Results 111 to 120 of about 4,682,633 (203)

Highly‐Uniform Passive Crossbar Arrays of Resistive Switching Random Access Memory (RRAM) for In‐Memory Computing Applications

Advanced Electronic Materials, EarlyView.
Passive resistive memory arrays promise efficient in‐memory computing but suffer from sneak paths and programming variability. Here, highly uniform 32 × 32 passive RRAM crossbars are programmed with multilevel precision below 3% error and 99.5% yield.
S. Ricci, P. Mannocci, M. Porzani, D. Bridarolli, F. Carletti, M. Farronato, D. Ielmini   +6 more
wiley   +1 more source

AI-based development and optimization of sealing materials for secondary grouting in methane recovery. [PDF]

Sci Rep
Zandy Ilghani N, Maleki H.
europepmc   +1 more source

Exploiting Temperature Effects for Robust Control and Reference Circuits Using Thin‐Film Contact‐Controlled Transistors

Advanced Electronic Materials, EarlyView.
Compact circuits based on contact‐controlled transistors are well‐suited to unsupervised thermal management, sensitive temperature measurement, or temperature‐stable current references. Demonstrated on flexible microcrystalline silicon and supported by simulation, the approach does not require supply voltage regulation, remains manufacturable across ...
Eva Bestelink, Jean‐Charles Fustec, Olivier de Sagazan, Rogier Fransen, George Bairaktatis, Emmanuel Jacques, Radu A. Sporea   +6 more
wiley   +1 more source

Hybrid physics-informed artificial intelligence for high-fidelity modeling and optimization of electrical systems. [PDF]

Front Artif Intell
Nyangon J.
europepmc   +1 more source

Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing

Advanced Electronic Materials, EarlyView.
A compact QASRR‐based THz metamaterial absorber enables polarization‐insensitive dual‐band absorption and skin‐cancer‐related refractive‐index sensing through measurable resonance shifts. Field, surface‐current, and circuit analyses clarify the dual‐resonance mechanism, while StackNet‐assisted prediction accurately estimates the simulated absorption ...
Md. Murad Kabir Nipun, S. M. Rifatur Rana, Md. Golam Morshed, Imrose Jahan, F. A. Sabbir Ahamed, Md. Jahedul Islam   +5 more
wiley   +1 more source

BFMGHC: a boosted fuzzy manifold granule hypersurface classifier with local topology preservation. [PDF]

Front Neurorobot
Li W, Si W, Liu Z, Song Y.
europepmc   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

Predictable and Scalable Analog Matrix–Vector Multiplication in Memristor Crossbars via Closed‐Form Wire‐Resistance Compensation

Advanced Electronic Materials, EarlyView.
Wire resistance and sneak paths severely compromise the performance of large memristor crossbars. A predictive closed‐form distributed line‐resistance model, combined with a fourmatrix geometry‐averaging algorithm, eliminates these parasitic limitations.
Davide Rossetti, Yichun Xu, Ludovica Asselta, J. Joshua Yang, R. Stanley Williams, Fernando Corinto   +5 more
wiley   +1 more source

Fourier Neural Operators for Fast Multi-Physics Sensor Response Prediction: Applications in Thermal, Acoustic, and Flow Measurement Systems. [PDF]

Sensors (Basel)
Sayghe A, Mousa M, Batiyah S, Husawi A.
europepmc   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source