Wood-Derived Nanopaper Dielectrics for Organic Synaptic Transistors

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.

Recent advancements in implantable neural links based on organic synaptic transistors

The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.

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.

Chlorophyl-Passivated Ytterbium-Doped Perovskite Quantum-Cutting Film for High-Performance Solar Energy Conversion and Near-Infrared Light-Emitting Diode Applications

The quantum cutting ytterbium (Yb3+)-doped CsPbX3 (X = Cl, Cl, or Br) nanocrystals, exhibiting photoluminescence quantum yields (PLQYs) exceeding 100%, hold significant promise for applications in solar energy conversion technologies and near-infrared (NIR) light-emitting diodes (LEDs). This work investigates the usage of chlorophyll (CHL), a naturally existing organic pigment, as an efficient molecular passivator to improve the performance of quantum cutting films. With the assistance of CHL, the resultant perovskite film displays an increased PLQY of 176%. The commercial silicon solar cells (SSCs) with CHL-treated perovskite films demonstrate a remarkable photon-to-current conversion efficiency improvement of 1.83% for a 330.15 cm2 area SSC device. Additionally, a CHL-modified Yb3+:CsPbCl3 film was used to create 988 nm NIR LEDs with an external quantum efficiency of 3.2%. This work provides a new, eco-friendly approach for producing high-quality, large-area Yb3+-doped perovskite film for deployment in photoelectric and night vision applications.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Transparent Structures for ZnO Thin Film Paper Transistors Fabricated by Pulsed Electron Beam Deposition

Thin film transistors on paper are increasingly in demand for emerging applications, such as flexible displays and sensors for wearable and disposable devices, making paper a promising substrate for green electronics and the circular economy. ZnO self-assembled thin film transistors on a paper substrate, also using paper as a gate dielectric, were fabricated by pulsed electron beam deposition (PED) at room temperature. These self-assembled ZnO thin film transistor source-channel-drain structures were obtained in a single deposition process using 200 and 300 µm metal wires as obstacles in the path of the ablation plasma. These transistors exhibited a memory effect, with two distinct states, "on" and "off", and with a field-effect mobility of about 25 cm2/Vs in both states. For the "on" state, a threshold voltage (Vth on = -1.75 V) and subthreshold swing (S = 1.1 V/decade) were determined, while, in the "off" state, Vth off = +1.8 V and S = 1.34 V/decade were obtained. A 1.6 μA maximum drain current was obtained in the "off" state, and 11.5 μA was obtained in the "on" state of the transistor. Due to ZnO's non-toxicity, such self-assembled transistors are promising as components for flexible, disposable smart labels and other various green paper-based electronics.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

High-performance asymmetric electrode structured light-stimulated synaptic transistor for artificial neural networks

Photonics neuromorphic computing shows great prospects due to the advantages of low latency, low power consumption and high bandwidth. Transistors with asymmetric electrode structures are receiving increasing attention due to their low power consumption, high optical response, and simple preparation technology. However, intelligent optical synapses constructed by asymmetric electrodes are still lacking systematic research and mechanism analysis. Herein, we present an asymmetric electrode structure of the light-stimulated synaptic transistor (As-LSST) with a bulk heterojunction as the semiconductor layer. The As-LSST exhibits superior electrical properties, photosensitivity and multiple biological synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, and long-term memory. Benefitting from the asymmetric electrode configuration, the devices can operate under a very low drain voltage of 1 × 10-7 V, and achieve an ultra-low energy consumption of 2.14 × 10-18 J per light stimulus event. Subsequently, As-LSST implemented the optical logic function and associative learning. Utilizing As-LSST, an artificial neural network (ANN) with ultra-high recognition rate (over 97.5%) of handwritten numbers was constructed. This work presents an easily-accessible concept for future neuromorphic computing and intelligent electronic devices.

