Room-temperature solution-phase graphoepitaxial growth of in-plane nanowire arrays on flexible films for bendable synaptic devices DOI

W. Mao,

Zhanhao Liang,

Shubin Yi

et al.

Applied Physics Letters, Journal Year: 2025, Volume and Issue: 126(18)

Published: May 5, 2025

Recent advancements in artificial intelligence have spurred growing interest developing innovative architectures for synapses. Among these, nanowires emerged as promising candidates creating lightweight, flexible, and energy-efficient However, achieving in-plane aligned growth of on flexible substrates poses a substantial challenge their integration into bendable This study introduces room-temperature solution-phase graphoepitaxial technique that facilitates the along hydrophilic nanogrooves polyvinyl alcohol films. scalable method obviates need complex vacuum systems bypasses constraints traditional lattice-matching epitaxy by leveraging surface topography to guide nanowire alignment. Devices incorporating tri-isopropylsilylethynyl pentacene exhibit wavelength-sensitive photoresponse mimic fundamental biological synaptic behaviors, including paired pulse facilitation spike-number-dependent plasticity. Furthermore, these devices demonstrate exceptional bending stability, maintaining consistent response even after 2000 bends at curvature radius 0.4 cm. The approach's versatility is further highlighted its applicability diverse organic arrays. By seamlessly integrating without requiring post-growth transfer assembly, this approach simplifies fabrication processes improves device durability. underscores transformative potential efficient strategy advancing conformable nanowire-based technologies.

Language: Английский

Recent trends in neuromorphic systems for non-von Neumann in materia computing and cognitive functionalities DOI
Indrajit Mondal, Rohit Attri, Tejaswini S. Rao

et al.

Applied Physics Reviews, Journal Year: 2024, Volume and Issue: 11(4)

Published: Oct. 1, 2024

In the era of artificial intelligence and smart automated systems, quest for efficient data processing has driven exploration into neuromorphic aiming to replicate brain functionality complex cognitive actions. This review assesses, based on recent literature, challenges progress in developing basic focusing “material-neuron” concepts, that integrate structural similarities, analog memory, retention, Hebbian learning brain, contrasting with conventional von Neumann architecture spiking circuits. We categorize these devices filamentary non-filamentary types, highlighting their ability mimic synaptic plasticity through external stimuli manipulation. Additionally, we emphasize importance heterogeneous neural content support conductance linearity, plasticity, volatility, enabling effective storage various types information. Our comprehensive approach categorizes fundamentally different under a generalized pattern dictated by driving parameters, namely, pulse number, amplitude, duration, interval, as well current compliance employed contain conducting pathways. also discuss hybridization protocols fabricating systems making use existing complementary metal oxide semiconductor technologies being practiced silicon foundries, which perhaps ensures smooth translation user interfacing new generation devices. The concludes outlining insights challenges, future directions realizing deployable field intelligence.

Language: Английский

Citations

5
Electrochemical rewiring through quantum conductance effects in single metallic memristive nanowires DOI Creative Commons
Gianluca Milano, Federico Raffone, Katarzyna Bejtka

et al.

Nanoscale Horizons, Journal Year: 2024, Volume and Issue: 9(3), P. 416 - 426

Published: Jan. 1, 2024

In this work, Milano et al. reported on quantum conductance effects in memristive nanowires, unveiling the origin of deviations levels from integer multiples and analyzing fluctuations over time devices.

Language: Английский

Citations

4
Advancements in Nanowire-Based Devices for Neuromorphic Computing: A Review DOI
Jiawen Qiu, Junlong Li, Wenhao Li

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(46), P. 31632 - 31659

Published: Nov. 5, 2024

Neuromorphic computing, inspired by the highly interconnected and energy-efficient way human brain processes information, has emerged as a promising technology for post-Moore's law era. This emerging can emulate structures functions of is expected to overcome fundamental limitation current von Neumann computing architecture. devices stand out key components future electronic systems, exhibiting potential in shaping landscape neuromorphic computing. Especially, nanowire (NW)-based devices, with their advantages high integration, high-speed low power consumption, have recently candidates technology. Here, critical overview development relevant research field NW-based are provided. based on different NW materials comprehensively discussed, including Ag NW-based, organic metal oxide semiconductor devices. Finally, foresight perspective, potentials challenges these use brain-like electronics discussed.

