Electrolyte-gated transistors for enhanced performance bioelectronics

Nature Reviews Methods Primers, Journal Year: 2021, Volume and Issue: 1(1)

Published: Oct. 7, 2021

Electrolyte-gated transistors (EGTs), capable of transducing biological and biochemical inputs into amplified electronic signals stably operating in aqueous environments, have emerged as fundamental building blocks bioelectronics. In this Primer, the different EGT architectures are described with mechanisms underpinning their functional operation, providing insight key experiments including necessary data analysis validation. Several organic inorganic materials used structures fabrication approaches for an optimal experimental design presented compared. The bio-layers and/or biosystems integrated or interfaced to EGTs, self-organization self-assembly strategies, reviewed. Relevant promising applications discussed, two-dimensional three-dimensional cell monitoring, ultra-sensitive biosensors, electrophysiology, synaptic neuromorphic bio-interfaces, prosthetics robotics. Advantages, limitations possible optimizations also surveyed. Finally, current issues future directions further developments discussed. (EGTs) bioelectronics, which transduce electrical signals. This Primer examines mechanism operation practical considerations related wide range applications.

Language: Английский

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2D Material Based Synaptic Devices for Neuromorphic Computing

Advanced Functional Materials, Journal Year: 2020, Volume and Issue: 31(4)

Published: Oct. 7, 2020

Abstract The demand for computing power has been increasing exponentially since the emergence of artificial intelligence (AI), internet things (IoT), and machine learning (ML), where novel primitives are required. Brain inspired neuromorphic systems, capable combining analog data storage at device level, have drawn great attention recently. In addition, basic electronic devices mimicking biological synapse achieved significant progress. Owing to their atomic thickness reduced screening effect, physical properties 2D materials could be easily modulated by various stimuli, which is quite beneficial synaptic applications. this article, aiming high‐performance functional applications, a comprehensive review based on provided, including advantages heterostructures, robust multifunctional devices, associated Challenges strategies future development also discussed. This will provide an insight into design preparation applications in computing.

Language: Английский

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Neuromorphic Engineering: From Biological to Spike‐Based Hardware Nervous Systems

Advanced Materials, Journal Year: 2020, Volume and Issue: 32(52)

Published: Nov. 9, 2020

Abstract The human brain is a sophisticated, high‐performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) can rival the brain. Spiking neural networks based on neuromorphic computing platforms simulate architecture information processing of intelligent brain, providing new insights for building AIs. rapid development materials engineering, device physics, chip integration, neuroscience has led to exciting progress goal overcoming von Neumann bottleneck. Herein, fundamental knowledge related structures working principles neurons synapses biological nervous system reviewed. An overview then provided hardware systems, from spike‐based platforms. It hoped this review will shed light evolution brain‐like computing.

Language: Английский

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Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: July 7, 2020

Abstract As a key building block of biological cortex, neurons are powerful information processing units and can achieve highly complex nonlinear computations even in individual cells. Hardware implementation artificial with similar capability is great significance for the construction intelligent, neuromorphic systems. Here, we demonstrate an neuron based on NbO x volatile memristor that not only realizes traditional all-or-nothing, threshold-driven spiking spatiotemporal integration, but also enables dynamic logic including XOR function linearly separable multiplicative gain modulation among different dendritic inputs, therefore surpassing neuronal functions described by simple point model. A monolithically integrated 4 × fully memristive neural network consisting nonvolatile TaO synapses single crossbar array experimentally demonstrated, showing pattern recognition through online learning using simplified δ-rule coincidence detection, which paves way bio-inspired intelligent

Language: Английский

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All‐Optically Controlled Memristor for Optoelectronic Neuromorphic Computing

Advanced Functional Materials, Journal Year: 2020, Volume and Issue: 31(4)

