Related references
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Xiao-Juan Lian et al.
Summary: Threshold switching (TS) memristors based on the Ag/MXene/GST/Pt device are proposed as low power artificial neurons, demonstrating low set voltage and current, steep slope, and large off/on ratio characteristics. The integrate and fire neuron properties essential for spiking neural networks are experimentally demonstrated. Additionally, the memristive mechanism is interpreted through first-principles calculation.
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Yingxin Chen et al.
Summary: In this article, a zero subthreshold swing and zero impact ionization FET (Z(2)-FET) is used as an innovative artificial spiking neuron with CMOS-compatible technology. The Z(2)-FET successfully emulates key biological neuronal behaviors based on a single device, including threshold-driven spiking and stimulus strength-modulated frequency response. The firing threshold of the neuron can be conveniently tuned by the gate voltage, making it suitable for spiking neural network (SNN) applications. The results suggest that Z(2)-FET has great potential for highly compact and energy-efficient artificial spiking neurons.
IEEE ELECTRON DEVICE LETTERS
(2023)
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Zechen Zhang et al.
Summary: Bio-inspired computing architecture based on artificial neurons and synapses is gaining attention for power-efficient artificial intelligence. A compact LIF neuron circuit based on an Ag/Ti/HfSe2-xOy/Pt TS device is designed to achieve bio-inspired burst firing. By adjusting oxidation conditions and embedding a thin Ti layer, device performance is significantly improved, enabling the realization of bionic bursting functions in the LIF neuron.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
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Nanoscience & Nanotechnology
Nan He et al.
Summary: In this study, the environmentally friendly copper-based Cs3Cu2Cl5 material was applied in memristors, and multifunctional memristors with both non-volatile memory and volatile threshold switching behaviors were achieved using an Ag-In-Zn-S/Cs3Cu2Cl5 heterostructure. The heterostructure memristor was able to imitate essential biological synaptic functions and achieve high recognition accuracies through artificial neural networks, demonstrating the feasibility of implementing neuromorphic computation using heterostructure memristors.
ADVANCED ELECTRONIC MATERIALS
(2023)
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Jia-He Yang et al.
Summary: This paper introduces a threshold-switching memristor for artificial neural networks, which mimics multiple biological functions of neurons and overcomes the issues in conventional memristors.
ADVANCED ELECTRONIC MATERIALS
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Article
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Zhipeng Zhong et al.
Summary: This research presents a stable threshold-switching device based on Cu/CIPS/graphene heterostructure, achieved after investigating the activation of Cu's ionic conductivity. The device exhibits exceptional threshold-switching performance, with good cycling endurance, high on/off ratio, low operation voltages, and an ultrasmall subthreshold swing. The stable resistive-switching mechanism is interpreted with a drifting and diffusion model of Cu ions under the electric field, making the layered ferroionic CIPS material a promising candidate for information storage devices.
Article
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Zhuoxin Ye et al.
Summary: In this study, low-toxic quaternary AgInZnS quantum dots were synthesized and used as novel anodic electrochemiluminescence (ECL) emitters. These QDs exhibited strong and stable ECL emission, a low excitation potential, and high ECL efficiency. An on-off-on ECL biosensor for detecting microRNA-141 was designed based on a dual isothermal enzyme-free strand displacement reaction, achieving cyclic amplification of the target and ECL signal, as well as a switch function. The ECL biosensor showed a wide linear range and low detection limit, making it a promising tool for rapid and accurate diagnosis of clinical diseases.
BIOSENSORS & BIOELECTRONICS
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Yiqun Liu et al.
Summary: Versatile memristors with switchable volatile and non-volatile operating modes were implemented in Ag/CIPS/Au devices. They exhibited diode-like volatile memristor performances with rectification ratio of 10(3) and endurance of 500 switching cycles. Significant non-volatile memory performances with on/off ratio of 10(3) and retention up to 10(4) s were also achieved, enabling their application as both selectors and memories. Moreover, these versatile memristors demonstrated the ability to emulate short-term and long-term plasticity of artificial synapses, showing their potential in neuromorphic computing applications.
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Nature Communications
(2023)
Article
Multidisciplinary Sciences
Rui Yuan et al.
Summary: The authors propose a hardware system that uses VO2 memristors to perform brain-like encoding and analysis of physiological signals, and is capable of identifying arrhythmia and epileptic seizures. The system demonstrates superior computing capabilities, achieving high accuracies in arrhythmia classification and epileptic seizure detection. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces.
NATURE COMMUNICATIONS
(2023)
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Yang Jiang et al.
Summary: This article demonstrates the experimental realization of a second-order memristor using yttria-stabilized zirconia with Ag doping, which exhibits spontaneous threshold lowering (STL) functionality and enables spatial attention. The physical origin of the second-order dynamics is uncovered, paving the way for future machine intelligence with high-efficiency and compact footprint.