Enhanced Synaptic Behaviors in Chitosan Electrolyte-Based Electric-Double-Layer Transistors with Poly-Si Nanowire Channel Structures

In this study, we enhance the synaptic behavior of artificial synaptic transistors by utilizing nanowire (NW)-type polysilicon channel structures. The high surface-to-volume ratio of the NW channels enables efficient modulation of the channel conductance, which is interpreted as the synaptic weight. As a result, NW-type synaptic transistors exhibit a larger hysteresis window compared to film-type synaptic transistors, even within the same gate voltage sweeping range. Moreover, NW-type synaptic transistors demonstrate superior short-term facilitation and long-term memory transition compared with film-type ones, as evidenced by the measured paired-pulse facilitation and excitatory post-synaptic current characteristics at varying frequencies and pulse numbers. Additionally, we observed gradual potentiation/depression characteristics, making these artificial synapses applicable to artificial neural networks. Furthermore, the NW-type synaptic transistors exhibit improved Modified National Institute of Standards and Technology pattern recognition rate of 91.2%. In conclusion, NW structure channels are expected to be a promising technology for next-generation artificial intelligence (AI) semiconductors, and the integration of NW structure channels has significant potential to advance AI semiconductor technology.

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.

Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence.

Stretchable Transistor-Structured Artificial Synapses for Neuromorphic Electronics

Stretchable synaptic transistors, a core technology in neuromorphic electronics, have functions and structures similar to biological synapses and can concurrently transmit signals and learn. Stretchable synaptic transistors are usually soft and stretchy and can accommodate various mechanical deformations, which presents significant prospects in soft machines, electronic skin, human-brain interfaces, and wearable electronics. Considerable efforts have been devoted to developing stretchable synaptic transistors to implement electronic device neuromorphic functions, and remarkable advances have been achieved. Here, this review introduces the basic concept of artificial synaptic transistors and summarizes the recent progress in device structures, functional-layer materials, and fabrication processes. Classical stretchable synaptic transistors, including electric double-layer synaptic transistors, electrochemical synaptic transistors, and optoelectronic synaptic transistors, as well as the applications of stretchable synaptic transistors in light-sensory systems, tactile-sensory systems, and multisensory artificial-nerves systems, are discussed. Finally, the current challenges and potential directions of stretchable synaptic transistors are analyzed. This review presents a detailed introduction to the recent progress in stretchable synaptic transistors from basic concept to applications, providing a reference for the development of stretchable synaptic transistors in the future.

Alkali-Doped Nanopaper Membranes Applied as a Gate Dielectric in FETs and Logic Gates with an Enhanced Dynamic Response

The market for flexible, hybrid, and printed electronic systems, which can appear in everything from sensors and wearables to displays and lighting, is still uncertain. What is clear is that these systems are appearing every day, enabling devices and systems that can, in the near future, be crumpled up and tucked in our pockets. Within this context, cellulose-based modified nanopapers were developed to serve both as a physical support and a gate dielectric layer in field-effect transistors (FETs) that are fully recyclable. It was found that the impregnation of those nanopapers with sodium (Na⁺) ions allows for low operating voltage FETs (<3 V), with mobility above 10 cm² V^-1 s^-1, current modulation surpassing 10⁵, and an improved dynamic response. Thus, it was possible to implement those transistors into simple circuits such as inverters, reaching a clear discrimination between logic states. Besides the overall improvement in electrical performance, these devices have shown to be an interesting alternative for reliable, sustainable, and flexible electronics, maintaining proper operation even under stress conditions.

Soft Fiber Electronics Based on Semiconducting Polymer

Fibers, originating from nature and mastered by human, have woven their way throughout the entire history of human civilization. Recent developments in semiconducting polymer materials have further endowed fibers and textiles with various electronic functions, which are attractive in applications such as information interfacing, personalized medicine, and clean energy. Owing to their ability to be easily integrated into daily life, soft fiber electronics based on semiconducting polymers have gained popularity recently for wearable and implantable applications. Herein, we present a review of the previous and current progress in semiconducting polymer-based fiber electronics, particularly focusing on smart-wearable and implantable areas. First, we provide a brief overview of semiconducting polymers from the viewpoint of materials based on the basic concepts and functionality requirements of different devices. Then we analyze the existing applications and associated devices such as information interfaces, healthcare and medicine, and energy conversion and storage. The working principle and performance of semiconducting polymer-based fiber devices are summarized. Furthermore, we focus on the fabrication techniques of fiber devices. Based on the continuous fabrication of one-dimensional fiber and yarn, we introduce two- and three-dimensional fabric fabricating methods. Finally, we review challenges and relevant perspectives and potential solutions to address the related problems.

