Compact Artificial Synapse-Neuron Module with Chemically Mediated Spiking Behaviors DOI
Jie Qiu, Pei Chen, Ming Wang

et al.

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

Published: March 20, 2025

Neuromorphic electronic devices mimicking the structure and functionality of biological counterparts have shown promising applications in biorealistic computing bioelectronic interfaces. However, current neuromorphic systems comprising synapses neurons typically exhibit complex integrated structures lack chemically mediated characteristics, hindering them from direct biointerfacing. Here, we report a compact artificial synapse-neuron module (ASNM) by seamlessly integrating an organic electrochemical synaptic transistor niobium dioxide Mott memristor, showing plasticity highly stable spiking characteristics (>1010 cycles). Sodium ions dopamine neurotransmitter induce short-term long-term transistors, respectively, thus enabling temporary modulation ASNM's firing frequency bioplausible range (0–100 Hz). Furthermore, construct neuromuscular system based on ASNM, which could replicate learning processes shooting basketball. These results demonstrate that our ASNM achieve multiple functionalities including sensing, plasticity, structure, providing way for

Language: Английский

From fundamentals to frontiers: a review of memristor mechanisms, modeling and emerging applications DOI
Parth Thakkar, Jeny Gosai, Himangshu Jyoti Gogoi

et al.

Journal of Materials Chemistry C, Journal Year: 2024, Volume and Issue: 12(5), P. 1583 - 1608

Published: Jan. 1, 2024

The escalating demand for artificial intelligence (AI), the internet of things (IoTs), and energy-efficient high-volume data processing has brought need innovative solutions to forefront.

Language: Английский

Citations

20
Neuromorphic Nanoionics for Human–Machine Interaction: From Materials to Applications DOI
Xuerong Liu,

Cui Sun,

Xiaoyu Ye

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(37)

Published: Feb. 29, 2024

Abstract Human–machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion extended into various emerging domains, including human healthcare, machine perception, biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted nanoionic devices that emulate operations architecture of brain, emerged as powerful tool highly efficient information processing. This paper delivers comprehensive review developments device‐based neuromorphic computing technologies their pivotal role shaping next‐generation HMI. Through detailed examination fundamental mechanisms behaviors, explores ability memristors ion‐gated transistors to intricate functions neurons synapses. Crucial performance metrics, such reliability, energy efficiency, flexibility, biocompatibility, are rigorously evaluated. Potential applications, challenges, opportunities using HMI technologies, discussed outlooked, shedding light on fusion with

Language: Английский

Citations

17
Synaptic and neural behaviours in a standard silicon transistor DOI Creative Commons
Sebastián Pazos, Kaichen Zhu, Marco A. Villena

et al.

Nature, Journal Year: 2025, Volume and Issue: unknown

Published: March 26, 2025

Abstract Hardware implementations of artificial neural networks (ANNs)—the most advanced which are made millions electronic neurons interconnected by hundreds synapses—have achieved higher energy efficiency than classical computers in some small-scale data-intensive computing tasks 1 . State-of-the-art neuromorphic computers, such as Intel’s Loihi 2 or IBM’s NorthPole 3 , implement ANNs using bio-inspired neuron- and synapse-mimicking circuits complementary metal–oxide–semiconductor (CMOS) transistors, at least 18 per neuron six synapse. Simplifying the structure size these two building blocks would enable construction more sophisticated, larger energy-efficient ANNs. Here we show that a single CMOS transistor can exhibit synaptic behaviours if biased specific (unconventional) manner. By connecting one additional series, build versatile 2-transistor-cell exhibits adjustable neuro-synaptic response (which named random access memory cell, NS-RAM cell). This performance comes with yield 100% an ultra-low device-to-device variability, owing to maturity silicon platform used—no materials devices alien process required. These results represent short-term solution for implementation efficient opportunity terms circuit design optimization intelligence applications.

Language: Английский

Citations

2
Pulse electrochemical synaptic transistor for supersensitive and ultrafast biosensors DOI Creative Commons
Jianlong Ji, Zhenxing Wang, Fan Zhang

et al.

InfoMat, Journal Year: 2023, Volume and Issue: 5(11)

Published: Aug. 16, 2023

Abstract High sensitivity and fast response are the figures of merit for benchmarking commercial sensors. Due to advantages intrinsic signal amplification, bionic ability, mechanical flexibility, electrochemical transistors (ECTs) have recently gained increasing popularity in constructing various In current work, we proposed a pulse‐driven synaptic ECT supersensitive ultrafast biosensors. By pulsing presynaptic input (drain bias, V D ) setting modulation potential (gate bias) near transconductance intersection ( G,i ), ECT‐based pH sensor can achieve record high up 124 mV −1 (almost twice Nernstian limit, 59.2 an time as low 8.75 ms (7169 times faster than potentiostatic sensors, 62.73 s). The sensing strategy effectively eliminate fluctuation issue during calibration process significantly reduce power consumption. Besides, most sensitive working point at has been elaborately figured out through series detailed mathematical derivations, which is great significance provide higher with quasi‐nonfluctuating amplification capability. transistor paired optimized operating gate offers new paradigm standardizing commercializing high‐performance image

