility of peptide materials, the reversible photoconductive nanofibers may have broad applications in sensing and storage for biotic and abiotic interfaces.

Highly Sensitive Artificial Visual Array Using Transistors Based on Porphyrins and Semiconductors

Artificial visual systems with image sensing and storage functions have considerable potential in the field of artificial intelligence. Light-stimulated synaptic devices can be applied for neuromorphic computing to build artificial visual systems. Here, optoelectronic synaptic transistors based on 5,15-(2-hydroxyphenyl)-10,20-(4-nitrophenyl)porphyrin (TPP) and dinaphtho[2,3-b:2',3'-f ]thieno[3,2-b]thiophene (DNTT) are demonstrated. By utilizing stable TPP with high light absorption, the number of photogenerated carriers in the transport layer can be increased significantly. The devices exhibit high photosensitivity and tunable synaptic plasticity. The synaptic weight can be effectively modulated by the intensity, width, and wavelength of the light signals. Due to the high light absorption of TPP, an ultrasensitive artificial visual array based on these devices is developed, which can detect weak light signals as low as 1 µW cm^-2 . Low-voltage operation is further demonstrated. Even with applied voltages as low as -70 µV, the devices can still show obvious responses, leading to an ultralow energy consumption of 1.4 fJ. The devices successfully demonstrate image sensing and storage functions, which can accurately identify visual information. In addition, the devices can preprocess information and achieve noise reduction. The excellent synaptic behavior of the TPP-based electronics suggests their good potential in the development of new intelligent visual systems.