Hybrid C8-BTBT/In GaAs nanowire heterojunction for artificial photosynaptic transistors

The emergence of light-tunable synaptic transistors provides opportunities to break through the von Neumann bottleneck and enable neuromorphic computing.Herein,a multifunctional synaptic transistor is constructed by using 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene(C8-BTBT)and indium gallium arsenide(InGaAs)nanowires(NWs)hybrid heterojunction thin film as the active layer.Under illumination,the Type-I C8-BTBT/InGaAs NWs heterojunction would make the dissociated photogenerated excitons more difficult to recombine.The persistent photoconductivity caused by charge trapping can then be used to mimic photosynaptic behaviors,including excitatory postsynaptic current,long/short-term memory and Pavlovian learning.Furthermore,a high classification accuracy of 89.72%can be achieved through the single-layer-perceptron hardware-based neural network built from C8-BTBT/InGaAs NWs synaptic transistors.Thus,this work could provide new insights into the fabrication of high-performance optoelectronic synaptic devices.

Study of short-term synaptic plasticity in Ion-Gel gated graphene electric-double-layer synaptic transistors

Multi-terminal electric-double-layer transistors have recently attracted extensive interest in terms of mimicking synaptic and neural functions.In this work,an Ion-Gel gated graphene synaptic transistor was proposed to mimic the essential synaptic behaviors by exploiting the bipolar property of graphene and the ionic conductivity of Ion-Gel.The Ion-Gel dielectrics were deposited onto the graphene film by the spin coating process.We consider the top gate and graphene channel as a presynaptic and postsynaptic terminal,respectively.Basic synaptic functions were successfully mimicked,including the excitatory postsynaptic current(EPSC),the effect of spike amplitude and duration on EPSC,and paired-pulse facilitation(PPF).This work may facilitate the application of graphene synaptic transistors in flexible electronics.

A synaptic transistor with NdNiO3

Recently, neuromorphic devices for artificial intelligence applications have attracted much attention. In this work, a three-terminal electrolyte-gated synaptic transistor based on NdNiO3 epitaxial films, a typical correlated electron material, is presented. The voltage-controlled metal-insulator transition was achieved by inserting and extracting H+ ions in the NdNiO3 channel through electrolyte gating. The non-volatile conductance change reached 104 under a 2 V gate voltage. By manipulating the amount of inserted protons, the three-terminal NdNiO3 artificial synapse imitated important synaptic functions, such as synaptic plasticity and spike-timing-dependent plasticity. These results show that the correlated material NdNiO3 has great potential for applications in neuromorphic devices.

Synaptic Transistor Implemented Using Quasi-2D Molybdenum Oxide

Recent years has seen increasing interest in building artificial synaptic devices to emulate the computation performed by biological synapses.Biological synapses are functional links between neurons,through which information is transmitted in the neuron network.The information can be stored and processed simultaneously in the same synapse through tuning synaptic weight,which is defined as the strength of the correlation between

Soft multifunctional neurological electronic skin through intrinsically stretchable synaptic transistor

Neurological electronic skin(E-skin)can process and transmit information in a distributed manner that achieves effective stimuli perception,holding great promise in neuroprosthetics and soft robotics.Neurological E-skin with multifunctional perception abilities can enable robots to precisely interact with the complex surrounding environment.However,current neurological E-skins that possess tactile,thermal,and visual perception abilities are usually prepared with rigid materials,bringing difficulties in realizing biologically synapse-like softness.Here,we report a soft multifunctional neurological E-skin(SMNE)comprised of a poly(3-hexylthiophene)(P3HT)nanofiber polymer semiconductor-based stretchable synaptic transistor and multiple soft artificial sensory receptors,which is capable of effectively perceiving force,thermal,and light stimuli.The stretchable synaptic transistor can convert electrical signals into transient channel currents analogous to the biological excitatory postsynaptic currents.And it also possesses both short-term and long-term synaptic plasticity that mimics the human memory system.By integrating a stretchable triboelectric nanogenerator,a soft thermoelectric device,and an elastic photodetector as artificial receptors,we further developed an SMNE that enables the robot to make precise actions in response to various surrounding stimuli.Compared with traditional neurological E-skin,our SMNE can maintain the softness and adaptability of biological synapses while perceiving multiple stimuli including force,temperature,and light.This SMNE could promote the advancement of E-skins for intelligent robot applications.

