Interface Charge Engineering in Ferroelectric Neuristors for a Complete Machine Vision System DOI
Qinyong Dai,

Mengjiao Pei,

Jianhang Guo

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: unknown, P. 12068 - 12075

Published: Nov. 26, 2024

The rapid advancement of artificial intelligence has driven the demand for hardware solutions neuromorphic pathways to effectively mimic biological functions human visual system. However, current machine vision systems (MVSs) fail fully replicate retinal and lack ability update weights through all-optical pulses. Here, by employing rational interface charge engineering via varying trapping layer thickness PMMA, we determine that ferroelectric polarization our neuristors can be flexibly manipulated light or electrical This capability enables dynamic modulation device's optoelectronic characteristics, facilitating a complete MVS. As front-end sensors, devices with thickest PMMA (∼32 nm) demonstrate autonomous adaptation while those thinnest (∼2 exhibit bidirectional photoresponse characteristics akin bipolar cells. Furthermore, as components back-end processor, conductances these moderate (∼12 updated linearly Our MVS, constructed neuristors, achieved an impressive recognition accuracy 93% in handwritten digit tasks under extreme lighting conditions. work offers effective strategy development energy-efficient highly integrated intelligent MVSs.

Language: Английский

Hemispherical Retina Emulated by Plasmonic Optoelectronic Memristors with All‐Optical Modulation for Neuromorphic Stereo Vision DOI Creative Commons
Xuanyu Shan, Zhongqiang Wang, Jun Xie

et al.

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

Published: July 25, 2024

Binocular stereo vision relies on imaging disparity between two hemispherical retinas, which is essential to acquire image information in three dimensional environment. Therefore, retinomorphic electronics with structural and functional similarities biological eyes are always highly desired develop perception system. In this work, a optoelectronic memristor array based Ag-TiO

Language: Английский

Citations

18
Temperature‐Resilient Polymeric Memristors for Effective Deblurring in Static and Dynamic Imaging DOI Creative Commons
Ziyu Lv,

Minghao Jiang,

Huiying Liu

et al.

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

Published: Jan. 24, 2025

Abstract Organic memristors have emerged as promising candidates for neuromorphic computing due to their potential low‐cost fabrication, large‐scale integration, and biomimetic functionality. However, practical applications are often hindered by limited thermal stability device‐to‐device variability. Here, an organic polymer‐based memristor using a thiadiazolobenzotriazole (TBZ) 2,5‐Dioctyl‐3,6‐di(thiophen‐2‐yl)pyrrolo[3,4‐c]pyrrole‐1,4(2H,5H)‐dione (DPP)‐based conjugated polymer is presented that exhibits exceptional reliable resistance switching behavior over wide temperature range (153–573 K). The device leverages charge‐transfer mechanism achieve gradual uniform switching, overcoming the challenges associated with filamentary‐based mechanisms. memristor's consistent performance enable its integration into various applications, including image processing. device's ability demonstrated effectively deblur images, even under varying conditions, showcasing robust computing. This study establishes pathway toward high‐performance, thermally stable advanced artificial intelligence applications.

Language: Английский

Citations

2
Synaptic devices based on silicon carbide for neuromorphic computing DOI

B.J. Ye,

Xiao Liu, Chao Wu

et al.

Journal of Semiconductors, Journal Year: 2025, Volume and Issue: 46(2), P. 021403 - 021403

Published: Feb. 1, 2025

Abstract To address the increasing demand for massive data storage and processing, brain-inspired neuromorphic computing systems based on artificial synaptic devices have been actively developed in recent years. Among various materials investigated fabrication of devices, silicon carbide (SiC) has emerged as a preferred choices due to its high electron mobility, superior thermal conductivity, excellent stability, which exhibits promising potential applications harsh environments. In this review, progress SiC-based is summarized. Firstly, an in-depth discussion conducted regarding categories, working mechanisms, structural designs these devices. Subsequently, several application scenarios are presented. Finally, few perspectives directions their future development outlined.

Language: Английский

Citations

2
Strategic Development of Memristors for Neuromorphic Systems: Low‐Power and Reconfigurable Operation DOI Open Access
Jang Woo Lee, Jiye Han, Boseok Kang

et al.

