Threshold-Tunable, Spike-Rate-Dependent Plasticity Originating from Interfacial Proton Gating for Pattern Learning and Memory

Electrolyte Gated Transistors for Brain Inspired Neuromorphic Computing and Perception Applications: A Review

Emerging neuromorphic computing offers a promising and energy-efficient approach to developing advanced intelligent systems by mimicking the information processing modes of the human brain. Moreover, inspired by the high parallelism, fault tolerance, adaptability, and low power consumption of brain perceptual systems, replicating these efficient and intelligent systems at a hardware level will endow artificial intelligence (AI) and neuromorphic engineering with unparalleled appeal. Therefore, construction of neuromorphic devices that can simulate neural and synaptic behaviors are crucial for achieving intelligent perception and neuromorphic computing. As novel memristive devices, electrolyte-gated transistors (EGTs) stand out among numerous neuromorphic devices due to their unique interfacial ion coupling effects. Thus, the present review discusses the applications of the EGTs in neuromorphic electronics. First, operational modes of EGTs are discussed briefly. Second, the advancements of EGTs in mimicking biological synapses/neurons and neuromorphic computing functions are introduced. Next, applications of artificial perceptual systems utilizing EGTs are discussed. Finally, a brief outlook on future developments and challenges is presented.

Two-Terminal Neuromorphic Devices for Spiking Neural Networks: Neurons, Synapses, and Array Integration

The ever-increasing volume of complex data poses significant challenges to conventional sequential global processing methods, highlighting their inherent limitations. This computational burden has catalyzed interest in neuromorphic computing, particularly within artificial neural networks (ANNs). In pursuit of advancing neuromorphic hardware, researchers are focusing on developing computation strategies and constructing high-density crossbar arrays utilizing history-dependent, multistate nonvolatile memories tailored for multiply-accumulate (MAC) operations. However, the real-time collection and processing of massive, dynamic data sets require an innovative computational paradigm akin to that of the human brain. Spiking neural networks (SNNs), representing the third generation of ANNs, are emerging as a promising solution for real-time spatiotemporal information processing due to their event-based spatiotemporal capabilities. The ideal hardware supporting SNN operations comprises artificial neurons, artificial synapses, and their integrated arrays. Currently, the structural complexity of SNNs and spike-based methodologies requires hardware components with biomimetic behaviors that are distinct from those of conventional memristors used in deep neural networks. These distinctive characteristics required for neuron and synapses devices pose significant challenges. Developing effective building blocks for SNNs, therefore, necessitates leveraging the intrinsic properties of the materials constituting each unit and overcoming the integration barriers. This review focuses on the progress toward memristor-based spiking neural network neuromorphic hardware, emphasizing the role of individual components such as memristor-based neurons, synapses, and array integration along with relevant biological insights. We aim to provide valuable perspectives to researchers working on the next generation of brain-like computing systems based on these foundational elements.

Low Latency and Sparse Computing Spiking Neural Networks With Self-Driven Adaptive Threshold Plasticity

Spiking neural networks (SNNs) have captivated the attention worldwide owing to their compelling advantages in low power consumption, high biological plausibility, and strong robustness. However, the intrinsic latency associated with SNNs during inference poses a significant challenge, impeding their further development and application. This latency is caused by the need for spiking neurons to collect electrical stimuli and generate spikes only when their membrane potential exceeds a firing threshold. Considering the firing threshold plays a crucial role in SNN performance, this article proposes a self-driven adaptive threshold plasticity (SATP) mechanism, wherein neurons autonomously adjust the firing thresholds based on their individual state information using unsupervised learning rules, of which the adjustment is triggered by their own firing events. SATP is based on the principle of maximizing the information contained in the output spike rate distribution of each neuron. This article derives the mathematical expression of SATP and provides extensive experimental results, demonstrating that SATP effectively reduces SNN inference latency, further reduces the computation density while improving computational accuracy, so that SATP facilitates SNN models to be with low latency, sparse computing, and high accuracy.

