Transient security transistors self-supported on biodegradable natural-polymer membranes for brain-inspired neuromorphic applications

Vertical 0.6 V sub-10 nm oxide-homojunction transistor gated by a silk fibroin/sodium alginate crosslinking hydrogel for pain-sensitization enhancement emulation

The sensory nervous system of humans mainly depends on continuous training and memory to improve the pain-perceptional abilities for the complex noxious information in the real world and make appropriate responses. Unfortunately, the solid-state device for emulating this pain recognition with ultralow voltage operation still remains to be a great challenge. Herein, a vertical transistor with an ultrashort channel of ∼9.6 nm and ultralow voltage of ∼0.6 V based on protonic silk fibroin/sodium alginate crosslinking hydrogel electrolyte is successfully demonstrated. Such a hydrogel electrolyte with high ionic conductivity allows the transistor to work in an ultralow voltage, while the vertical transistor structure makes it have an ultrashort channel. Pain perception, memory, and sensitization can be integrated into this vertical transistor. Furthermore, using the photogating effect of light stimulus, the device displays multi-state pain-sensitization enhancement abilities through Pavlovian training. Most importantly, the cortical reorganization that reveals a close relationship among the pain stimulus, memory, and sensitization is finally realized. Therefore, this device can provide a great opportunity for multi-dimensional pain assessment, which is of great significance for the new generation of bio-inspired intelligent electronics, such as bionic robots, and smart medical equipment.

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.

Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases

Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn't been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases.

Polarization-perceptual anisotropic two-dimensional ReS2 neuro-transistor with reconfigurable neuromorphic vision

Polarization is a common and unique phenomenon in nature, which reveals more camouflage features of objects. However, current polarization-perceptual devices based on conventional physical architectures face enormous challenges for high-performance computation due to the traditional von Neumann bottleneck. In this work, a novel polarization-perceptual neuro-transistor with reconfigurable anisotropic vision is proposed based on a two-dimensional ReS₂ phototransistor. The device exhibits excellent photodetection ability and superior polarization sensitivity due to its direct band gap semiconductor property and strong anisotropic crystal structure, respectively. The fascinating polarization-sensitive neuromorphic behavior, such as polarization memory consolidation and reconfigurable visual imaging, are successfully realized. In particular, the regulated polarization responsivity and dichroic ratio are successfully emulated through our artificial compound eyes. More importantly, two intriguing polarization-perceptual applications for polarized navigation with reconfigurable adaptive learning abilities and three-dimensional visual polarization imaging are also experimentally demonstrated. The proposed device may provide a promising opportunity for future polarization perception systems in intelligent humanoid robots and autonomous vehicles.

A biopolymer-gated ionotronic junctionless oxide transistor array for spatiotemporal pain-perception emulation in nociceptor network

Capable of reflecting the location and intensity of external harmful stimuli, a nociceptor network is of great importance for receiving pain-perception information. However, the hardware-based implementation of a nociceptor network through the use of a transistor array remains a great challenge in the area of brain-inspired neuromorphic applications. Herein, a simple ionotronic junctionless oxide transistor array with pain-perception abilities is successfully realized due to a coplanar-gate proton-coupling effect in sodium alginate biopolymer electrolyte. Several important pain-perception characteristics of nociceptors are emulated, such as a pain threshold, the memory of prior injury, and sensitization behavior due to pathway alterations. In particular, a good graded pain-perception network system has been successfully established through coplanar capacitance and resistance. More importantly, clear polarity reversal of Lorentz-type spatiotemporal pain-perception emulation can be finally realized in our projection-dependent nociceptor network. This work may provide new avenues for bionic medical machines and humanoid robots based on these intriguing pain-perception abilities.

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.

