Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 16(1)

Published: Nov. 13, 2023

The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in intelligentialize human society. As an essential component that bridges physical world and digital signals, flexible sensors are evolving from a single sensing element to smarter system, which is capable highly efficient acquisition, analysis, even perception vast, multifaceted data. While challenging manual perspective, development intelligent been remarkably facilitated owing rapid advances brain-inspired AI innovations both algorithm (machine learning) framework (artificial synapses) level. This review presents progress emerging AI-driven, systems. basic concept machine learning synapses introduced. new enabling features induced by fusion comprehensively reviewed, significantly applications such as sensory systems, soft/humanoid robotics, activity monitoring. two most profound twenty-first century, deep incorporation technology holds tremendous potential for creating beings.

Language: Английский

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Artificial Neuron Devices

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(23), P. 13796 - 13865

Published: Nov. 17, 2023

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on artificial synapses, bioinspired sensors, actuators, are meticulously engineered mimic the systems. However, this field is its infancy, requiring substantial groundwork achieve autonomous with intelligent feedback, adaptability, tangible problem-solving capabilities. This review provides comprehensive overview recent devices. It starts fundamental principles synaptic explores sensory systems, integrating synapses sensors replicate all five senses. A systematic presentation nervous follows, designed emulate system functions. The also discusses potential applications outlines existing challenges, offering insights into future prospects. We aim for illuminate burgeoning inspiring further innovation captivating area research.

Language: Английский

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Memristor-Based Artificial Chips

ACS Nano, Journal Year: 2023, Volume and Issue: 18(1), P. 14 - 27

Published: Dec. 28, 2023

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled a physically integrated core processing unit (CPU) and memory even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing show efficient ability parallel computation high information storage. These advantages position them as potential candidates for future data-centric requirements add remarkable vigor to research next-generation artificial intelligence (AI) systems, particularly those involve brain-like applications. This work provides an overview evolution memristor-based devices, from their initial use in creating synapses neural networks application developing advanced AI systems chips. It offers broad perspective key device primitives enabling special applications view materials, nanostructure, mechanism models. We highlight these demonstrations have field AI, point out existing challenges nanodevices toward chips, propose guiding principle outlook promotion system optimization biomedical field.

Language: Английский

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Two‐Dimensional Memtransistors for Non‐Von Neumann Computing: Progress and Challenges

Advanced Functional Materials, Journal Year: 2023, Volume and Issue: 34(15)

Published: Oct. 22, 2023

Abstract The increased demand of high‐performance computing systems has exposed the inherent limitations current state‐of‐the‐art von Neumann architecture. Therefore, developing alternate primitives that can offer faster speed with low energy expenditure is critical. In this context, while several non‐volatile memory (NVM) devices such as synaptic transistors, spintronic devices, phase change (PCM), and memristors have been demonstrated in past, their two‐terminal nature necessitates additional peripheral elements increase area overhead. Recently, a new multiterminal device prototype known memtransistor shown tremendous potential to overcome these through exceptional control gate electrostatics enabled by 2D channel materials. perspective, brief overview recent developments 2D‐memtransistor provided, including fundamental operational mechanisms role defects enabling multiple NVM states optical photoresponse. An implementation context neuromorphic, probabilistic, information security, edge‐sensing also provided. Finally, futuristic perspective provided looking toward successful large‐scale technological integration.

Language: Английский

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Neuromorphic Nanoionics for Human–Machine Interaction: From Materials to Applications

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

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Recent Progress on Heterojunction‐Based Memristors and Artificial Synapses for Low‐Power Neural Morphological Computing

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

Published: March 19, 2025

Abstract Memristors and artificial synapses have attracted tremendous attention due to their promising potential for application in the field of neural morphological computing, but at same time, continuous optimization improvement energy consumption are also highly desirable. In recent years, it has been demonstrated that heterojunction is great significance improving memristors synapses. By optimizing material composition, interface characteristics, device structure heterojunctions, can be reduced, performance stability durability improved, providing strong support achieving low‐power computing systems. Herein, we review progress on heterojunction‐based by summarizing working mechanisms advances memristors, terms selection, design, fabrication techniques, strategies, etc. Then, applications neuromorphological deep learning introduced discussed. After that, remaining bottlenecks restricting development discussed detail. Finally, corresponding strategies overcome challenges proposed. We believe this may shed light high‐performance synapse devices.

Language: Английский

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Understanding the brain with attention: A survey of transformers in brain sciences

Brain‐X, Journal Year: 2023, Volume and Issue: 1(3)

Published: Sept. 1, 2023

Abstract Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard understanding complex brain processing mechanisms. This study aims comprehensively review discuss the applications of in sciences. First, we present a brief introduction critical architecture Transformers. Then, overview analyze most relevant sciences, including disease diagnosis, age prediction, anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, multi‐task collaboration. We organize model details open sources for reference replication. In addition, quantitative assessments, complexity, optimization Transformers, which are topics great concern field. Finally, explore possible future challenges opportunities, exploiting some concrete recent cases provoke discussion innovation. hope that this will stimulate interest further research on context

Language: Английский

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Unleashing the potential of gallium oxide: A paradigm shift in optoelectronic applications for image sensing and neuromorphic computing applications

Materials Today Physics, Journal Year: 2023, Volume and Issue: 38, P. 101279 - 101279

Published: Nov. 1, 2023

Language: Английский

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A Flexible Laser-Induced Graphene Memristor with Volatile Switching for Neuromorphic Applications

ACS Applied Materials & Interfaces, Journal Year: 2024, Volume and Issue: 16(37), P. 49724 - 49732

Published: Sept. 6, 2024

Two-dimensional graphene and graphene-based materials are attracting increasing interest in neuromorphic computing applications by the implementation of memristive architectures that enable closest solid-state equivalent to biological synapses neurons. However, state-of-the-art fabrication methodology involves routine use high-temperature processes multistepped chemical synthesis, often on a rigid substrate constraining experimental exploration field high-tech facilities. Here, we demonstrate one-step process using commercial laser fabricate laser-induced (LIG) memristors directly flexible polyimide substrate. For first time, volatile resistive switching phenomenon is reported LIG without any additional materials. The absence precursor or patterning mask greatly simplifies while reducing cost providing greater controllability. fabricated show multilevel resistance-switching characteristics with high endurance tunable timing characteristics. recovery time trigger pulse-dependent state change shown be highly suitable for its as synaptic element realization leaky-integrate fire neuron circuits.

Language: Английский

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Organic iontronic memristors for artificial synapses and bionic neuromorphic computing

Nanoscale, Journal Year: 2023, Volume and Issue: 16(4), P. 1471 - 1489

Published: Dec. 15, 2023

To tackle the current crisis of Moore's law, a sophisticated strategy entails development multistable memristors, bionic artificial synapses, logic circuits and brain-inspired neuromorphic computing. In comparison with conventional electronic systems, iontronic memristors offer greater potential for manifestation intelligence brain-machine interaction. Organic memristive materials (OIMs), which possess an organic backbone exhibit stoichiometric ionic states, have emerged as pivotal contenders realization high-performance memristors. this review, comprehensive analysis progress prospects OIMs is presented, encompassing their inherent advantages, diverse types, synthesis methodologies, wide-ranging applications in devices. Predictably, field OIMs, rapidly developing research subject, presents exciting opportunity highly efficient neuro-iontronic systems areas such in-sensor computing devices, human perception.

Language: Английский

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