Influence of the TiN diffusion barrier on the leakage current and ferroelectricity in an Al-doped HfOx ferroelectric memristor and its application to neuromorphic computing

The HfOx-based ferroelectric memristor is in the spotlight due to its complementary metal-oxide-semiconductor compatibility and scaling compared to existing perovskite-based ferroelectric memory. However, ferroelectric properties vary depending on the coefficient of thermal expansion of the top electrode, which is caused by strain engineering. When tungsten (W) with a small coefficient of thermal expansion is used as an electrode, the ferroelectric properties are improved, although the reliability is poor due to the diffusion of W atoms. Here, TiN can be used to prevent the diffusion of W. This metal nitride successfully suppresses the leakage current and induces a larger remanent polarization of 19.7 μC cm-2, a smaller coercive voltage of 9.26 V, and a faster switching speed. W/TiN/HAO/n+ Si can also exhibit multi-level characteristics and achieve a 10% read margin in 320 × 320 arrays. Ferroelectrics can also be applied to neuromorphic computing by imitating synaptic properties such as potentiation, depression, paired-pulse facilitation, and excitatory postsynaptic current. Using short-term plasticity, successful implementation in reservoir computing is also realized, achieving 95% classification accuracy. This paper shows promise for the use of memristors in artificial neural networks.

Bioinspired Flexible and Low-Voltage Organic Synaptic Transistors for UV Light-Driven Vision Systems

Neuromorphic vision systems, particularly those stimulated by ultraviolet (UV) light, hold great potential applications in portable electronics, wearable technology, biological analysis, military surveillance, etc. Organic artificial synaptic devices hold immense potential in this field due to their ease of processing, flexibility, and biocompatibility. In this work, we have fabricated a flexible organic field-effect transistor (OFET) that utilizes chitosan-silver nanoparticles (AgNPs) composite material as the active dielectric material. During UV light illumination, both silver nanoparticles and the pentacene layer generate a large number of charge carriers. The photogenerated carriers lead to a more significant hole accumulation at the pentacene interface, resulting in a current rise. In the absence of light, the trapped electron in the silver nanoparticles persists for a longer duration, preventing the instant recombination with holes. This extended retention of electrons leads to the observed synaptic performance of the transistor. The use of aluminum oxide (Al2O3) as one of the dielectric layers enables the device to operate effectively at low voltage (<1 V). The device mimics various crucial synaptic properties of the brain, including short-term potentiation and long-term potentiation (STP and LTP), paired-pulse facilitation (PPF), spike-duration dependent plasticity (SDDP), spike-number dependent plasticity (SNDP), and spike-rate dependent plasticity (SRDP), etc. This work introduces an approach to develop flexible organic synaptic transistors that operate efficiently at low voltages, paving the way toward high-performance, UV light-driven neuromorphic vision systems.

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.

Oxide Ionic Neuro-Transistors for Bio-inspired Computing

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.

Optoelectronic synapses based on a triple cation perovskite and Al/MoO3 interface for neuromorphic information processing

Optoelectronic synaptic transistors are attractive for applications in next-generation brain-like computation systems, especially for their visible-light operation and in-sensor computing capabilities. However, from a material perspective, it is difficult to build a device that meets expectations in terms of both its functions and power consumption, prompting the call for greater innovation in materials and device construction. In this study, we innovatively combined a novel perovskite carrier supply layer with an Al/MoO3 interface carrier regulatory layer to fabricate optoelectronic synaptic devices, namely Al/MoO3/CsFAMA/ITO transistors. The device could mimic a variety of biological synaptic functions and required ultralow-power consumption during operation with an ultrafast speed of >0.1 μs under an optical stimulus of about 3 fJ, which is equivalent to biological synapses. Moreover, Pavlovian conditioning and visual perception tasks could be implemented using the spike-number-dependent plasticity (SNDP) and spike-rate-dependent plasticity (SRDP). This study suggests that the proposed CsFAMA synapse with an Al/MoO3 interface has the potential for ultralow-power neuromorphic information processing.