Hardware implementation of photoelectrically modulated dendritic arithmetic and spike-timing-dependent plasticity enabled by an ion-coupling gate-tunable vertical 0D-perovskite/2D-MoS2 hybrid-dimensional van der Waals heterostructure

Brain-inspired nanodevices have been demonstrated to possess outstanding characteristics for implementing neuromorphic computing. Among these devices, photoelectrically modulated neuromorphic transistors are regarded as the basic building blocks for applications in emerging brain-like devices. However, to date, efficient optoelectronic-hybrid neuromorphic devices are still lacking. Because conventional transistors based on mono-semiconductor materials cannot absorb adequate light to ensure efficient light-matter interactions, they pose significant challenges to the synchronous processing of photoelectric information. Here, a novel photoelectrically modulated neuromorphic device based on an ion-coupling gate-tunable vertical 0D-CsPbBr3-quantum-dots/2D-MoS2 hybrid-dimensional van der Waals heterojunction is demonstrated by using a polymer ion gel electrolyte as the gate dielectric. A super-efficient heterojunction interface for photo-carrier transport is developed by integrating CsPbBr3 quantum dots with 2D-layered MoS2 semiconductors. We experimentally demonstrate that the drain-source current can be modulated by applying spikes to the drain and gate terminals, and the conductance can also be tuned by external light stimulus. Most importantly, photoelectrically modulated spiking Boolean logics, dendritic integrations in both temporal and spatial modes, and Hebbian learning rules can be successfully mimicked in our proposed hybrid-dimensional device using this intriguing optical and electrical synergy approach. These results suggest that the proposed device has great potential in intelligent cognitive systems and neuromorphic computing applications.

Bienenstock-Cooper-Munro Learning Rule Realized in Polysaccharide-Gated Synaptic Transistors with Tunable Threshold

With reference to the organization of the human brain nervous system, a hardware-based approach that builds massively parallel neuromorphic circuits is of great significance to neuromorphic computing. The Bienenstock-Cooper-Munro (BCM) learning rule, which describes that the synaptic weight modulation exhibits frequency-dependent and tunable frequency threshold characteristics, is more compatible with the working principle of neuromorphic computing systems than spike-timing-dependent plasticity. Therefore, it is interesting to simulate the BCM learning rule on solid-state synaptic devices. Here, we have prepared λ-carrageenan (λ-car) electrolyte-gated oxide synaptic transistors, which exhibit good transistor performances, including a low subthreshold swing of 125 mV/dec, an on/off ratio larger than 10⁶, and a mobility of 9.5 cm² V^-1 s^-1. By modulating the initial channel current and spike frequency, the simulation of the BCM rule was successfully realized. The competitive relationship between the drift of protons under an electric field and the spontaneous diffusion of protons can explain this mechanism. The proposed λ-car-gated synaptic transistor has a great significance to neuromorphic computing.

Vertical 0D-Perovskite/2D-MoS2 van der Waals Heterojunction Phototransistor for Emulating Photoelectric-Synergistically Classical Pavlovian Conditioning and Neural Coding Dynamics

Optoelectronic-neuromorphic transistors are vital for next-generation nanoscale brain-like computational systems. However, the hardware implementation of optoelectronic-neuromorphic devices, which are based on conventional transistor architecture, faces serious challenges with respect to the synchronous processing of photoelectric information. This is because mono-semiconductor material cannot absorb adequate light to ensure efficient light-matter interactions. In this work, a novel neuromorphic-photoelectric device of vertical van der Waals heterojunction phototransistors based on a colloidal 0D-CsPbBr₃ -quantum-dots/2D-MoS₂ heterojunction channel is proposed using a polymer ion gel electrolyte as the gate dielectric. A highly efficient photocarrier transport interface is established by introducing colloidal perovskite quantum dots with excellent light absorption capabilities on the 2D-layered MoS₂ semiconductor with strong carrier transport abilities. The device exhibits not only high photoresponsivity but also fundamental synaptic characteristics, such as excitatory postsynaptic current, paired-pulse facilitation, dynamic temporal filter, and light-tunable synaptic plasticity. More importantly, efficiency-adjustable photoelectronic Pavlovian conditioning and photoelectronic hybrid neuronal coding behaviors can be successfully implemented using the optical and electrical synergy approach. The results suggest that the proposed device has potential for applications associated with next-generation brain-like photoelectronic human-computer interactions and cognitive systems.

