Results 211 to 220 of about 80,479 (283)

Optical Charge Trap Memory Based on Graphene/ZnO Heterostructures for Long‐Term Retention and Adaptive Learning

Advanced Electronic Materials, EarlyView.
A biocompatible graphene/ZnO optical charge trap memory (CTM) is reported with over 54 h retention, enabled by interfacial photodoping. Using transient absorption spectroscopy and electrical analysis, charge transfer quenching is elucidated and reveal that a large energy barrier at the interface is responsible for long‐term memory retention.
Seungmin Shin, Junhyung Kim, Hyungdoh Lee, Yixuan Dou, Jacob T. Heiden, Seung Eun Lee, Jinwoo Song, Lina Quan, Giulia Tagliabue, Min Seok Jang, Himchan Cho   +10 more
wiley   +1 more source

Exfoliated‐MoS2 Gradual Resistive Switching Devices as Artificial Synapses

Advanced Electronic Materials, EarlyView.
A vertical memristor based on untreated, exfoliated MoS2 is presented, revealing gradual resistive switching governed by Schottky barrier modulation at the MoS2/metal interface from the trapping/detrapping of charges. Furthermore, the device emulates synaptic‐like plasticity functions, including: potentiation, depression, and spike‐amplitude‐dependent ...
Deianira Fejzaj, Richard Schroedter, Thomas Mikolajick, André Heinzig   +3 more
wiley   +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

Analog Weight Update Rule in Ferroelectric Hafnia, Using picoJoule Programming Pulses

Advanced Electronic Materials, EarlyView.
Resistive, ferroelectric synaptic weights based on BEOL‐compatible hafnia/zirconia nanolaminates are fabricated. Lateral downscaling the devices below 10 µm2 enables 20 ns programming with electrical pulses, dissipating ≤ 3 pJ. Experimental results show that final conductance state is set by pulse amplitude, and is largely independent of the initial ...
Alexandre Baigol, Nikhil Garg, Matteo Mazza, Yanming Zhang, Elisa Zaccaria, Wooseok Choi, Bert Jan Offrein, Laura Bégon‐Lours   +7 more
wiley   +1 more source

Synchronization of Analog Neuron Circuits With Digital Memristive Synapses: An Hybrid Approach

Advanced Electronic Materials, EarlyView.
An hybrid circuit mimicking neural units coupled using memristive synapses is introduced. The analog neurons provide flexibility and robustness, and the digital memristive coupling guarantees the full reconfigurability of the interconnection. The onset of a synchronized spiking behavior in two circuits mimicking the Izhikevich neuron is discussed from ...
Lamberto Carnazza, Francesco Maria Esposito, Carlo Famoso, Arturo Buscarino   +3 more
wiley   +1 more source

A Dual‐Memory Ferroelectric Transistor Emulating Synaptic Metaplasticity for High‐Speed Reservoir Computing

Advanced Electronic Materials, EarlyView.
A CMOS‐compatible ferroelectric transistor harnesses the interplay between stable gate polarization memory and volatile non‐quasi‐static channel charge dynamics to emulate how biological synapses regulate their own plasticity. This brain‐inspired dual‐memory mechanism, realized in a single device, enables a physical reservoir computer that solves ...
Yifan Wang, Muhammad Sakib Shahriar, Salma Soliman, Noah Vaillancourt, Lance Fernandes, Andrea Padovani, Asif Islam Khan, Md Sakib Hasan, Raisul Islam   +8 more
wiley   +1 more source

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source

Research Progress and Applications of Non‐Carrier‐Injection Electroluminescence

Advanced Electronic Materials, EarlyView.
Non‐carrier‐injection electroluminescence (NCI‐EL) uses AC fields and displacement currents to trigger light from internal charge reservoirs, enabling minimalist emitters with remotely coupled terminals. This review maps shared mechanisms across organics, GaN, quantum dots, and TMDCs, compares planar, interdigital, single‐terminal, and coaxial designs,
Wei Huang, Jianbi Le, Rong Chen, Zhixian Lin, Xiongtu Zhou, Yongai Zhang, Chaoxing Wu   +6 more
wiley   +1 more source

Janus Synapses as Modular Neurointerfaces. [PDF]

ACS Appl Mater Interfaces
Cho W, Jung M, Chung TD.
europepmc   +1 more source