Synaptic Plasticity Modulation of Neuromorphic Transistors through Phosphorus Concentration in Phosphosilicate Glass Electrolyte Gate

This study proposes a phosphosilicate glass (PSG)-based electrolyte gate synaptic transistor with varying phosphorus (P) concentrations. A metal oxide semiconductor capacitor structure device was employed to measure the frequency-dependent (C-f) capacitance curve, demonstrating that the PSG electric double-layer capacitance increased at 103 Hz with rising P concentration. Fourier transform infrared spectroscopy spectra analysis facilitated a theoretical understanding of the C-f curve results, examining peak differences in the P-OH structure based on P concentration. Using the proposed synaptic transistors with different P concentrations, changes in the hysteresis window were investigated by measuring the double-sweep transfer curves. Subsequently, alterations in proton movement within the PSG and charge characteristics at the channel/PSG electrolyte interface were observed through excitatory post-synaptic currents, paired-pulse facilitation, signal-filtering functions, resting current levels, and potentiation and depression characteristics. Finally, we demonstrated the proposed neuromorphic system's feasibility based on P concentration using the Modified National Institute of Standards and Technology learning simulations. The study findings suggest that, by adjusting the PSG film's P concentration for the same electrical stimulus, it is possible to selectively mimic the synaptic signal strength of human synapses. Therefore, this approach can positively contribute to the implementation of various neuromorphic systems.

Bidirectional Invisible Photoresponse Implemented in a Traps Matrix-Combination toward Fully Optical Artificial Synapses

Fully optical artificial synapses are crucial hardware for neuromorphic computing, which is very promising to address the future large-scale computing capacity problem. The key characteristic required in a semiconductor device to emulate synaptic potentiation and depression in a fully optical artificial synapse is the bidirectional photoresponse. This work integrates wide-band-gap TiO2 polycrystals and narrow-band-gap PbS quantum dots into a graphene transistor simultaneously, providing the device with both near-ultraviolet and near-infrared photoresponses through the photogating effect. Moreover, the TiO2 serves as a hole-trapping matrix and the PbS as an electron-trapping matrix, which impose opposite effects to the device after photoexcitation, resulting in a photoresponse in the opposite polarity. As a result, the device demonstrates a wavelength-dependent bidirectional photoresponse, which enables it to be utilized as a fully optical artificial synapse. By using near-ultraviolet or near-infrared lights as stimuli, the device successfully mimics synaptic plasticity, including synaptic potentiation/depression, paired-pulse facilitation, and spike-rating-dependent plasticity, as well as the human brain-like transition of short-term memory and long-term memory and learning-experience behavior. This work validates the methodology of combining different trap matrices to achieve the bidirectional photoresponse, which can significantly inspire future research in fully optical artificial synapses.

TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance

We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature. It is shown that this effect is important and greatly depends on the noise statistical characteristics.

A Digital-Analog Bimodal Memristor Based on CsPbBr3 for Tactile Sensory Neuromorphic Computing

Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information-processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr3 -based memristor demonstrates a high ON/OFF ratio (>103 ), long retention (>104 s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5 × 5 spinosum-structured piezoresistive sensor array (sensitivity of 22.4 kPa-1 , durability of 1.5 × 104 cycles, and fast response time of 2.43 ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr3 -based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5 × 5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital-analog bimodal memristor would demonstrate potential application in human-machine interaction, prosthetics, and artificial intelligence.

Artificial synapse based on 1,4-diphenylbutadiyne with femtojoule energy consumption

Memristors as electronic artificial synapses have attracted increasing attention in neuromorphic computing. Especially, organic small molecule artificial synapses show great promise for low-energy neuromorphic devices. In this study, the basic functions of biological synapses including paired-pulse facilitation/paired-pulse depression (PPF/PPD), spike rate-dependent plasticity (SRDP) and fast Bienenstock-Cooper-Munro learning rules (BCM) have been successfully simulated in the 1,4-diphenylbutadiyne (DPDA) memristor device. Furthermore, ultra-low energy consumption (∼25 fJ per spike), linear and large conductance changes have been obtained in the small molecule DPDA device. This work makes a great contribution to improve the accuracy, speed and to reduce the energy consumption for neuromorphic computing.

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 electrolyte-gated transistors for neuromorphic perception

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.