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

Published: March 25, 2025

The ongoing global energy crisis has heightened the demand for low-power electronic devices, driving interest in neuromorphic computing inspired by parallel processing of human brains and efficiency. Reconfigurable memristors, which integrate both volatile non-volatile behaviors within a single unit, offer powerful solution in-memory computing, addressing von Neumann bottleneck that limits conventional architectures. These versatile devices combine high density, low power consumption, adaptability positioning them as superior alternatives to traditional complementary metal-oxide-semiconductor (CMOS) technology emulating brain-like functions. Despite their potential, studies on reconfigurable memristors remain sparse are often limited specific materials such Mott insulators without fully unique reconfigurability. This review specifically focuses examining dual-mode operation, diverse physical mechanisms, structural designs, material properties, switching behaviors, applications. It highlights recent advancements low-power-consumption solutions memristor-based neural networks critically evaluates challenges deploying standalone or artificial systems. provides in-depth technical insights quantitative benchmarks guide future development implementation computing.

Language: Английский

Citations

1
Photosensitive resistive switching in parylene-PbTe nanocomposite memristors for neuromorphic computing DOI
Andrey Trofimov, A. V. Emelyanov, А. Н. Мацукатова

et al.

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

Published: Jan. 1, 2025

Reliable parylene–PbTe memristors controlled via electrical and optical stimuli replicate key synaptic functions are applicable in neuromorphic computing systems.

Language: Английский

Citations

0
Analysis of a Memcapacitor‐Based Online Learning Neural Network Accelerator Framework DOI Creative Commons
Ankur Singh, Dowon Kim, Byung‐Geun Lee

et al.

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

Published: March 31, 2025

Data‐intensive computing tasks, such as training neural networks, are fundamental to artificial intelligence applications but often demand substantial energy resources. This study presents a novel complementary metal‐oxide‐semiconductor (CMOS)‐based memcapacitor framework designed address these challenges by enabling efficient and robust neuromorphic computing. Utilizing devices, crossbar array that performs parallel vector‐matrix multiplication operations, validated through cadence simulations implemented in python for scalable accelerator design, is developed. The demonstrates outstanding performance across classification achieving 98.4% accuracy digit recognition 85.9% object recognition. A key aspect of this research its focus on real‐world fabrication nonidealities, including up 30% device parameter variations, ensuring robustness reliability under practical deployment conditions. results emphasize the effectiveness capacitance‐based systems handling tasks while demonstrating resilience fabrication‐induced variations. work establishes foundation scalable, energy‐efficient, memcapacitor‐based advancing potential intelligent intelligence‐driven paving way future innovations

Language: Английский

Citations

0
All-Optical Synapses Enabled by Photochromic Materials for High-Accuracy Optical Signal Recognition DOI

Fangzhen Hu,

Xiao‐Guang Ma, Xi Chen

et al.

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

Published: April 8, 2025

Language: Английский

Citations

0
Electrical/light-modulated Kesterite Synaptic Memristor for Potential Near-Infrared Vision Imaging DOI
Jian‐Hui Lan, Zhanchuan Cai, Xiaofei Dong

et al.

Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 180469 - 180469

Published: April 1, 2025

Language: Английский

Citations

0
Donor‐redox covalent organic framework‐based memristors for visual neuromorphic system DOI Creative Commons

Qiongshan Zhang,

Qiang Che,

Fu‐Zhen Xuan

et al.

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

Published: May 12, 2025

Abstract Artificial visual neural systems have emerged as promising candidates for overcoming the von Neumann bottleneck via integrating image perception, storage, and computation. Existing photoelectric memristors are limited by need specific wavelengths or long input times to maintain stable behavior. Here, we introduce a benzothiophene‐modified covalent organic framework, enhancing response of methyl trinuclear copper low‐voltage (0.2 V) redox processes. The material enables modulation 50 conductive states light electrical signals, improving recognition accuracy in low light, dense fog, high‐frequency motion. ITO/BTT‐Cu 3 /ITO device's increases from 7.1% with 2 87.1% after training. This construction strategy synergistic effect interactions offer new pathway development neuromorphic computing elements capable processing environmental information situ.

Language: Английский

Citations

0
人工突触和光磁神经网络的研究进展（特邀） DOI

林基伟 Lin Jiwei,

陈杰威 Chen Jiewei,

李丽华 Li Lihua

et al.

Chinese Journal of Lasers, Journal Year: 2025, Volume and Issue: 52(9), P. 0907303 - 0907303

Published: Jan. 1, 2025

Citations

0