Results 231 to 240 of about 24,472 (312)

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source

Printed Organic Memristive Device on Rigid and Flexible Supports for Neuromorphic Applications. [PDF]

Biomimetics (Basel)
Vurro D, Basso SD, Marasso SL, Ballesio A, Tarabella G, D'Angelo P, Erokhin V.   +6 more
europepmc   +1 more source

Logic Functionality and Circuit Design of In2Se3‐Based Split‐Gate Ferroelectric Field‐Effect Transistor for Zero‐Trust Applications

Advanced Electronic Materials, EarlyView.
ABSTRACT Van der Waals ferroelectric materials are emerging as key building blocks for future logic devices and integrated circuits. Among them, α‐In2Se3 offers a unique combination of robust room temperature ferroelectricity and semiconducting behavior.
Ankita Ram, Cédric Marchand, Shehr Bano Masood, Aymen Mahmoudi, Takashi Taniguchi, Kenji Watanabe, Bernard Doudin, Abdelkarim Ouerghi, Ian O'Connor, Alexei Gruverman, Jean‐François Dayen   +10 more
wiley   +1 more source

Dedicated and Reconfigurable Artificial Neurons and Synapses based on Two-Dimensional Materials for Efficient Neuromorphic Application. [PDF]

Nanomicro Lett
Chen D, Yu P, Li Y, Shang J, Wu H, Yao X, Tang X, Li C, Zhu M, Gao C, Sun J, Tian H, Deng T.   +12 more
europepmc   +1 more source

Experimental Demonstration of Temporally Aware Fault‐Tolerant Sensor Fusion Using Memristive Associative Learning

Advanced Electronic Materials, EarlyView.
In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj, Ashfak Nehal, Dmitrii Semenov, Roman Sotner, Sayani Majumdar   +4 more
wiley   +1 more source

Retinocortical in-sensor neuromorphic vision platform for NIR-augmented artificial vision. [PDF]

Nat Commun
An JB, Park K, Lee IS, Lu S, Park HG, Heo NS, Hwang YS, Kwak K, Park K, Park SH, Lee JM, Hong J, Park SK, Kim HJ.   +13 more
europepmc   +1 more source

Ferroelectric Behavior of Micro‐ to Submicron‐Scale HZO Capacitors: Impact of the Perimeter‐to‐Area Ratio

Advanced Electronic Materials, EarlyView.
Using C‐AFM, W/HZO/p‐Ge capacitors with areas down to 0.26 µm2 are investigated. Frequency‐dependent voltage ramps reveal switching currents that confirm complete polarization reversal across the entire electrode area, while PUND enables reconstruction of P–V loops.
Lucian Trupina, Stefan Neatu, Lucian Pintilie, Lucia Nicoleta Leonat, Stavros Kitsios, Stefania Skorda, Alexandros El Sachat, Polychronis Tsipas, Alexander Flasby, Laura Bégon‐Lours, Athanasios Dimoulas   +10 more
wiley   +1 more source

Self-Powered Neuromorphic Touch Sensors Based on Triboelectric Devices: Current Approaches and Open Challenges. [PDF]

Small
Torricelli F, Pace G.
europepmc   +1 more source

Nonmonotonic Enhancement of Electro‐Optic Properties of Wurtzite AlN Thin Films by Sc Doping

Advanced Electronic Materials, EarlyView.
EO coefficient, rc, for Sc‐AlN thin films in comparison with that for Mg ZnO thin films (left). Calculated electric field intensity of the fundamental mode supported by the active area that includes Sc‐AlN (right). ABSTRACT Wurtzite ferroelectrics, such as Sc‐doped AlN, have recently attracted considerable attention for their potential in realizing ...
K. Abe, X. Yuan, K. Okamoto, X. Liu, C. Wang, T. Wu, H. Funakubo, S. Kondo, T. Nagasaki, P. Ma, A. Emboras, T. Yamada   +11 more
wiley   +1 more source

Federated training of spiking neural networks on edge hardware for audio processing. [PDF]

Front Neurosci
Kaimal SS, Jb A, Reka SS, Venugopal P.
europepmc   +1 more source