Artificial sensory neurons and their applications

Journal of Semiconductors, Journal Year: 2025, Volume and Issue: 46(1), P. 011606 - 011606

Published: Jan. 1, 2025

Abstract With the rapid development of artificial intelligence (AI) technology, demand for high-performance and energy-efficient computing is increasingly growing. The limitations traditional von Neumann architecture have prompted researchers to explore neuromorphic as a solution. Neuromorphic mimics working principles human brain, characterized by high efficiency, low energy consumption, strong fault tolerance, providing hardware foundation new generation AI technology. Artificial neurons synapses are two core components systems. perception crucial aspect computing, where sensory play an irreplaceable role thus becoming frontier hot topic research. This work reviews recent advances in their applications. First, biological briefly described. Then, different types neurons, such transistor memristive discussed detail, focusing on device structures mechanisms. Next, research progress applications systems systematically elaborated, covering various types, including vision, touch, hearing, taste, smell. Finally, challenges faced at both system levels summarized.

Language: Английский

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Demonstration of cognitive learning, associative learning, and multi-bit reservoir computing using TiOx/HfOx-based volatile memristor with low current

Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 178897 - 178897

Published: Jan. 1, 2025

Language: Английский

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Deep-UV-photo-excited synaptic Ga₂O₃ nano-device with low-energy consumption for neuromorphic computing

Journal of Semiconductors, Journal Year: 2025, Volume and Issue: 46(2), P. 022401 - 022401

Published: Feb. 1, 2025

Abstract Synaptic nano-devices have powerful capabilities in logic, memory and learning, making them essential components for constructing brain-like neuromorphic computing systems. Here, we successfully developed demonstrated a synaptic nano-device based on Ga 2 O 3 nanowires with low energy consumption. Under 255 nm light stimulation, the biomimetic can stimulate various functionalities of biological synapses, including pulse facilitation, peak time-dependent plasticity learning ability. It is found that artificial device achieve an excellent "learning−forgetting−relearning" functionality. The transition from short-term to long-term retention level after stepwise attribute great relearning functionality nanowires. Furthermore, consumption be lower than 2.39 × 10 ‒11 J event. Moreover, our demonstrates exceptional stability stimulation storage. In application neural morphological computation, accuracy digit recognition exceeds 90% 12 training sessions, indicating strong capability cognitive system composed this nano-device. Therefore, work paves effective way advancing hardware-based computation intelligence systems requiring power

Language: Английский

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Research progress and applications of optoelectronic synaptic devices based on 2D materials

Brain‐X, Journal Year: 2024, Volume and Issue: 2(3)

Published: Sept. 1, 2024

Abstract In the natural world, human brain is most powerful information processor, using a highly parallel, efficient, fault‐tolerant, and reconfigurable neural network. Taking inspiration from this impressive architecture, optoelectronic synaptic devices have gained considerable attention for their ability to process retain data simultaneously, making them essential components in upcoming era of neuromorphic computing systems. recent years, significant progress has been made development two‐dimensional (2D) material heterostructures. This review focuses on use 2D materials creating devices. It discusses utilizing heterostructures these examines potential different areas such as image recognition, wearable electronics, logical operations, Heterostructures with provide wide range possibilities electronic band structures can be easily tailored achieve effective optical electrical modulation. Optoelectronic based simultaneously exhibit two functionalities: detection memory. Furthermore, strong interatomic bonding within layers possess thickness only one atomic layer, giving exceptional flexibility, transparency, mechanical strength. By solution processing ultra‐thin profile, manufacturing three‐terminal synapses becomes cost‐effective, simplifying integration processes.

Language: Английский

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InGaZnO-based photoelectric synaptic devices for neuromorphic computing

Journal of Semiconductors, Journal Year: 2024, Volume and Issue: 45(9), P. 092402 - 092402

Published: Sept. 1, 2024

Abstract Photoelectric synaptic devices could emulate behaviors utilizing photoelectric effects and offer promising prospects with their high-speed operation low crosstalk. In this study, we introduced a novel InGaZnO-based memristor. Under both electrical optical stimulation, the device successfully emulated characteristics including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation (LTP), depression (LTD). Furthermore, demonstrated practical application of our through recognition handwritten digits. The have shown ability to modulate weights effectively light pulse resulting in accuracy up 93.4%. results illustrated potential IGZO-based memristors neuromorphic computing, particularly simulate functionalities contribute image tasks.