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.

Natural Organic Materials Based Memristors and Transistors for Artificial Synaptic Devices in Sustainable Neuromorphic Computing Systems

Natural organic materials such as protein and carbohydrates are abundant in nature, renewable, and biodegradable, desirable for the construction of artificial synaptic devices for emerging neuromorphic computing systems with energy efficient operation and environmentally friendly disposal. These artificial synaptic devices are based on memristors or transistors with the memristive layer or gate dielectric formed by natural organic materials. The fundamental requirement for these synaptic devices is the ability to mimic the memory and learning behaviors of biological synapses. This paper reviews the synaptic functions emulated by a variety of artificial synaptic devices based on natural organic materials and provides a useful guidance for testing and investigating more of such devices.

Biocompatible Potato-Starch Electrolyte-Based Coplanar Gate-Type Artificial Synaptic Transistors on Paper Substrates

In this study, we propose the use of artificial synaptic transistors with coplanar-gate structures fabricated on paper substrates comprising biocompatible and low-cost potato-starch electrolyte and indium-gallium-zinc oxide (IGZO) channels. The electrical double layer (EDL) gating effect of potato-starch electrolytes enabled the emulation of biological synaptic plasticity. Frequency dependence measurements of capacitance using a metal-insulator-metal capacitor configuration showed a 1.27 μF/cm² at a frequency of 10 Hz. Therefore, strong capacitive coupling was confirmed within the potato-starch electrolyte/IGZO channel interface owing to EDL formation because of internal proton migration. An electrical characteristics evaluation of the potato-starch EDL transistors through transfer and output curve resulted in counterclockwise hysteresis caused by proton migration in the electrolyte; the hysteresis window linearly increased with maximum gate voltage. A synaptic functionality evaluation with single-spike excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), and multi-spike EPSC resulted in mimicking short-term synaptic plasticity and signal transmission in the biological neural network. Further, channel conductance modulation by repetitive presynaptic stimuli, comprising potentiation and depression pulses, enabled stable modulation of synaptic weights, thereby validating the long-term plasticity. Finally, recognition simulations on the Modified National Institute of Standards and Technology (MNIST) handwritten digit database yielded a 92% recognition rate, thereby demonstrating the applicability of the proposed synaptic device to the neuromorphic system.

Biocompatible Casein Electrolyte-Based Electric-Double-Layer for Artificial Synaptic Transistors

In this study, we proposed a synaptic transistor using an emerging biocompatible organic material, namely, the casein electrolyte as an electric-double-layer (EDL) in the transistor. The frequency-dependent capacitance of the indium-tin-oxide (ITO)/casein electrolyte-based EDL/ITO capacitor was assessed. As a result, the casein electrolyte was identified to exhibit a large capacitance of ~1.74 μF/cm2 at 10 Hz and operate as an EDL owing to the internal proton charge. Subsequently, the implementation of synaptic functions was verified by fabricating the synaptic transistors using biocompatible casein electrolyte-based EDL. The excitatory post-synaptic current, paired-pulse facilitation, and signal-filtering functions of the transistors demonstrated significant synaptic behavior. Additionally, the spike-timing-dependent plasticity was emulated by applying the pre- and post-synaptic spikes to the gate and drain, respectively. Furthermore, the potentiation and depression characteristics modulating the synaptic weight operated stably in repeated cycle tests. Finally, the learning simulation was conducted using the Modified National Institute of Standards and Technology datasets to verify the neuromorphic computing capability; the results indicate a high recognition rate of 90%. Therefore, our results indicate that the casein electrolyte is a promising new EDL material that implements artificial synapses for building environmental and biologically friendly neuromorphic systems.