Language: Английский

Citations

41
Bio‐Inspired Sensory Receptors for Artificial‐Intelligence Perception DOI
Atanu Bag, Gargi Ghosh,

M. Junaid Sultan

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: unknown

Published: May 3, 2024

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired receptors with synaptic plasticity have recently gained significant attention developing energy-efficient AI Various their applications perception are reviewed here. The critical challenges for future development outlined, emphasizing need innovative solutions to overcome hurdles sensor design, integration, scalability. can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, assistive technologies. As advancements sensing continue accelerate, promise creating more intelligent adaptive systems becomes increasingly attainable, marking step forward evolution human-like

Language: Английский

Citations

15
Recent developments in the state-of-the-art optoelectronic synaptic devices based on 2D materials: a review DOI
Rajesh Jana, Sagnik Ghosh, Ritamay Bhunia

et al.

Journal of Materials Chemistry C, Journal Year: 2024, Volume and Issue: 12(15), P. 5299 - 5338

Published: Jan. 1, 2024

This review showcases the diverse functionalities of 2D materials and state-of-the-art developments in device structures, working principles, design strategies materials, integration material-based optoelectronic synaptic devices.

Language: Английский

Citations

10
Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching DOI

Dongyeol Ju,

Jungwoo Lee, Sungjun Kim

et al.

Advanced Materials Technologies, Journal Year: 2024, Volume and Issue: unknown

Published: May 19, 2024

Abstract In the era of big data, traditional computing architectures face limitations in handling vast amounts data owing to separate processing and memory units, thus causing bottlenecks high‐energy consumption. Inspired by human brain's information exchange mechanism, neuromorphic offers a promising solution. Resistive random access devices, particularly those with bilayer structures like Pt/TaO x /TiO /TiN, show potential for their simple design, low‐power consumption, compatibility existing technology. This study investigates synaptic applications /TiN devices computing. The unique coexistence nonfilamentary filamentary switching device enables realization reservoir functions artificial nociceptors synapses. Additionally, linkage between synapses is examined based on injury‐enhanced spike‐time‐dependent plasticity paradigms. underscores device's computing, providing framework simulating nociceptors, synapses, learning principles.

Language: Английский

Citations

9
Versatile NbOx‐Based Volatile Memristor for Artificial Intelligent Applications DOI Open Access

Dongyeol Ju,

Sungjun Kim

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

Abstract To achieve cost‐effectiveness, researchers are exploring various memristors for their adaptation in neuromorphic computing. Recent studies have focused on developing versatile functioning singular memristors, such as those involved on‐receptor computing, which integrates sensory functions into current computing paradigms. Additionally, adaptations like reservoir being investigated systems. In this study, a memristor composed of stack Ti/NbO x /Pt layers is fabricated to explore multifunctional behaviors within single memristor. By applying bias toward the top Ti electrode, gradual changes with volatile features demonstrated, revealing an ion‐migration‐based nonfilamentary switching Leveraging functionality, artificial nociceptor first implemented, demonstrating key biological nociceptors including thresholding, relaxation, no‐adaptation, and sensitization. Subsequently, synapse emulation akin brain achieved through easy conductance potentiation depression diverse functions, enabling mimic learning activities spike firing. Lastly, computational applications explored by adapting edge multi‐bit expanding memristor's across fields behaviors.

Language: Английский

Citations

9
Exploring photosensitive nanomaterials and optoelectronic synapses for neuromorphic artificial vision DOI

Hyun-Haeng Lee,

Jun-Seok Ro,

Kwan‐Nyeong Kim

et al.

Current Opinion in Solid State and Materials Science, Journal Year: 2025, Volume and Issue: 35, P. 101215 - 101215

Published: Feb. 7, 2025

Language: Английский

Citations

1
Inkjet‐Printed Tungsten Oxide Memristor Displaying Non‐Volatile Memory and Neuromorphic Properties DOI Creative Commons
Hongrong Hu, Alexander Scholz, Christian Dölle

et al.

Advanced Functional Materials, Journal Year: 2023, Volume and Issue: 34(20)

Published: July 7, 2023

Abstract Printed electronics including large‐area sensing, wearables, and bioelectronic systems are often limited to simple circuits hence it remains a major challenge efficiently store data perform computational tasks. Memristors can be considered as ideal candidates for both purposes. Herein, an inkjet‐printed memristor is demonstrated, which serve digital information storage device, or artificial synapse neuromorphic circuits. This achieved by suitable manipulation of the ion species in active layer device. For digital‐type operation resistive switching dominated cation movement after initial electroforming step. It allows device utilized non‐volatile memristor, offers high endurance over 12 672 cycles uniformity at low operating voltages. To use electroforming‐free, interface‐based, analog‐type anion migration exploited leads volatile switching. An important figure merits such short‐term plasticity with close biological timescales facilitation (10–177 ms), augmentation (10s), potentiation (35 s). Furthermore, thoroughly studied regarding its metaplasticity memory formation. Last but not least, shows non‐linear signal integration low‐frequency filtering capabilities, renders good candidate computing architectures, reservoir computing.

Language: Английский

Citations

20