Pulse electrochemical synaptic transistor for supersensitive and ultrafast biosensors

High sensitivity and fast response are the figures of merit for benchmarking commercial sensors.Due to the advantages of intrinsic signal amplification,bionic ability,and mechanical flexibility,electrochemical transistors(ECTs)have recently gained increasing popularity in constructing various sensors.In the current work,we have proposed a pulse-driven synaptic ECT for supersensitive and ultrafast biosensors.By pulsing the presynaptic input(drain bias,VD)and setting the modulation potential(gate bias)near transconductance intersection(VG,i),the synaptic ECT-based pH sensor can achieve a record high sensitivity up to 124 mV pH^(-1)(almost twice the Nernstian limit,59.2 mV pH^(-1))and an ultrafast response time as low as 8.75 ms(7169 times faster than the potentiostatic sensors,62.73 s).The proposed synaptic sensing strategy can effectively eliminate the transconductance fluctuation issue during the calibration process of the pH sensor and significantly reduce power consumption.Besides,the most sensitive working point at VG,i has been elaborately figured out through a series of detailed mathematical derivations,which is of great significance to provide higher sensitivity with quasi-nonfluctuating amplification capability.The proposed electrochemical synaptic transistor paired with an optimized operating gate offers a new paradigm for standardizing and commercializing high-performance biosensors.

Recent advances in fabrication and functions of neuromorphic system based on organic field effect transistor

The development of various artificial electronics and machines would explosively increase the amount of information and data,which need to be processed via in-situ remediation.Bioinspired synapse devices can store and process signals in a parallel way,thus improving fault tolerance and decreasing the power consumption of artificial systems.The organic field effect transistor(OFET)is a promising component for bioinspired neuromorphic systems because it is suitable for large-scale integrated circuits and flexible devices.In this review,the organic semiconductor materials,structures and fabrication,and different artificial sensory perception systems functions based on neuromorphic OFET devices are summarized.Subsequently,a summary and challenges of neuromorphic OFET devices are provided.This review presents a detailed introduction to the recent progress of neuromorphic OFET devices from semiconductor materials to perception systems,which would serve as a reference for the development of neuromorphic systems in future bioinspired electronics.

One memristor–one electrolyte-gated transistor-based high energy-efficient dropout neuronal units

Artificial neural networks(ANN) have been extensively researched due to their significant energy-saving benefits.Hardware implementations of ANN with dropout function would be able to avoid the overfitting problem. This letter reports a dropout neuronal unit(1R1T-DNU) based on one memristor–one electrolyte-gated transistor with an ultralow energy consumption of 25 p J/spike. A dropout neural network is constructed based on such a device and has been verified by MNIST dataset, demonstrating high recognition accuracies(> 90%) within a large range of dropout probabilities up to40%. The running time can be reduced by increasing dropout probability without a significant loss in accuracy. Our results indicate the great potential of introducing such 1R1T-DNUs in full-hardware neural networks to enhance energy efficiency and to solve the overfitting problem.

Fully rubbery synaptic transistors made out of all-organic materials for elastic neurological electronic skin

Neurologic function implemented soft organic electronic skin holds promise for wide range of applications,such as skin prosthetics,neurorobot,bioelectronics,human-robotic interaction(HRI),etc.Here,we report the development of a fully rubbery synaptic transistor which consists of all-organic materials,which shows unique synaptic characteristics existing in biological synapses.These synaptic characteristics retained even under mechanical stretch by 30%.We further developed a neurological electronic skin in a fully rubbery format based on two mechanoreceptors(for synaptic potentiation or depression)of pressure-sensitive rubber and an all-organic synaptic transistor.By converting tactile signals into Morse Code,potentiation and depression of excitatory postsynaptic current(EPSC)signals allow the neurological electronic skin on a human forearm to communicate with a robotic hand.The collective studies on the materials,devices,and their characteristics revealed the fundamental aspects and applicability of the all-organic synaptic transistor and the neurological electronic skin.