Published: Nov. 20, 2020

Memristors have emerged as key candidates for beyond-von-Neumann neuromorphic or in-memory computing owing to the feasibility of their ultrahigh-density three-dimensional integration and ultralow energy consumption. A memristor is generally a two-terminal electronic element with conductance that varies nonlinearly external electric stimuli can be remembered when power turned off. As an alternative, light used tune memconductance endow combination advantages both photonics electronics. Both increases decreases in optically induced been realized different memristors; however, reversible tuning same device remains considerable challenge severely restricts development optoelectronic memristors. Here we describe all-optically controlled (AOC) analog reversibly tunable over continuous range by varying only wavelength controlling light. Our based on relatively mature semiconductor material InGaZnO (IGZO) mechanism light-induced electron trapping detrapping. We demonstrate spike-timing-dependent plasticity (STDP) learning our device, indicating its potential applications AOC spiking neural networks (SNNs) highly efficient computing.

Language: Английский

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Optoelectronic Synaptic Devices for Neuromorphic Computing

Advanced Intelligent Systems, Journal Year: 2020, Volume and Issue: 3(1)

Published: Nov. 6, 2020

Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream because it excels at self‐adaptive learning and highly parallel consumes much less energy. Synaptic devices that mimic biological synapses are critical building blocks for neuromorphic computing. Inspired by recent progress in optogenetics visual sensing, light has been increasingly incorporated into synaptic devices. This paves way to optoelectronic with a series advantages such as wide bandwidth, negligible resistance–capacitance (RC) delay power loss, global regulation multiple Herein, basic functionalities introduced. All kinds then discussed categorizing them optically stimulated devices, assisted optical output. Existing practical scenarios application also presented. Finally, perspectives on development future outlined.

Language: Английский

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Advances in the Application of Perovskite Materials

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 15(1)

Published: July 10, 2023

Nowadays, the soar of photovoltaic performance perovskite solar cells has set off a fever in study metal halide materials. The excellent optoelectronic properties and defect tolerance feature allow to be employed wide variety applications. This article provides holistic review over current progress future prospects materials representative promising applications, including traditional devices (solar cells, light-emitting diodes, photodetectors, lasers), cutting-edge technologies terms neuromorphic (artificial synapses memristors) pressure-induced emission. highlights fundamentals, remaining challenges for each application, aiming provide comprehensive overview development status navigation research devices.

Language: Английский

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Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

Advanced Functional Materials, Journal Year: 2020, Volume and Issue: 30(50)

Published: Sept. 11, 2020

Abstract Traditional von Neumann computing architecture with separated computation and storage units has already impeded the data processing performance energy efficiency, calling for emerging neuromorphic electronic optical devices systems which can mimic human brain to shift this paradigm. Material‐level innovation become key component revolution of information technology. Chalcogenide phase‐change material (PCM) as a well‐acknowledged data‐storage medium is promising candidate tackle challenge. In review, use PCMs implement artificial neurons synapses from both respects discussed, in particular, structure–property physics transition dynamics that enable such brain‐inspired in‐memory applications are emphasized. Recent advances on atomic‐level amorphous crystalline structures, mechanisms, materials optimization design, neural synaptic devices, chips, systems, well future opportunities PCMs, summarized discussed.

Language: Английский

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Flexible Artificial Sensory Systems Based on Neuromorphic Devices

ACS Nano, Journal Year: 2021, Volume and Issue: 15(3), P. 3875 - 3899

Published: Jan. 28, 2021

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of synaptic devices and sensing elements to mimic complicated in biological is prerequisite the realization. To realize high-efficiency systems, development synapses consumption high-density integration essential. Furthermore, realization efficient coupling between element device crucial. This Review presents recent progress area systems. We focus on both advances synapses, including structures, mechanisms, functions, design intelligent, perception based devices. Additionally, key challenges opportunities related are examined, potential solutions suggestions provided.

Language: Английский

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Anomalous resistive switching in memristors based on two-dimensional palladium diselenide using heterophase grain boundaries

Nature Electronics, Journal Year: 2021, Volume and Issue: 4(5), P. 348 - 356

Published: May 17, 2021

Language: Английский

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