Principles and Research Progress of Artificial Lightemitting Synaptic Devices

Artificial neural networks (ANNs) based on synaptic devices, which can simultaneously perform processing and storage of data, have superior computing performance compared to conventional von Neumann architectures. Here, we present a ferroelectric coupled artificial synaptic device with reliable weight update and storage properties for ANNs. The artificial synaptic device, which is based on a ferroelectric polymer capacitively coupled with an oxide dielectric via an electric-field-permeable, semiconducting single-walled carbon-nanotube channel, is successfully fabricated by inkjet printing. By controlling the ferroelectric polarization, synaptic dynamics, such as excitatory and inhibitory postsynaptic currents and long-term potentiation/depression characteristics, is successfully implemented in the artificial synaptic device. Furthermore, the constructed ANN, which is designed in consideration of the device-to-device variation within the synaptic array, efficiently executes the tasks of learning and recognition of the Modified National Institute of Standards and Technology numerical patterns.

Neuromorphic computing, an emerging computational paradigm, aims to overcome the bottlenecks of the traditional von Neumann architecture. Two-dimensional materials serve as ideal platforms for constructing artificial synaptic devices, yet existing devices based on these materials face challenges such as insufficient stability. Indium selenide (InSe), a two-dimensional semiconductor with unique properties, demonstrates significant potential in the field of neuromorphic devices, though its application research remains in the initial stage. This study presents an artificial synaptic device based on the InSe/Charge Trapping Layer (CTL)/h-BN heterojunction. By applying oxygen plasma treatment to h-BN to form a controllable charge-trapping layer, efficient regulation of carriers in the InSe channel is achieved. The device successfully emulates fundamental synaptic behaviors including paired-pulse facilitation and long-term potentiation/inhibition, exhibiting excellent reproducibility and stability. Through investigating the influence of electrical pulse parameters on synaptic weights, a structure–activity relationship between device performance and structural parameters is established. Experimental results show that the device features outstanding linearity and symmetry, realizing the simulation of key synaptic behaviors such as dynamic conversion between short-term and long-term plasticity. It possesses a high dynamic range ratio of 7.12 and robust multi-level conductance tuning capability, with stability verified through 64 pulse cycle tests. This research provides experimental evidence for understanding interfacial charge storage mechanisms, paves the way for developing high-performance neuromorphic computing devices, and holds broad application prospects in brain-inspired computing and artificial intelligence hardware.

An artificial synaptic device that can provide color discrimination, image storage, and image recognition is highly required to mimic the human vision for biological robots. All-inorganic halide perovskites have attracted extensive attention for the reason of their high stability and favorable photoelectric properties. In this study, a light-stimulated synaptic phototransistor based on a CsPbBr3/organic semiconductor hybrid film is reported. The fabricated CsPbBr3 film exhibits an island structure, which reduces the hysteresis effectively and at the same time achieves a high specific detectivity of up to 2 × 1015 Jones. The decay of the photocurrent can be delayed by changing the gate bias, which is essential for achieving high-performance light-stimulated synaptic devices. Due to the outstanding detectivity of the device, the obvious synaptic functions can be observed when triggered by a light signal with a power of 1.6 nW that is much weaker than previous most perovskite-based hybrid synaptic phototransistors under a low operating voltage of -1 V. The electrical power consumption of the device could be as low as 0.076 pJ when the power of light spike was 7.36 nW. Taking into account this characterization, with changing of light intensity or wavelength, the contrast of the image was enlarged, which can further promote the image recognition accuracy. More significantly, this CsPbBr3/TIPS hybrid film can be fabricated by facile and low-cost solution processes. This study indicates the great potential of solution-processed perovskite-based light-stimulated synapses for future artificial visual systems.