CsPbBr_(3)quantum dots/PDVT-10 conjugated polymer hybrid film-based photonic synaptic transistors toward high-efficiency neuromorphic computing

Photonic synaptic transistors are promising neuromorphic computing systems that are expected to circumvent the intrinsic limitations of von Neumann-based computation.The design and construction of photonic synaptic transistors with a facile fabrication process and highefficiency information processing ability are highly desired,while it remains a tremendous challenge.Herein,a new approach based on spin coating of a blend of CsPbBr_(3) perovskite quantum dot(QD)and PDVT-10 conjugated polymer is reported for the fabrication of photonic synaptic transistors.The combination of flat surface,outstanding optical absorption,and remarkable charge transporting performance contributes to high-efficiency photon-to-electron conversion for such perovskite-based synapses.High-performance photonic synaptic transistors are thus fabricated with essential synaptic functionalities,including excitatory postsynaptic current(EPSC),paired-pulse facilitation(PPF),and long-term memory.By utilizing the photonic potentiation and electrical depression features,perovskite-based photonic synaptic transistors are also explored for neuromorphic computing simulations,showing high pattern recognition accuracy of up to 89.98%,which is one of the best values reported so far for synaptic transistors used in pattern recognition.This work provides an effective and convenient pathway for fabricating perovskite-based neuromorphic systems with high pattern recognition accuracy.

Floating-gate based PN blending optoelectronic synaptic transistor for neural machine translation

Neural machine translation, which has an encoder-decoder framework, is considered to be a feasible way for future machine translation. Nevertheless, with the fusion of multiple languages and the continuous emergence of new words, most current neural machine translation systems based on von Neumann’s architecture have seen a substantial increase in the number of devices for the decoder, resulting in high-energy consumption rate. Here, a multilevel photosensitive blending semiconductor optoelectronic synaptic transistor(MOST) with two different trapping mechanisms is firstly demonstrated, which exhibits 8 stable and well distinguishable states and synaptic behaviors such as excitatory postsynaptic current, short-term memory, and long-term memory are successfully mimicked under illumination in the wavelength range of 480–800 nm. More importantly, an optical decoder model based on MOST is successfully fabricated,which is the first application of neuromorphic device in the field of neural machine translation, significantly simplifying the structure of traditional neural machine translation system.Moreover, as a multi-level synaptic device, MOST can further reduce the number of components and simplify the structure of the codec model under light illumination. This work first applies the neuromorphic device to neural machine translation, and proposes a multi-level synaptic transistor as the based cell of decoding module, which would lay the foundation for breaking the bottleneck of machine translation.

Monolayer molecular crystals for low-energy consumption optical synaptic transistors

Artificial synaptic devices hold great potential in building neuromorphic computers.Due to the unique morphological features,twodimensional organic semiconductors at the monolayer limit show interesting properties when acting as the active layers for organic field-effect transistors.Here,organic synaptic transistors are prepared with 1,4-bis((5’-hexyl-2,2’-bithiophen-5-yl)ethyl)benzene(HTEB)monolayer molecular crystals.Functions similar to biological synapses,including excitatory postsynaptic current(EPSC),pair-pulse facilitation,and short/long-term memory,have been realized.The synaptic device achieves the minimum power consumption of 4.29 fJ at low drain voltage of−0.01 V.Moreover,the HTEB synaptic device exhibits excellent long-term memory with 109 s EPSC estimated retention time.Brain-like functions such as dynamic learning-forgetting process and visual noise reduction are demonstrated by nine devices.The unique morphological features of the monolayer molecular semiconductors help to reveal the device working mechanism,and the synaptic behaviors of the devices can be attributed to oxygen induced energy level.This work shows the potential of artificial neuroelectronic devices based on organic monolayer molecular crystals.