Metal Halide Perovskite/Electrode Contacts in Charge-Transporting-Layer-Free Devices

Metal halide perovskites have drawn substantial interest in optoelectronic devices in the past decade. Perovskite/electrode contacts are crucial for constructing high-performance charge-transporting-layer-free perovskite devices, such as solar cells, field-effect transistors, artificial synapses, memories, etc. Many studies have evidenced that the perovskite layer can directly contact the electrodes, showing abundant physicochemical, electronic, and photoelectric properties in charge-transporting-layer-free perovskite devices. Meanwhile, for perovskite/metal contacts, some critical interfacial physical and chemical processes are reported, including band bending, interface dipoles, metal halogenation, and perovskite decomposition induced by metal electrodes. Thus, a systematic summary of the role of metal halide perovskite/electrode contacts on device performance is essential. This review summarizes and discusses charge carrier dynamics, electronic band engineering, electrode corrosion, electrochemical metallization and dissolution, perovskite decomposition, and interface engineering in perovskite/electrode contacts-based electronic devices for a comprehensive understanding of the contacts. The physicochemical, electronic, and morphological properties of various perovskite/electrode contacts, as well as relevant engineering techniques, are presented. Finally, the current challenges are analyzed, and appropriate recommendations are put forward. It can be expected that further research will lead to significant breakthroughs in their application and promote reforms and innovations in future solid-state physics and materials science.

Sputtered Electrolyte-Gated Transistor with Temperature-Modulated Synaptic Plasticity Behaviors

Temperature has always been considered as an essential factor for almost all kinds of semiconductor-based electronic components. In this work, temperature-dependent synaptic plasticity behaviors, which are mimicked by the indium-gallium-zinc oxide thin-film transistors gated with sputtered SiO2 electrolytes, have been studied. With the temperature increasing from 303 to 323 K, the electrolyte capacitance decreases from 0.42 to 0.11 μF cm-2. The mobility increases from 1.4 to 3.7 cm2 V-1 s-1, and the threshold voltage negatively shifts from -0.23 to -0.51 V. Synaptic behaviors under both a single pulse and multiple pulses are employed to study the temperature dependence. With the temperature increasing from 303 to 323 K, the post-synaptic current (PSC) at the resting state increases from 1.8 to 7.3 μA. Under a single gate pulse of 1 V and 1 s, the PSC signal altitude and the PSC retention time decrease from 2.0 to 0.7 μA and 5.1 × 102 to 2.5 ms, respectively. A physical model based on the electric field-induced ion drifting, ionic-electronic coupling, and gradient-coordinated ion diffusion is proposed to understand these temperature-dependent synaptic behaviors. Based on the experimental data on individual transistors, temperature-modulated pattern learning and memorizing behaviors are conceptually demonstrated. The in-depth investigation of the temperature dependence helps pave the way for further electrolyte-gated transistor-based neuromorphic applications.

Ultralow-power flexible transparent carbon nanotube synaptic transistors for emotional memory

Emulating the biological behavior of the human brain with artificial neuromorphic devices is essential for the future development of human-machine interactive systems, bionic sensing systems and intelligent robotic systems. In this paper, artificial flexible transparent carbon nanotube synaptic transistors (F-CNT-STs) with signal transmission and emotional learning functions are realized by adopting the poly(vinyl alcohol) (PVA)/SiO2 proton-conducting electrolyte. Synaptic functions of biological synapses including excitatory and inhibitory behaviors are successfully emulated in the F-CNT-STs. Besides, synaptic plasticity such as spike-duration-dependent plasticity, spike-number-dependent plasticity, spike-amplitude-dependent plasticity, paired-pulse facilitation, short-term plasticity, and long-term plasticity have all been systematically characterized. Moreover, the F-CNT-STs also closely imitate the behavior of human brain learning and emotional memory functions. After 1000 bending cycles at a radius of 3 mm, both the transistor characteristics and the synaptic functions can still be implemented correctly, showing outstanding mechanical capability. The realized F-CNT-STs possess low operating voltage, quick response, and ultra-low power consumption, indicating their high potential to work in low-power biological systems and artificial intelligence systems. The flexible artificial synaptic transistor enables its potential to be generally applicable to various flexible wearable biological and intelligent 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.

Oxide-Based Electrolyte-Gated Transistors for Spatiotemporal Information Processing

Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time- and energy-efficient computational paradigms for the Internet-of-Things and edge computing. Nonvolatile electrolyte-gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large-scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide-based EGT employing amorphous Nb₂ O₅ and Li_x SiO₂ is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi-linear update, good endurance (10⁶ ) and retention, a high switching speed of 100 ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm^-2 ). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT-based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide-based EGT devices for energy-efficient neuromorphic computing to support edge application.