The development of high-performance artificial synaptic neuromorphic devices poses a significant challenge in the creation of biomimetic sensing neural systems that seamlessly integrate both sensory and computational functionalities. In pursuit of this objective, promising bionic opto-olfactory co-sensory artificial synapse devices are constructed utilizing the BP-C/CNT (2D/1D) hybrid filter membrane as the resistive layer. Experimental results demonstrated that the devices seamlessly integrated the light modulation, gas detection, and biological synaptic functions into a single device while addressing the challenge with separating artificial synaptic devices from sensors. These devices offered the following advantages: 1) Simulating visual synapses, they can effectively replicate fundamental synaptic functions under both electrical and optical stimulation. 2) By emulating olfactory synapse responses to specific gases, they can achieve ultra-low detection limits and rapid identification of ethanol and acetone gases. 3) They enable photo-olfactory co-sensing simulations that mimic synaptic function under light-modulated pulse conditions in distinct gas environments, facilitating the study of synaptic learning rules and Pavlovian responses. This work provides a pioneering approach for exploring highly stable 2D BP-based optoelectronics and advancing the development of biomimetic neural systems.

Neuromorphic and cognitive computing with a capability of analyzing complicated information is explored as a new paradigm of intelligent systems. An implementation of a renewable material as an essential building block of an artificial synaptic device is suggested and a flexible and transparent synaptic device based on collagen extracted from fish skin is demonstrated. This device exhibits essential synaptic behaviors including analog memory characteristics, excitatory postsynaptic current, and paired‐pulse facilitation as short‐term plasticity. The brain‐inspired electronic synapse undergoes incremental potentiation and depression when flat or bent. The device emulates spike‐timing‐dependent plasticity when stimulated by engineered pre‐ and post‐neuron spikes with the appropriate time difference between the imposed pulses. The proposed synaptic device has the advantage of being biocompatible owing to use of Mg electrodes and collagen as a naturally abundant protein. This device has a potential to be used in flexible and implantable neuromorphic systems in the future.

In this study, we deposited a TiO2 photosensitive layer on SrTiO3 film by the spin-coating method and successfully developed an artificial synaptic device with the integration of sense-store-computation functions. The energy band structure of the heterojunction and the direction of the built-in electric field are determined by steady-state absorption spectroscopy. The carrier relaxation dynamics at the femtosecond level of the device is observed by transient absorption spectroscopy, and it is determined that the built-in electric field of the heterojunction dominates the complex dynamics of the photogenerated carriers. External bias is used to modulate this electric field, and intelligent processing of the photovoltaic signals is realized. Through the comodulation of photoelectric signals on the artificial synaptic device, intelligent applications are realized in three dimensions: sensing-storage-computing. The device has a long retention time (>1000 s), function switching based on different wavelengths of light signals (positive and negative optical conductance and photodetector functions can be realized separately), and high handwriting digit set detection accuracy (>95%). This study integrates the dual advantages of low-cost processing and a simple preparation process to prepare an integrated sensor-storage-computing device, which opens up a manufacturing path for the construction of a high-density integrated photoelectric device.

Brain-inspired computing is considered to be an effective solution to the bottleneck of the von Neumann system, and artificial nerve synaptic devices are a key part of brain-like computing. Here, we propose an artificial nerve synapse device based on graphene and ion gel gated transistors, which uses graphene as the channel and ion gel as the gate electrolyte. The weight of the nerve synapse can be adjusted by the gate voltage, and the artificial nerve synaptic device can successfully realize the important synaptic functions of the nerve synapse such as excitatory postsynaptic membrane current, inhibitory postsynaptic membrane current, enhancement, inhibition and impulse alienation. These results indicate that graphene ion gel artificial nerve synapses have good application prospects in cranial nerve-like computing and flexible circuits.

Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics. Although two-terminal memristors and three-terminal synaptic transistors have exhibited significant capabilities in the artificial synapse, more stable devices and simpler integration are needed in practical applications. Combining the configuration advantages of memristors and transistors, a novel pseudo-transistor is proposed. Here, recent advances in the development of pseudo-transistor-based neuromorphic electronics in recent years are reviewed. The working mechanisms, device structures and materials of three typical pseudo-transistors, including tunneling random access memory (TRAM), memflash and memtransistor, are comprehensively discussed. Finally, the future development and challenges in this field are emphasized.