Short-Term Synaptic Plasticity Mimicked on Ionic/Electronic Hybrid Oxide Synaptic Transistor Gated by Nanogranular SiO_2 Films

An indium-zinc-oxide （IZO） based ionic/electronic hybrid synaptic transistor gated by field-configurable nanogranular SiO2 films was reported. The devices exhibited a high current ON/OFF ratio of above 107, a high electron mobility of ～14 cm2 V^-1 s^-1 and a low subthreshold swing of ～80 mV/decade. The gate bias would modulate the interplay between protons and electrons at the channel/dielectric interface. Due to the dynamic modulation of the transient protons flux within the nanogranular SiO2 films, the channel current would be modified dynamically. Short-term synaptic plasticities, such as short-term potentiation and short- term depression, were mimicked on the proposed IZO synaptic transistor. The results indicate that the synaptic transistor proposed here has potential applications in future neuromorphic devices.

Piezotronic neuromorphic devices:principle,manufacture,and applications

With the arrival of the era of artificial intelligence(AI)and big data,the explosive growth of data has raised higher demands on computer hardware and systems.Neuromorphic techniques inspired by biological nervous systems are expected to be one of the approaches to breaking the von Neumann bottleneck.Piezotronic neuromorphic devices modulate electrical transport characteristics by piezopotential and directly associate external mechanical motion with electrical output signals in an active manner,with the capability to sense/store/process information of external stimuli.In this review,we have presented the piezotronic neuromorphic devices(which are classified into strain-gated piezotronic transistors and piezoelectric nanogenerator-gated field effect transistors based on device structure)and discussed their operating mechanisms and related manufacture techniques.Secondly,we summarized the research progress of piezotronic neuromorphic devices in recent years and provided a detailed discussion on multifunctional applications,including bionic sensing,information storage,logic computing,and electrical/optical artificial synapses.Finally,in the context of future development,challenges,and perspectives,we have discussed how to modulate novel neuromorphic devices with piezotronic effects more effectively.It is believed that the piezotronic neuromorphic devices have great potential for the next generation of interactive sensation/memory/computation to facilitate the development of the Internet of Things,AI,biomedical engineering,etc.

Weak UV‑Stimulated Synaptic Transistors Based on Precise Tuning of Gallium‑Doped Indium Zinc Oxide Nanofibers

In this work,a light-stimulated artificial synaptic transistor based on one-dimensional nanofibers of gallium-doped indium zinc oxides(IGZO)is demonstrated.The introduction of gallium into the nanofiber lattice can effectively alter the morphology and crystallinity,leading to a wider regulatory range of synaptic plasticity.The fabricated IGZO synaptic transistor with the optimal gallium concentration and low surface defects exhibits a superior photoresponsivity of 4300 A・W^(−1)and excellent photosensitivity,which can detect light signals as weak as 0.03 mW・cm^(−2).In particular,the paired-pulse facilitation index reaches up to 252%with over 2 h of enhanced memory retention exhibiting the long-term potentiation.Furthermore,the simulated image contrast and image recognition accuracy based on the newly designed IGZO synaptic transistors are successfully enhanced.These remarkable behaviors of light-stimulated synapses utilizing low-cost electrospun nanofibers have potential for ultraweak light applications in future artificial systems.

Photonic synapses with ultralow energy consumption for artificial visual perception and brain storage

The human visual system,dependent on retinal cells,can be regarded as a complex combination of optical system and nervous system.Artificial retinal system could mimic the sensing and processing function of human eyes.Optically stimulated synaptic devices could serve as the building blocks for artificial retinas and subsequent information transmission system to brain.Herein,photonic synaptic transistors based on polycrystalline MoS_(2),which could simulate human visual perception and brain storage,are presented.Moreover,the photodetection range from visible light to near-infrared light of MoS_(2) multilayer could extend human eyes’vision limitation to near-infrared light.Additionally,the photonic synaptic transistor shows an ultrafast speed within 5μs and ultralow power consumption under optical stimuli about 40 aJ,several orders of magnitude lower than biological synapses(50 ms and 10 fJ).Furthermore,the backgate control could act as emotional modulation of the artificial brain to enhance or suppress memory function,i.e.the intensity of photoresponse.The proposed carrier trapping/detrapping as the main working mechanism is presented for the device.In addition,synaptic functionalities including short synaptic plasticity,long synaptic plasticity and paired-pulse facilitation could be successfully simulated based on the prepared device.Furthermore,the large difference between short synaptic plasticity and long synaptic plasticity reveals the better image pre-processing function of the prepared photonic synapses.The classical Pavlovian conditioning associated with the associative learning is successfully implemented as well.Therefore,the efficient and rich functionalities demonstrate the potential of the MoS_(2) synaptic device that integrates sensing-memory-preprocessing capabilities for realizing artificial neural networks with different emotions that mimic human retina and brain.