Language: Английский

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Integration of AI with artificial sensory systems for multidimensional intelligent augmentation

International Journal of Extreme Manufacturing, Journal Year: 2025, Volume and Issue: 7(4), P. 042002 - 042002

Published: March 27, 2025

Abstract Artificial sensory systems mimic the five human senses to facilitate data interaction between real and virtual worlds. Accurate analysis is crucial for converting external stimuli from each artificial sense into user-relevant information, yet conventional signal processing methods struggle with massive scale, noise, characteristics of generated by devices. Integrating intelligence (AI) essential addressing these challenges enhancing performance systems, making it a rapidly growing area research in recent years. However, no studies have systematically categorized output functions or analyzed associated AI algorithms methods. In this review, we present systematic overview latest techniques aimed at cognitive capabilities replicating senses: touch, taste, vision, smell, hearing. We categorize AI-enabled four key areas: simulation, perceptual enhancement, adaptive adjustment, early warning. introduce specialized raw function, designed enhance optimize sensing performance. Finally, offer perspective on future AI-integrated highlighting technical potential real-world application scenarios further innovation. Integration will enable advanced multimodal perception, real-time learning, predictive capabilities. This drive precise environmental adaptation personalized feedback, ultimately positioning as foundational technologies smart healthcare, agriculture, automation.

Language: Английский

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Advanced memristive architectures based on nanomaterials for biomedical applications: a mini review

Frontiers in Nanotechnology, Journal Year: 2025, Volume and Issue: 7

Published: April 23, 2025

In recent years, the interest of science in big data sensing, storage and processing has been growing fast. Nano-materials have widely used resistive switching devices thanks to their distinguished properties. Furthermore, they provide nano-scale dimensions compatibility with fabrication procedures complementary metal oxide semiconductor (CMOS) technology. can also enhance performance memristive structures. The operation a memristor, which enables efficient characterized by fast response, increased density, low power requirements, depends largely on nano-materials deposition techniques. Herein, comprehensive brief review nano-material RRAM arrays application biomedical is discussed. First, we introduce planar array Second, report different nanomaterial categories that be random-access memories (RRAMs). Then, focus integration 3D nano-material-based crossbars for in-memory computing biosensing discuss representative applications. exploration development enhanced architectures signal integrity, great speed, ultra-high sensitivity towards thermally electrically stable platforms.

Language: Английский

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An artificial visual perception system based on ZnO threshold switching neurons with integrated rate and time-to-first-spike coding

Journal of Materials Chemistry C, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

An artificial visual system is developed based on ZnO TS neurons, featuring excellent device performance and stable neuron circuit operation. The utilizes rate-time fusion coding strategies to enable efficient accurate recognition.

Language: Английский

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Multifunctional Organic Materials, Devices, and Mechanisms for Neuroscience, Neuromorphic Computing, and Bioelectronics

Nano-Micro Letters, Journal Year: 2025, Volume and Issue: 17(1)

Published: May 8, 2025

Abstract Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks. Recent advancements large crossbar arrays and silicon-based asynchronous spiking neural networks have led promising neuromorphic systems. However, developing compact parallel for integrating artificial into hardware remains a challenge. Organic computational materials offer affordable, biocompatible devices with exceptional adjustability energy-efficient switching. Here, review investigates made development organic devices. This explores resistive switching mechanisms such as interface-regulated filament growth, molecular-electronic dynamics, nanowire-confined vacancy-assisted ion migration, while proposing methodologies enhance state retention conductance adjustment. The survey examines challenges faced implementing low-power computing, e.g., reducing device size improving time. analyses these adjustable, flexible, consumption applications, viz. biohybrid circuits interacting biological systems, systems that respond specific events, robotics, intelligent agents, bioelectronics, neuroscience, other prospects this technology.

Language: Английский

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Far-gate synaptic transistors utilizing ion-charge dual-transfer mechanism for neurotransmitter-multiplexing temporal coding

Applied Physics Letters, Journal Year: 2024, Volume and Issue: 124(16)

Published: April 15, 2024

The ability of artificial synapses to replicate multiplexed-transmission is a significant advancement in emulating complex brain activities. However, it generally required more stringent material requirements intrinsic-ambipolarity and structures P/N dual-channel. Here, we proposed far-gate synaptic transistor (FGST) just using single-channel composed common unipolar semiconductor emulate the cooperation competition between two excitatory neurotransmitters. FGST exhibits unique ion-charge dual-transfer mechanism, enabling distinct behavioral regulation modes with switchable plasticity: ion-dominant potentiation-depression short-term plasticity hole-dominant potentiation enhanced memory. Moreover, dual-excitatory enhancement can be used for temporal contrast encoding, dividing currents into multiple memory states based on fixed threshold; by comparing variations postsynaptic different thresholds, offers method further expanding number device. This work step toward constructing multifunctional intelligent systems.

Language: Английский

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