Language: Английский

Citations

4
A Comprehensive Taxonomy of Machine Consciousness DOI
Ruilin Qin, Changle Zhou, Mengjie He

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: 119, P. 102994 - 102994

Published: Jan. 31, 2025

Language: Английский

Citations

0
Charge Transport and Noise in PVP-Coated Silver Nanowire Networks: Implications for Neuromorphic Applications DOI

Charu Singh,

Nirat Ray

ACS Applied Nano Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Language: Английский

Citations

0
Effectiveness of Computer-Based Cognitive Rehabilitation on Academic Engagement in Students with Working Memory Impairment: A Quasi-Experimental Study DOI

Neda Nazarboland,

Mina Zandieh,

Saeid Sadeghi

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
A simulated memristor architecture of neural networks of human memory DOI Creative Commons

Tihomir Taskov,

Juliana Dushanova

Brain Organoid and Systems Neuroscience Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Ephaptic Coupling in Ultralow‐Power Ion‐Gel Nanofiber Artificial Synapses for Enhanced Working Memory DOI Open Access

Yuanxia Chen,

J. Xia, Youzhi Qu

et al.

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

Neuromorphic devices are designed to replicate the energy-efficient information processing advantages found in biological neural networks by emulating working mechanisms of neurons and synapses. However, most existing neuromorphic focus primarily on functionally mimicking synapses, with insufficient emphasis ion transport mechanisms. This limitation makes it challenging achieve complexity connectivity inherent systems, such as ephaptic coupling. Here, an ionic biomimetic synaptic device based a flexible ion-gel nanofiber network is proposed, which transmits enables coupling through capacitance formation extremely low energy consumption just 6 femtojoules. The hysteretic behavior endows synaptic-like memory effects, significantly enhancing performance reservoir computing system for classifying MNIST handwritten digit dataset demonstrating high efficiency edge learning. More importantly, array establish communication connections, exhibiting global oscillatory behaviors similar networks. perform tasks, paving way developing brain-like systems characterized vast connectivity.

Language: Английский

Citations

0
Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms DOI Creative Commons
Frank Barrows,

Jonathan Lin,

Francesco Caravelli

et al.

Advanced Intelligent Systems, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

Neuromorphic computing has the potential to revolutionize future technologies and our understanding of intelligence, yet it remains challenging realize in practice. The learning‐from‐mistakes algorithm, inspired by brain's simple learning rules inhibition pruning, is one few brain‐like training methods. This algorithm implemented neuromorphic memristive hardware through a codesign process that evaluates essential trade‐offs. While effectively trains small networks as binary classifiers perceptrons, performance declines significantly with increasing network size unless tailored algorithm. work investigates trade‐offs between depth, controllability, capacity—the number learnable patterns—in hardware. highlights importance topology governing equations, providing theoretical tools evaluate device's computational capacity based on its measurements circuit structure. findings show breaking neural symmetry enhances both controllability capacity. Additionally, pruning circuit, algorithms all‐memristive circuits can utilize stochastic resources create local contrasts weights. Through combined experimental simulation efforts, parameters are identified enable exhibit emergent intelligence from rules, advancing computing.

Language: Английский

Citations

0
Self-organizing neuromorphic nanowire networks as stochastic dynamical systems DOI Creative Commons
Gianluca Milano, Fabio Michieletti,

Davide Pilati

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 13, 2025

Abstract Neuromorphic computing aims to develop hardware platforms that emulate the effectiveness of our brain. In this context, brain-inspired self-organizing memristive networks have been demonstrated as promising physical substrates for in materia computing. However, understanding connection between network dynamics and information processing capabilities these systems still represents a challenge. work, we show neuromorphic nanowire behavior can be modeled an Ornstein-Uhlenbeck process which holistically combines stimuli-dependent deterministic trajectories stochastic effects. This unified modeling framework, able describe main features including noise jumps, enables investigation quantification roles played by on system context reservoir These results pave way development paradigms exploiting same platform similar what brain does.

Language: Английский

Citations

0