The dual-detection mode and improved photoresponse of IGZO-based photodetectors by interfacing with water-soluble biomaterials

The use of conventional fabrication methods rapidly developed the performance and notable enhancements of optoelectronic devices. However, it proved challenging to develop and demonstrate stable optoelectronic devices with biodegradability and biocompatibility properties towards sustainable development and extensive applications. This study incorporates a water-soluble Cr-phycoerythrin (Cr-PE) biomaterial to observe its optical and electronic properties effects on the pristine indium gallium zinc oxide (IGZO)-based photodetector. The fabricated photodetector demonstrates an extended absorption detection region, enhanced optoelectronic performance, and switchable function properties. The resulting photocurrent and responsivity of the IGZO/Cr-PE structure have increased by 5.7 and 7.1 times as compared to the pristine IGZO photodetector. It was also observed that the photodetector could operate in UV and UV-visible with enhanced optical properties by effectively adding the water-soluble Cr-PE. Also, the sensing region of IGZO photodetector becomes changeable. It exhibits switchable dual detection by alternatively dripping and removing the Cr-PE on the IGZO layer. Different measurement parameters such as detectivity, repeatability, and sensitivity are highlighted to effectively prove the advantage of including Cr-PE on the photodetector structure. This study contributes to understanding the potential functions in improving optoelectronic devices through an environmental-friendly method.

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.

High ionic conductivity Li0.33La0.557TiO3nanofiber/polymer composite solid electrolyte for flexible transparent InZnO synaptic transistors

With the rapid development of wearable artificial intelligence devices, there is an increasing demand for flexible oxide neuromorphic transistors with the solid electrolytes. To achieve high-performance flexible synaptic transistors, the solid electrolytes should exhibit good mechanical bending characteristics and high ion conductivity. However, the polymer-based electrolytes with good mechanical bending characteristics show poor ion conductivity (10^-6-10^-7S cm^-1), which limits the performance of flexible synaptic transistors. Thus, it is urgent to improve the ion conductivity of the polymer-based electrolytes. In the work, a new strategy of electrospun Li_0.33La_0.557TiO₃nanofibers-enhanced ion transport pathway is proposed to simultaneously improve the mechanical bending and ion conductivity of polyethylene oxide/polyvinylpyrrolidone-based solid electrolytes. The flexible InZnO synaptic transistors with Li_0.33La_0.557TiO₃nanofibers-based solid electrolytes successfully simulated excitatory post-synaptic current, paired-pulse-facilitation, dynamic time filter, nonlinear summation, two-terminal input dynamic integration and logic function. This work is a useful attempt to develop high-performance synaptic transistors.

Neuromorphic Devices for Bionic Sensing and Perception

Neuromorphic devices that can emulate the bionic sensory and perceptual functions of neural systems have great applications in personal healthcare monitoring, neuro-prosthetics, and human-machine interfaces. In order to realize bionic sensing and perception, it's crucial to prepare neuromorphic devices with the function of perceiving environment in real-time. Up to now, lots of efforts have been made in the incorporation of the bio-inspired sensing and neuromorphic engineering in the booming artificial intelligence industry. In this review, we first introduce neuromorphic devices based on diverse materials and mechanisms. Then we summarize the progress made in the emulation of biological sensing and perception systems. Finally, the challenges and opportunities in these fields are also discussed.

Degradable Photonic Synaptic Transistors Based on Natural Biomaterials and Carbon Nanotubes

Artificial synaptic devices have potential for overcoming the bottleneck of von Neumann architecture and building artificial brain-like computers. Up to now, developing synaptic devices by utilizing biocompatible and biodegradable materials in electronic devices has been an interesting research direction due to the requirements of sustainable development. Here, a degradable photonic synaptic device is reported by combining biomaterials chlorophyll-a and single-walled carbon nanotubes (SWCNTs). Several basic synaptic functions, including excitatory postsynaptic current (EPSC), paired pulse facilitation (PPF), transition from short-term memory (STM) to long-term memory (LTM), and learning and forgetting behaviors, are successfully emulated through the chlorophyll-a/SWCNTs synaptic device. Furthermore, decent synaptic behaviors can still be achieved at a low drain voltage of -0.0001 V, which results in quite low energy consumption of 17.5 fJ per pulse. Finally, the degradability of this chlorophyll-a/SWCNTs transistor array is demonstrated, indicating that the device can be environmentally friendly. This work provides a new guide to the development of next-generation green and degradable neuromorphic computing electronics.

