Artificial synapses based on nanomaterials

Mixed-Dimensional Nanoparticle-Nanowire Channels for Flexible Optoelectronic Artificial Synapse with Enhanced Photoelectric Response and Asymmetric Bidirectional Plasticity

A mixed-dimensional dual-channel synaptic transistor composed of inorganic nanoparticles and organic nanowires was fabricated to expand the photoelectric gain range. The device can actualize the sensitization features of the nociceptor and shows improved responsiveness to visible light. Under electrical pulses with different polarities, the apparatus exhibits reconfigurable asymmetric bidirectional plasticity. Moreover, the devices demonstrate good operational tolerance and mechanical stability, retaining more than 60% of their maximum responsiveness after 100 consecutive/bidirectional and 1000 flex/flat operations. The improved photoelectric response of the device endows a high image recognition accuracy of greater than 80%. Asymmetric bidirectional plasticity is used as punishment/reward in a psychological experiment to emulate the improvement of learning motivation and enables real-time forward and backward deflection (+7 and -25°) of artificial muscle. The mixed-dimensional optoelectronic artificial synapses with switchable behavior and electron/hole transport type have important prospects for neuromorphic processing and artificial somatosensory nerves.

Biomedical application of 2D nanomaterials in neuroscience

Two-dimensional (2D) nanomaterials, such as graphene, black phosphorus and transition metal dichalcogenides, have attracted increasing attention in biology and biomedicine. Their high mechanical stiffness, excellent electrical conductivity, optical transparency, and biocompatibility have led to rapid advances. Neuroscience is a complex field with many challenges, such as nervous system is difficult to repair and regenerate, as well as the early diagnosis and treatment of neurological diseases are also challenged. This review mainly focuses on the application of 2D nanomaterials in neuroscience. Firstly, we introduced various types of 2D nanomaterials. Secondly, due to the repairment and regeneration of nerve is an important problem in the field of neuroscience, we summarized the studies of 2D nanomaterials applied in neural repairment and regeneration based on their unique physicochemical properties and excellent biocompatibility. We also discussed the potential of 2D nanomaterial-based synaptic devices to mimic connections among neurons in the human brain due to their low-power switching capabilities and high mobility of charge carriers. In addition, we also reviewed the potential clinical application of various 2D nanomaterials in diagnosing and treating neurodegenerative diseases, neurological system disorders, as well as glioma. Finally, we discussed the challenge and future directions of 2D nanomaterials in neuroscience.

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.

Hybrid Fiber Materials according to the Manufacturing Technology Methods and IOT Materials: A Systematic Review

With the development of convergence technology, the Internet of Things (IoT), and artificial intelligence (AI), there has been increasing interest in the materials industry. In recent years, numerous studies have attempted to identify and explore multi-functional cutting-edge hybrid materials. In this paper, the international literature on the materials used in hybrid fibers and manufacturing technologies were investigated and their future utilization in the industry is predicted. Furthermore, a systematic review is also conducted. This includes sputtering, electrospun nanofibers, 3D (three-dimensional) printing, shape memory, and conductive materials. Sputtering technology is an eco-friendly, intelligent material that does not use water and can be applied as an advantageous military stealth material and electromagnetic blocking material, etc. Electrospinning can be applied to breathable fabrics, toxic chemical resistance, fibrous drug delivery systems, and nanoliposomes, etc. 3D printing can be used in various fields, such as core-sheath fibers and artificial organs, etc. Conductive materials include metal nanowires, polypyrrole, polyaniline, and CNT (Carbon Nano Tube), and can be used in actuators and light-emitting devices. When shape-memory materials deform into a temporary shape, they can return to their original shape in response to external stimuli. This study attempted to examine in-depth hybrid fiber materials and manufacturing technologies.

Ferroelectric van der Waals heterostructures of CuInP2S6for non-volatile memory device applications

Two-dimensional (2D) ferroelectric materials are providing promising platforms for creating future nano- and opto-electronics. Here we propose new hybrid van der Waals heterostructures, in which the 2D ferroelectric material CuInP₂S₆(CIPS) is layered on a 2D semiconductor for near-infrared (NIR) memory device applications. Using density functional theory, we show that the band gap of the hybrid bilayers formed with CIPS can be tuned and that the optical and electronic properties can be successfully modulated via ferroelectric switching. Of the 3712 heterostructures considered, we identified 19 structures that have a type II band alignment and commensurate lattice matches. Of this set, both the CuInP₂S₆/PbSe and CuInP₂S₆/Ge₂H₂heterostructures possess absorption peaks in the NIR region that change position and intensity with switching polarisation, making them suitable for NIR memory devices. The CuInP₂S₆/ISSb, CuInP₂S₆/ISbSe, CuInP₂S₆/ClSbSe and CuInP₂S₆/ZnI₂heterostructures had band gaps which can be switched from direct to indirect with changing the polarisation of CIPS making them suitable for optoelectronics and sensors. The heterostructures formed with CIPS are exciting candidates for stable ferroelectric devices, opening a pathway for tuning the band alignment of van der Waal heterostructures and the creation of modern memory applications that use less energy.