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Nanoscience & Nanotechnology
Young Geun Song et al.
Summary: A highly reliable threshold switching device, based on Ag and SiO2 nanorods, that is immune to atmospheric changes has been developed. This device exhibits switching characteristics that are electroforming-free, repeatable, consistent, and resistant to changes in ambient conditions and compliance levels during operation.
ACS APPLIED MATERIALS & INTERFACES
(2023)
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Haowei Wang et al.
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IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
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Xiangyu Chen et al.
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JAPANESE JOURNAL OF APPLIED PHYSICS
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Yanting Ding et al.
Summary: Inspired by the human brain, spike-based neuromorphic systems have attracted strong research interest due to their high energy efficiency and powerful computation. This study systematically explores the spiking behavior of NbOx-based TS neurons from both extrinsic and intrinsic factors, providing insights on how circuit parameters affect the performance. Results show that external factors like input intensity and synaptic weights, as well as internal factors like threshold voltage and resistance states, all play a role in modulating the spiking frequency.
FRONTIERS IN NEUROSCIENCE
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Kangmin Leng et al.
Summary: Resistive switching memory devices, as the building block of brain-inspired computing, have attracted great interest due to their ability to mimic synapses and neurons. This study presents artificial neurons and synapses based on the Al/a-SiNxOy:H/P+-Si device, which exhibit tunable switching characteristics from threshold to memory. By controlling the compliance current, the researchers were able to induce volatile threshold switching and stable nonvolatile memory switching. The conduction mechanisms of these switches were found to agree with established models. This discovery offers a promising approach to constructing neuromorphic devices for brain-inspired computing in the era of AI.
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Nanoscience & Nanotechnology
Huiwu Mao et al.
Summary: This research introduces a stacked memristor based on IGZO as a spiking stochastic neuron, showing tunable firing probability and demonstrating eliminated switching variation and small relative deviation in stacked configuration compared to a single memristor.
ADVANCED ELECTRONIC MATERIALS
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Momo Zhao et al.
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ADVANCED ELECTRONIC MATERIALS
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P. Bousoulas et al.
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IEEE TRANSACTIONS ON ELECTRON DEVICES
(2022)
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Yaxiong Cao et al.
Summary: This article proposes a fully physically transient volatile memristor with Mg as an active electrode, demonstrating threshold switching performance and biological synapse plasticity. The device is fabricated on biodegradable and biocompatible substrates using a water-assisted transfer printing method. The study highlights the significance of the fully transient volatile memristor in security neuromorphic computing and bio-integrated electronic systems.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2022)
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Xinxin Wang et al.
Summary: This paper reports on the behavioral similarity between the complementary resistive switching (CRS) memristor and the leaky integrate-and-fire (LIF) neuron. The CRS memristor can exhibit neuronal behaviors and perform various fundamental neuronal operations.
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Jingyao Bian et al.
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IEEE ELECTRON DEVICE LETTERS
(2022)
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Liang Yao et al.
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Lingzhi Tang et al.
Summary: This study demonstrates flexible threshold switching memristors with ultrahigh switching speed and robust flexibility through a low-cost solution process. The leaky integrate-and-fire dynamics and strength-modulated spike frequency response as artificial neurons are investigated. The working mechanism of the ultrahigh switching speed and the potential cause of the robust flexibility are analyzed using a migration model and time-resolved photoluminescence.
JOURNAL OF MATERIALS CHEMISTRY C
(2022)
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Mingyi Rao et al.
Summary: This study proposes a memristor crossbar array with a timing selector to address sneak path current issues. By using silver-based diffusive memristors as timing selectors, large-dimension real-world problems can be solved effectively.
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Nanoscience & Nanotechnology
Taehyun Kim et al.
Summary: The study proposes a versatile artificial neuron based on the bipolar electrochemical metallization threshold switch, which exhibits four key features for a spiking neuron and unique characteristics for changing synaptic weight. Utilizing a filament confinement technique, the neuron achieves high on/off ratio, low threshold voltage, low variability, and endurance over 10^6 cycles, paving the way for advanced large-scale neuromorphic systems.
ADVANCED ELECTRONIC MATERIALS
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Yu Wang et al.
Summary: Memristors have attracted attention for emulating biological synapses and neurons due to their high-density integration, outstanding nonlinearity, and modulated plasticity. This study demonstrates the successful emulation of multiple neural functions by using a two-dimensional MXene(V2C) memristor to simulate a leaky integrate-and-fire neuron, without the need for auxiliary circuits. This work suggests that three-atom-type MXene memristors may provide an efficient method for constructing hardware neuromorphic computing systems.
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Nan He et al.
Summary: Electronic synaptic devices based on quaternary Ag-In-Zn-S (AIZS) quantum dots demonstrate excellent performance with low power consumption, and successfully mimic biological synaptic-like behaviors.
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