The association of stimuli is an important attribute in the neural basis of learning and memory. While the acquisition and extinction of association through conditioning are well emulated in artificial synaptic devices, the alteration of conditioning efficacy, which enables adaptability in learning, has yet to be demonstrated. A distinctive feature of latent inhibition is that the pre‐exposure to a neutral stimulus would suppress its subsequent associative pairing with a biologically salient counterpart. This presents an adaptive advantage in suppressing the learning efficacy of irrelevant stimuli, focusing attention only on relevant cues. Given the significant impact of the regulatory function in biological synapses, an associatively responsive optoelectronic synapse based on oxide Schottky interface capable of emulating latent inhibition is demonstrated. While optical programming based on photo‐assisted charge detrapping emulates the biologically salient stimulus, electrical modification acts as neutrally stimulating cues, capable of altering subsequent carrier recombination dynamics. The electrical–optical coupling is leveraged to implement inhibition and facilitation of synaptic plasticity. Subsequently, the adaptability in conditioning to regulate information uptake is demonstrated via latent inhibition. Distinct from conventional optoelectronic synapses, the proposed synaptic device offers significant advantages in adaptability in learning with an electrically tunable optical memory.

A flexible BiFeO3-based ferroelectric tunnel junction memristor for neuromorphic computing

Following the recent advances in artificial synaptic devices and the renewed interest regarding artificial intelligence and neuromorphic computing, a new two-terminal resistive switching device, based on mobile Li+ ions is hereby explored. Emulation of neural functionalities in a biorealistic manner has been recently implemented through the use of synaptic devices with diffusive dynamics. Mimicking of the spontaneous synaptic weight relaxation of neuron cells, which is regulated by the concentration kinetics of positively charged ions like Ca2+, is facilitated through the conductance relaxation of such diffusive devices. Adopting a battery-like architecture, using LiCoO2 as a resistive switching cathode layer, SiOx as an electrolyte and TiO2 as an anode, Au/LiCoO2/SiOx/TiO2/p++-Si two-terminal devices have been fabricated. Analog conductance modulation, via voltage-driven regulation of Li+ ion concentration in the cathode and anode layers, along with current rectification and nanobattery effects are reported. Furthermore, evidence is provided for biorealistic synaptic behavior, manifested as paired pulse facilitation based on the summation of excitatory post-synaptic currents and spike-timing-dependent plasticity, which are governed by the Li+ ion concentration and its relaxation dynamics.

We demonstrate a flexible nonvolatile multilevel memory and artificial synaptic devices based on the Cu/P(VDF-TrFE)/Ni memtranstor which exhibits pronounced nonlinear magnetoelectric effects at room temperature. The states of the magnetoelectric voltage coefficient αE of the memtranstor are used to encode binary information. By applying selective electric-field pulses, the states of αE can be switched repeatedly among 2n states (n = 1, 2, 3) in a zero dc bias magnetic field. In addition, the magnetoelectric coefficient is used to act as synaptic weight, and the induced magnetoelectric voltage VME is regarded as postsynaptic potentials (excitatory or inhibitory). The artificial synaptic devices based on the Cu/P(VDF-TrFE)/Ni memtranstor display the long-term potentiation (depression) and spiking-time-dependent plasticity behaviors. The advantages of a simple structure, flexibility, multilevel, and self-biasing make the Cu/P(VDF-TrFE)/Ni organic memtranstor a promising candidate for applications in nonvolatile memory as well as artificial synaptic devices with low energy consumption.

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

Electroluminescent synaptic devices with logic functions

Neuromorphic computing, inspired by the functionality of biological neural networks, has emerged as a promising paradigm for artificial intelligence applications, especially in the field of flexible electronics. Among the various artificial synaptic devices, floating‐gate synaptic transistors exhibit long‐term synaptic plasticity, but they face the challenge of achieving flexible compatibility. In this work, the first demonstration of a flexible MXene floating‐gate synaptic transistor is reported, which uses multiple layers of MXene as floating gates and MXene nanosheets as charge state modulators. The device shows excellent mechanical flexibility and can operate at low voltages, which improves its suitability for wearable electronic devices. It can also emulate Pavlovian conditioned reflexes under external stress, suggesting its potential for cognitive learning. Moreover, the device is utilized for handwritten digit recognition by simulating a fully connected neural network, achieving a high recognition accuracy of 92.0%. This demonstrates its practical applicability in neuromorphic computing. Besides, this research achieves the patterning of MXene and its application in flexible floating‐gate transistors. It provides a new solution for the integrated fabrication of flexible artificial synaptic devices.