Evolution of Bio-Inspired Artificial Synapses: Materials, Structures, and Mechanisms

Artificial synapses (ASs) are electronic devices emulating important functions of biological synapses, which are essential building blocks of artificial neuromorphic networks for brain-inspired computing. A human brain consists of several quadrillion synapses for information storage and processing, and massively parallel computation. Neuromorphic systems require ASs to mimic biological synaptic functions, such as paired-pulse facilitation, short-term potentiation, long-term potentiation, spatiotemporally-correlated signal processing, and spike-timing-dependent plasticity, etc. Feature size and energy consumption of ASs need to be minimized for high-density energy-efficient integration. This work reviews recent progress on ASs. First, synaptic plasticity and functional emulation are introduced, and then synaptic electronic devices for neuromorphic computing systems are discussed. Recent advances in flexible artificial synapses for artificial sensory nerves are also briefly introduced. Finally, challenges and opportunities in the field are discussed.

Heterosynaptic Plasticity Emulated by Liquid Crystal-Carbon Nanotube Composites with Modulatory Interneurons

The aim of the neuromorphic computing is to emulate energy-efficient and smart data-processing ability of the biological brain, which is achieved by massively interconnected neurons and synapses. The strength of a connection between two neurons is modified by homosynaptic and heterosynaptic plasticity. As current research in the neuromorphic device is mainly focused on emulating homosynaptic plasticity, complex biological functions are not easy to mimic because they require both homosynaptic and heterosynaptic plasticity. We demonstrate the use of a liquid crystal-carbon nanotube (LC-CNT) composite as a resistive switching material that can emulate both the homosynaptic and heterosynaptic functions of biological neurons. The LC-CNT composite undergoes resistance change by CNT alignment and aggregated wire formation subjected to an applied electric field. A two-terminal device that exploits this mechanism achieves analog switching and homosynaptic potentiation. In a multiterminal device structure, the modulatory interneuron could tune the synaptic properties to perform heterosynaptic functions such as heterosynaptic potentiation, heterosynaptic facilitation, and synaptic weight normalization to emulate complex biological functions of a brain. Artificial synapses that exploit this multifunctionality of the LC-CNT composite have uses in next-generation neuromorphic devices.

Voltage control of domain walls in magnetic nanowires for energy-efficient neuromorphic devices

An energy-efficient voltage-controlled domain wall (DW) device for implementing an artificial neuron and synapse is analyzed using micromagnetic modeling in the presence of room temperature thermal noise. By controlling the DW motion utilizing spin transfer or spin-orbit torques in association with voltage generated strain control of perpendicular magnetic anisotropy in the presence of Dzyaloshinskii-Moriya interaction, different positions of the DW are realized in the free layer of a magnetic tunnel junction to program different synaptic weights. The feasibility of scaling of such devices is assessed in the presence of thermal perturbations that compromise controllability. Additionally, an artificial neuron can be realized by combining this DW device with a CMOS buffer. This provides a possible pathway to realize energy-efficient voltage-controlled nanomagnetic deep neural networks that can learn in real time.

Artificial Synapse Emulated through Fully Aqueous Solution-Processed Low-Voltage In2O3 Thin-Film Transistor with Gd2O3 Solid Electrolyte

Brain-like neuromorphic computing system provides an alternative approach for the future computer for its characteristics of high-efficiency, power-efficient, self-learning, and parallel computing. Therefore, the imitation of synapse behavior based on microelectronics is particularly important. Recently, the synaptic transistors have received widespread attention. Among them, solid oxide-based synaptic transistors are more compatible with the large-scale fabrication than the liquid and organic-based transistors. So the development of oxide synaptic transistor is required. Here, a novel aqueous solution-processed Gd₂O₃ is suggested to be the solid electrolyte for synaptic transistors. The microstructure and the dielectric properties of Gd₂O₃ film are investigated, which show the potential for the simulation of synaptic transmission. Then, the fully aqueous solution-processed In₂O₃/Gd₂O₃ thin-film transistor (TFT) is fabricated. The device exhibits an acceptable electrical performance with a small threshold voltage of 1.24 V, and a small subthreshold swing of 0.12 V/decade. The artificial synapse behavior is stimulated and the short-term plasticity of In₂O₃/Gd₂O₃ TFT is studied. The dependence of its excitatory postsynaptic current on presynaptic pulse magnitude, width, and frequency is verified. Besides, the synapse behavior of devices under continuous illumination stresses is investigated. The lights with different photon energy have different effects on the synaptic transmission, which is related to the ionization of oxygen vacancies. Our results demonstrate that fully aqueous solution-processed In₂O₃ TFT with Gd₂O₃ solid electrolyte is a candidate for the synaptic transistor.

An artificial spiking afferent nerve based on Mott memristors for neurorobotics

Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbO_x Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future.

Though artificial sensory systems based on electronic devices have been realized, further transformation of data into spikes is required for neural network optimization. Here, based on NbO_x Mott memristors, the authors report artificial spiking afferent nerves for accessing spiking systems and demonstrate spiking mechanoreceptor systems.