Flexible Capacitive Humidity Sensors Based on Ionic Conductive Wood-Derived Cellulose Nanopapers

With the growing requirements for the renewability and sustainability of electronic products, environmentally friendly cellulose-based materials have attracted immense research interests and gained increasing prominence for electronic devices. Humidity sensors play an essential role in industries, agriculture, climatology, medical services, and daily life. Here, for the first time, we fabricate capacitive humidity sensors based on ionic conductive wood-derived cellulose nanopapers (WCNs). The WCN-based humidity sensors exhibited ultrahigh sensitivity, fast response, small hysteresis, and more importantly, a wide working range of relative humidity (RH). The sensors showed >10⁴ times increase in the sensing signal over the 7-94% RH range at 20 Hz, while many reported humidity sensors with high sensitivity often have the working range limited to high RH levels. Our sensors can realize the distinction of nuances in humidity and exhibit outstanding noncontact skin humidity sensing properties. Flexible WCN-based humidity sensors were also fabricated, and they displayed excellent sensing properties with long-time stability, endowing them with multifunctional applications. The contrast humidity sensing experiment compared to the existing commercial humidity sensor further demonstrated the higher and faster response of our WCN-based sensors. Thus, this work provides effective guidance for the design of high-performance humidity sensors using nanopapers and opens a new dimension for a variety of future applications.

Biodegradable Materials and Green Processing for Green Electronics

There is little question that the "electronic revolution" of the 20th century has impacted almost every aspect of human life. However, the emergence of solid-state electronics as a ubiquitous feature of an advanced modern society is posing new challenges such as the management of electronic waste (e-waste) that will remain through the 21st century. In addition to developing strategies to manage such e-waste, further challenges can be identified concerning the conservation and recycling of scarce elements, reducing the use of toxic materials and solvents in electronics processing, and lowering energy usage during fabrication methods. In response to these issues, the construction of electronic devices from renewable or biodegradable materials that decompose to harmless by-products is becoming a topic of great interest. Such "green" electronic devices need to be fabricated on industrial scale through low-energy and low-cost methods that involve low/non-toxic functional materials or solvents. This review highlights recent advances in the development of biodegradable materials and processing strategies for electronics with an emphasis on areas where green electronic devices show the greatest promise, including solar cells, organic field-effect transistors, light-emitting diodes, and other electronic devices.

Bioinspired Multifunctional Organic Transistors Based on Natural Chlorophyll/Organic Semiconductors

Inspired by the photosynthesis process of natural plants, multifunctional transistors based on natural biomaterial chlorophyll and organic semiconductors (OSCs) are reported. Functions as photodetectors (PDs) and light-stimulated synaptic transistors (LSSTs) can be switched by gate voltage. As PDs, the devices exhibit ultrahigh photoresponsivity up to 2 × 10⁶ A W^-1 , detectivity of 6 × 10¹⁵ Jones, and I_photo /I_dark ratio of 2.7 × 10⁶ , which make them among the best reported organic PDs. As LSSTs, important synaptic functions similar to biological synapses are demonstrated, together with a dynamic learning and forgetting process and image-processing function. Significantly, benefiting from the ultrahigh photosensitivity of chlorophyll, the lowest operating voltage and energy consumption of the LSSTs can be 10^-5 V and 0.25 fJ, respectively. The devices also exhibit high flexibility and long-term air stability. This work provides a new guide for developing organic electronics based on natural biomaterials.