Binary Electronic Synapses for Integrating Digital and Neuromorphic Computation in a Single Physical Platform

As a promising advanced computation technology, the integration of digital computation with neuromorphic computation into a single physical platform holds the advantage of a precise, deterministic, fast data process as well as the advantage of a flexible, paralleled, fault-tolerant data process. Even though two-terminal memristive devices have been respectively proved as leading electronic elements for digital computation and neuromorphic computation, it is difficult to steadily maintain both sudden-state-change and gradual-state-change in a single device due to the entirely different operating mechanisms. In this work, we developed a digital-analog compatible memristive device, namely, binary electronic synapse, through realizing controllable cation drift in a memristive layer. The devices feature nonvolatile binary memory as well as artificial neuromorphic plasticity with high operation endurance. With strong nonlinearity in switching dynamics, binary switching, neuromorphic plasticity, two-dimension information store, and trainable memory can be implemented by a single device.

Artificial Sensory Memory

Sensory memory, formed at the beginning while perceiving and interacting with the environment, is considered a primary source of intelligence. Transferring such biological concepts into electronic implementation aims at achieving perceptual intelligence, which would profoundly advance a broad spectrum of applications, such as prosthetics, robotics, and cyborg systems. Here, the recent developments in the design and fabrication of artificial sensory memory devices are summarized and their applications in recognition, manipulation, and learning are highlighted. The emergence of such devices benefits from recent progress in both bioinspired sensing and neuromorphic engineering technologies and derives from abundant inspiration and benchmarks from an improved understanding of biological sensory processing. Increasing attention to this area would offer unprecedented opportunities toward new hardware architecture of artificial intelligence, which could extend the capabilities of digital systems with emotional/psychological attributes. Pending challenges are also addressed to aspects such as integration level, energy efficiency, and functionality, which would undoubtedly shed light on the future development of translational implementations.

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.

A sensitivity-enhanced capacitance readout circuit with symmetric cross-coupling structure

This paper presents a proposed capacitance readout circuit that enables a quadrupled (x4) output strength. A symmetric cross-coupling structure is proposed to amplify the voltage difference between two adjacent channels; hence, the detected signal can be integrated twice every clock cycle. Compared with conventional schematics, the proposed readout circuit shows an increased output strength for integration times within dozens of μs. In addition, the measurements show that the integrator resistors should be less than 1 kΩ to suppress the resistance-capacitance delay effects. Although the proposed capacitance readout circuit is implemented using discrete transistors, it has a good signal integrity at an operating clock cycle of 100 µs. Therefore, the proposed readout circuit is a promising way to detect small capacitance variations with short integration times.

3D Stackable Synaptic Transistor for 3D Integrated Artificial Neural Networks

Although they have attracted enormous attention in recent years, software-based and two-dimensional hardware-based artificial neural networks (ANNs) may consume a great deal of power. Because there will be numerous data transmissions through a long interconnection for learning, power consumption in the interconnect will be an inevitable problem for low-power computing. Therefore, we suggest and report 3D stackable synaptic transistors for 3D ANNs, which would be the strongest candidate in future computing systems by minimizing power consumption in the interconnection. To overcome the problems of enormous power consumption, it might be necessary to introduce a 3D stackable ANN platform. With this structure, short vertical interconnection can be realized between the top and bottom devices, and the integration density can be significantly increased for integrating numerous neuromorphic devices. In this paper, we suggest and show the feasibility of monolithic 3D integration of synaptic devices using the channel layer transfer method through a wafer bonding technique. Using a low-temperature processible III-V and composite oxide (Al₂O₃/HfO₂/Al₂O₃)-based weight storage layer, we successfully demonstrated synaptic transistors showing good linearity (α_p/α_d = 1.8/0.5), a high transconductance ratio (6300), and very good stability. High learning accuracy of 97% was obtained in the training of 1 million MNIST images based on the device characteristics.

