Results 141 to 150 of about 25,251 (300)

The fluidic memristor: collective phenomena in elastohydrodynamic networks [PDF]

, 2023
Alejandro Martínez-Calvo, Matthew D Biviano, Anneline H. Christensen, Eleni Katifori, Kaare H. Jensen, Miguel Ruíz-García   +5 more
openalex   +1 more source

Tunable Electronic and Optoelectronic Properties of MoS2 Through Molecular Coverage‐Controlled Polyoxometalate Doping

Advanced Electronic Materials, EarlyView.
Molecular charge transfer at the V12‐DyPc/MoS2 interface stabilizes trions, suppressing neutral A‐exciton emission and enabling controlled modulation of the A–‐trion population, bridging excitonic physics with polyoxometalate charge‐transport functionality.
Jean‐Pierre Glauber, Marco Moors, Dmitry A. Ryndyk, Emad Najafidehaghani, Jonas Lorenz, Rahel‐Manuela Maas, Nils Boysen, Harish Parala, Thomas Heine, Anjana Devi, Kirill Monakhov   +10 more
wiley   +1 more source

Optimization of non-linear conductance modulation based on metal oxide memristors [PDF]

, 2018
Huan Liu, Min Wei, Yuzhong Chen
openalex   +1 more source

Electrode‐Engineered Dual‐Mode Multifunctional Lead‐Free Perovskite Optoelectronic Memristors for Neuromorphic Computing

Advanced Electronic Materials, EarlyView.
A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos, Konstantinos Rogdakis, Konstantinos Chatzimanolis, Katerina Anagnostou, Emmanuel Kymakis   +4 more
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Carbon-based memristors for resistive random access memory and neuromorphic applications

Chip
As a typical representative of nanomaterials, carbon nanomaterials have attracted widespread attention in the construction of electronic devices owing to their unique physical and chemical properties, multi-dimensionality, multi-hybridization methods ...
Fan Yang, Zhaorui Liu, Xumin Ding, Yang Li, Cong Wang, Guozhen Shen   +5 more
doaj   +1 more source

Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching (Adv. Mater. Technol. 19/2024) [PDF]

Dongyeol Ju, Jungwoo Lee, Sungjun Kim
openalex   +1 more source

Unsupervised learning in hexagonal boron nitride memristor-based spiking neural networks [PDF]

, 2023
Sahra Afshari, Jing Xie, Mirembe Musisi‐Nkambwe, Sritharini Radhakrishnan, Ivan Sanchez Esqueda   +4 more
openalex   +1 more source

Triboelectric Tactile Transducers for Neuromorphic Sensing and Synaptic Emulation: Materials, Architectures, and Interfaces

Advanced Energy and Sustainability Research, EarlyView.
Triboelectric nanogenerators are vital for sustainable energy in future technologies such as wearables, implants, AI, ML, sensors and medical systems. This review highlights improved TENG neuromorphic devices with higher energy output, better stability, reduced power demands, scalable designs and lower costs.
Ruthran Rameshkumar, Arunkumar Chandrasekhar, Poongundran Selvaprabhu   +2 more
wiley   +1 more source

Designing Memristive Materials for Artificial Dynamic Intelligence

Advanced Intelligent Discovery, EarlyView.
Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley   +1 more source