Artificial Tactile Perceptual Neuron with Nociceptive and Pressure Decoding Abilities

The neural system is a multifunctional perceptual learning system. Our brain can perceive different kinds of information to form senses, including touch, sight, hearing, and so on. Mimicking such perceptual learning systems is critical for neuromorphic platform applications. Here, an artificial tactile perceptual neuron is realized by utilizing electronic skins (E-skin) with oxide neuromorphic transistors, and this artificial tactile perceptual neuron successfully simulates biological tactile afferent nerves. First, the E-skin device is constructed using microstructured polydimethylsiloxane membranes coated with Ag/indium tin oxide (ITO) layers, exhibiting good sensitivities of ∼2.1 kPa^-1 and fast response time of tens of milliseconds. Then, the chitosan-based electrolyte-gated ITO neuromorphic transistor is fabricated and exhibits high performance and synaptic responses. Finally, the integrated artificial tactile perceptual neuron demonstrates pressure excitatory postsynaptic current and paired-pulse facilitation. The artificial tactile perceptual neuron is featured with low energy consumption as low as ∼0.7 nJ. Moreover, it can mimic acute and chronic pain and nociceptive characteristics of allodynia and hyperalgesia in biological nociceptors. Interestingly, the artificial tactile perceptual neuron can employ "Morse code" pressure-interpreting scheme. This simple and low-cost approach has excellent potential for applications including but not limited to intelligent humanoid robots and replacement neuroprosthetics.

The Design of 3D-Interface Architecture in an Ultralow-Power, Electrospun Single-Fiber Synaptic Transistor for Neuromorphic Computing

Synaptic electronics is a new technology for developing functional electronic devices that can mimic the structure and functions of biological counterparts. It has broad application prospects in wearable computing chips, human-machine interfaces, and neuron prostheses. These types of applications require synaptic devices with ultralow energy consumption as the effective energy supply for wearable electronics, which is still very difficult. Here, artificial synapse emulation is demonstrated by solid-ion gated organic field-effect transistors (OFETs) with a 3D-interface conducting channel for ultralow-power synaptic simulation. The basic features of the artificial synapse, excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and high-pass filtering, are successfully realized. Furthermore, the single-fiber based artificial synapse can be operated by an ultralow presynaptic spike down to -0.5 mV with an ultralow reading voltage at -0.1 mV due to the large contact surface between the ionic electrolyte and fiber-like semiconducting channel. Therefore, the ultralow energy consumption at one spike of the artificial synapse can be realized as low as ≈3.9 fJ, which provides great potential in a low-power integrated synaptic circuit.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

Light response behaviors of amorphous In–Ga–Zn–O thin-film transistors via in situ interfacial hydrogen doping modulation

Thin-film transistors (TFTs) based on amorphous In–Ga–Zn–O (a-IGZO) channels present high mobility, large-area uniformity, mechanical flexibility and photosensitivity, and thus have extensive applicability in photodetectors, wearable devices, etc. However, pure a-IGZO based photosensors only exhibit a UV light response with limited sensitivity performance. By utilizing in situ interfacial hydrogen doping, it is demonstrated that the a-IGZO TFTs with the Al₂O₃ dielectric deposited by plasma-enhanced atomic layer deposition at room temperature (RT) have excellent photosensing performance, such as a photoresponsivity of over 6 × 10⁵ A W⁻¹ and a light to dark current ratio up to 10⁷. This is attributed to spontaneous interfacial hydrogen doping into the a-IGZO channel during sputtering deposition of a-IGZO on hydrogen-rich Al₂O₃ films, thus generating subgap states in the band gap of IGZO. Further, color pattern imaging was achieved by employing an array of the color distinguishable devices, and flexibility was demonstrated by fabricating the TFTs onto polymer substrates. Moreover, it is also found that both the RT and 150 °C Al₂O₃ a-IGZO TFTs exhibit typical light-stimulated synaptic behaviors, including excitatory post-synaptic current and pair-pules facilitation, etc., and the memory time of the synaptic devices can be easily modulated by the degree of the interfacial hydrogen doping.

Thin-film transistors (TFTs) based on amorphous In–Ga–Zn–O (a-IGZO) channels present high mobility, large-area uniformity, mechanical flexibility and photosensitivity, and thus have extensive applicability in photodetectors, wearable devices, etc.