A Sub-10 nm Vertical Organic/Inorganic Hybrid Transistor for Pain-Perceptual and Sensitization-Regulated Nociceptor Emulation

Pain-perceptual nociceptors (PPN) are essential sensory neurons that recognize harmful stimuli and can empower the human body to react appropriately and perceive precisely unusual or dangerous conditions in the real world. Furthermore, the sensitization-regulated nociceptors (SRN) can greatly assist pain-sensitive human to reduce pain sensation by normalizing hyperexcitable central neural activity. Therefore, the implementation of PPNs and SRNs in hardware using emerging nanoscale devices can greatly improve the efficiency of bionic medical machines by giving them different sensitivities to external stimuli according to different purposes. However, current most-normal organic/oxide transistors face a great challenge due to channel scaling, especially in the sub-10 nm channel technology. Here, a sub-10 nm indium-tin-oxide transistor with an ultrashort vertical channel as low as ≈3 nm, using sodium alginate bio-polymer electrolyte as gate dielectric, is demonstrated. This device can emulate important characteristics of PPN such as pain threshold, memory of prior injury, and pain sensitization/desensitization. Furthermore, the most intriguing character of SRN can be achieved by tuning the channel thickness. The proposed device can open new avenues for the fascinating applications of next-generation neuromorphic brain-like systems, such as bio-inspired electronic skins and humanoid robots.

Solution-Processed, Electrolyte-Gated In2O3 Flexible Synaptic Transistors for Brain-Inspired Neuromorphic Applications

Emulating the essential synaptic behaviors using single synaptic transistor has attracted extensive attention for building the brain-inspired neuromorphic systems. However, few reports on synaptic transistors fabricated by solution processes have been reported. In this article, the indium oxide synaptic transistors based on polyimide substrates were fabricated by a nontoxic water-inducement method at a low temperature, and lithium perchlorate (LiClO₄) was dissolved in polyethylene oxide as the gate electrolyte. For water-inducement process, comparable electrical properties of the synaptic transistors can be achieved by prolonging the annealing time rather than high-temperature annealing with a relatively short time. The effect of the annealing time on the electrical performance of the electrolyte-gated transistors annealed at various temperatures was investigated. It is found that the electrolyte-gated-synaptic transistor on polyimide substrate annealed at 200 °C exhibits high electrical performance and good mechanical stability. Due to the ion migration relaxation dynamics in the polymer electrolyte, various important synaptic behaviors such as the excitatory postsynaptic current, paired-pulse facilitation, high-pass filtering characteristics, and long-term memory performance were successfully mimicked. The electrolyte-gated synaptic transistors based on solution-processed In₂O₃ exhibit great potential in neuromorphological applications.

Dendrite Integration Mimicked on Starch-Based Electrolyte-Gated Oxide Dendrite Transistors

Emulation of dendrite integration on brain-inspired hardware devices is of great significance for neuromorphic engineering. Here, solution-processed starch-based electrolyte films are fabricated, demonstrating strong proton gating activities. Starch gated oxide dendrite transistors with multigates are fabricated, exhibiting good electrical performances. Most importantly, dendrite modulation, spatiotemporal dendrite integration, and linear/superlinear dendrite algorithm are demonstrated on the proposed dendrite transistor. Furthermore, a low energy consumption of ∼1.2 pJ is obtained for triggering a synaptic response on the dendrite transistor. Accordingly, the signal-to-noise ratio is still as high as ∼2.9, indicating a high sensitivity of ∼4.6 dB. Such artificial dendrite transistors have potential applications in brain-inspired neuromorphic platforms.