Charge trap-based carbon nanotube transistor for synaptic function mimicking

Brain-inspired neuromorphic computing is expected for breaking through the bottleneck of the computer of conventional von Neumann architecture. To this end, the first step is to mimic functions of biological neurons and synapses by electronic devices. In this paper, synaptic transistors were fabricated by using carbon nanotube (CNT) thin films and interface charge trapping effects were confirmed to dominate the weight update of the synaptic transistors. Large synaptic weight update was realized due to the high sensitivity of the CNTs to the trapped charges in vicinity. Basic synaptic functions including inhibitory post-synaptic current (IPSC), excitatory post-synaptic current (EPSC), spike-timing-dependent plasticity (STDP), and paired-pulse facilitation (PPF) were mimicked. Large dynamic range of STDP (> 2,180) and low power consumption per spike (∼ 0.7 pJ) were achieved. By taking advantage of the long retention time of the trapped charges and uniform device-to-device performance, long-term image memory behavior of neural network was successfully imitated in a CNT synaptic transistor array.

Advanced synaptic devices and their applications in biomimetic sensory neural system

Human nervous system,which is composed of neuron and synapse networks,is capable of processing information in a plastic,dataparallel,fault-tolerant,and energy-efficient approach.Inspired by the ingenious working mechanism of this miraculous biological data processing system,scientists have been devoting great efforts to ar-tificial neural systems based on synaptic devices in recent decades.The continuous development of bioinspired sensors and synaptic devices in recent years have made it possible that artificial sensory neural systems are capable of capturing and processing stimuli informa-tion in real time.The progress of biomimetic sensory neural systems could provide new methods for next-generation humanoid robotics,human-machine interfaces,and other frontier applications.Herein,this review summarized the recent progress of synaptic devices and biomimetic sensory neural systems.Additionally,the opportunities and remaining challenges in the further development of biomimetic sensory neural systems were also outlined.

用于低能耗人工视觉系统的具有互补光调制和低功耗的双极突触有机/无机异质结晶体管

光电突触晶体管将光传感和突触功能集成到单个器件中,在视觉信息采集、识别、记忆和处理的神经形态计算具有显著的优势.然而,现有光电突触的权重更新主要是基于光刺激和电刺激分别调节突触的兴奋和抑制.这种方式严重限制了器件的处理速度和应用场景.在这项工作中,我们提出了双极突触有机/无机异质结晶体管(BSOIHT),可以有效地模拟光刺激下的双向(兴奋/抑制)突触行为.此外,通过优化电极接触位置以及电极材料,晶体管的载流子注入得到了显著改善,使得突触事件功耗降至2.4 fJ.此外,采用BSOIHT构建的神经形态视觉系统,有效地促进了图像预处理,将识别准确率从44.93%大幅提高到87.01%.这为构建低能耗的人工视觉系统提供了新的途径.

A thermally crosslinked ion-gel gated artificial synapse

We demonstrate a synaptic transistor that uses a thermally crosslinked three-dimensional network to accommodate ionic liquid to form an ion gel layer. The synaptic transistor successfully emulated important synaptic plasticity, such as paired-pulse facilitation, spike-number dependent plasticity, spike-voltage dependent plasticity, and spike-rate dependent plasticity;these responses imply successful use of the ion gel. Moreover, the device realized “OR” and “AND” logic operations, and high-pass filtering behavior. Energy consumption of the device can be reduced to sub-femtojoule level, which is below that of biological synapses. Compared with traditional physical cross-linking using block copolymers, this method provides a facile strategy to prepare ion gels with tunable properties by altering the polymers and crosslinkers,and to enormously reduce the price by replacing expensive block copolymers or eliminating additional synthesis processes. This report provides a versatile strategy for design of synaptic transistors and their applications in neuromorphic electronics.