Mixed‐Dimensional Floating Gate Phototransistors for Mixed‐Modal In‐Sensor Reservoir Computing DOI Creative Commons

Wei-Lun Ouyang,

Qirui Zhang, Jiangang Chen

и другие.

Advanced Science, Год журнала: 2025, Номер unknown

Опубликована: Май 8, 2025

Abstract Novel neuromorphic devices constructed from low‐dimensional materials have demonstrated significant potential in visual perception and information processing. Colloidal quantum dots (QDs) exhibit strong light absorption tunable band gaps, while 2D provide smooth interfaces channels with superior charge carrier mobility. However, the of utilizing QDs as floating gates remains largely underexplored. Herein, a floating‐gate phototransistor based on mixed‐dimensional heterostructure 0D‐CsPbBr 3 2D‐MoS 2 few layer is introduced. By leveraging optical advantages 0D‐QDs electrical properties materials, mixed‐modal in‐sensor reservoir computing (RC) realized. Upon electrical/optical stimulation, device demonstrates an on/off ratio 10 7 , over 7‐bit multistates, nonlinear memory decay behavior, dynamics time scales. Building upon these characteristics, enables RC using mixed‐inputs signals. Furthermore, accurate recognition endangered species under extreme weather conditions also through audio‐visual fusion. This study presents compelling paradigm for different‐dimensional to achieve fusion opens new pathways mimicking biological multisensory

Язык: Английский

What is the impact of discrete memristor on the performance of neural network: A research on discrete memristor-based BP neural network DOI
Yuexi Peng, Maolin Li, Zhijun Li

и другие.

Neural Networks, Год журнала: 2025, Номер 185, С. 107213 - 107213

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

2

An Analysis of Components and Enhancement Strategies for Advancing Memristive Neural Networks DOI Open Access
Hyungjun Park, Joon‐Kyu Han,

Seongpil Yim

и другие.

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Янв. 12, 2025

Abstract Advancements in artificial intelligence (AI) and big data have highlighted the limitations of traditional von Neumann architectures, such as excessive power consumption limited performance improvement with increasing parameter numbers. These challenges are significant for edge devices requiring higher energy area efficiency. Recently, many reports on memristor‐based neural networks (Mem‐NN) using resistive switching memory shown efficient computing a low requirement. Even further optimization can be made engineering mechanisms. Nevertheless, systematic reviews that address circuit‐to‐material aspects Mem‐NNs, including their dedicated algorithms, remain limited. This review first categorizes into three components: pre‐processing units, processing learning algorithms. Then, methods to improve integration operational reliability discussed across materials, devices, circuits, algorithms each component. Furthermore, compares recent advancements chip‐level neuromorphic hardware conventional systems, graphic units. The ongoing future directions field discussed, highlighting research enhance functionality Mem‐NNs.

Язык: Английский

Процитировано

1

An innovative biomimetic technology: Memristors mimic human sensation DOI
Kun Wang,

Mengna Wang,

Bai Sun

и другие.

Nano Energy, Год журнала: 2025, Номер unknown, С. 110698 - 110698

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1

Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems DOI Open Access
Xiangxiang Zhang, Chang-Guang Wang, Xin Pi

и другие.

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Янв. 21, 2025

Abstract Mechanical information is a medium for perceptual interaction and health monitoring of organisms or intelligent mechanical equipment, including force, vibration, sound, flow. Researchers are increasingly deploying recognition technologies (MIRT) that integrate acquisition, pre‐processing, processing functions expected to enable advanced applications. However, this also poses significant challenges acquisition performance efficiency. The novel exciting mechanosensory systems in nature have inspired us develop superior bionic (MIBRT) based on materials, structures, devices address these challenges. Herein, first strategies pre‐processing presented their importance high‐performance highlighted. Subsequently, design considerations sensors by mechanoreceptors described. Then, the concepts neuromorphic summarized order replicate biological nervous system. Additionally, ability MIBRT investigated recognize basic information. Furthermore, further potential applications robots, healthcare, virtual reality explored with view solve range complex tasks. Finally, future opportunities identified from multiple perspectives.

Язык: Английский

Процитировано

1

Sodium-Enhanced Perovskite Reservoir for Photonic In-Sensor Computing DOI
Yan Wang, Xiaohan Cheng, Shidong Chen

и другие.

Nano Energy, Год журнала: 2025, Номер unknown, С. 110830 - 110830

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1

Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives DOI Open Access
Amiya Kumar Tripathy, Akshata Y. Patne, Subhra Mohapatra

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(22), С. 12368 - 12368

Опубликована: Ноя. 18, 2024

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, data processing. ML enhances nanotechnology by facilitating the processing of dataset nanomaterial synthesis, characterization, optimization nanoscale properties. Conversely, improves speed efficiency computing power, which is crucial for algorithms. Although capabilities still their infancy, a review research literature provides insights into exciting frontiers these suggests that integration can be transformative. Future directions include developing tools manipulating nanomaterials ensuring ethical unbiased collection models. This emphasizes importance coevolution technologies mutual reinforcement to advance scientific societal goals.

Язык: Английский

Процитировано

4

Advancing High-Performance Memristors Enabled by Position-Controlled Grain Boundaries in Controllably Grown Star-Shaped MoS2 DOI
Shangui Lan, Fangyuan Zheng, Chang‐Chun Ding

и другие.

Nano Letters, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 22, 2024

Two-dimensional transition metal dichalcogenides are highly promising platforms for memristive switching devices that seamlessly integrate computation and memory. Grain boundaries (GBs), an important micro-nanoscale structure, hold tremendous potential in memristors, but their role remains unclear due to random distribution, which hinders fabrication. Herein, we present a novel chemical vapor deposition approach synthesize star-shaped MoS

Язык: Английский

Процитировано

4

Dynamic IGZO-based memristors for cost-effective physical reservoir computing DOI

Dongyeol Ju,

Sungjun Kim

Chinese Journal of Physics, Год журнала: 2024, Номер 91, С. 361 - 368

Опубликована: Июль 23, 2024

Язык: Английский

Процитировано

3

Advanced AI computing enabled by 2D material-based neuromorphic devices DOI Creative Commons
YunSeok Choi, Siwoo Jeong,

Hyeonu Jeong

и другие.

Deleted Journal, Год журнала: 2025, Номер 2(1)

Опубликована: Апрель 21, 2025

Язык: Английский

Процитировано

0

SOM-Associated-SNN: Enhancing Audio Classification with Spiking Neural Networks through Single-Modality Clustering and Associative Learning DOI
Xin Liu, Lingfei Mo,

Meng‐Ting Tang

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 130416 - 130416

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0