The HfO-based ferroelectric tunnel junction has received outstanding attention owing to its high-speed and low-power characteristics. In this work, aluminum-doped HfO (HfAlO) ferroelectric thin films are deposited on a muscovite substrate (Mica). We investigate the bending effect on the ferroelectric characteristics of the Au/Ti/HfAlO/Pt/Ti/Mica device. After 1000 bending times, the ferroelectric properties and the fatigue characteristics are largely degraded. The finite element analysis indicates that crack formation is the main reason for the fatigue damage under threshold bending diameters. Moreover, the HfAlO-based ferroelectric synaptic device exhibits excellent performance of neuromorphic computing. The artificial synapse can mimic the paired-pulse facilitation and long-term potentiation/depression of biological synapses. Meanwhile, the accuracy of digit recognition is 88.8%. This research provides a new research idea for the further development of hafnium-based ferroelectric devices.
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http://dx.doi.org/10.1039/d3mh00645j | DOI Listing |
Sensors (Basel)
January 2025
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece.
The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Neurosurgery, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania.
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Flexible memristors are promising candidates for multifunctional neuromorphic computing applications, overcoming the limitations of conventional computing devices. However, unpredictable switching behavior and poor mechanical stability in conventional memristors present significant challenges to achieving device reliability. Here, a reliable and flexible memristor using zirconium-oxo cluster (ZrOOH(OMc)) as the resistive switching layer is demonstrated.
View Article and Find Full Text PDFNano Lett
January 2025
Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, Texas 75080, United States.
Ferroelectric HfZrO (HZO) capacitors have been extensively explored for in-memory computing (IMC) applications due to their nonvolatility and back-end-of-line (BEOL) compatible process. Several IMC approaches using resistance and capacitance states in ferroelectric HZO have been proposed for vector-matrix multiplication (VMM), but previous approaches suffer from limited accuracy and reliability. In this work, we propose a promising approach centered on the remanent polarization (P) switching of binary ferroelectric HZO capacitor synapses.
View Article and Find Full Text PDFNanotechnology
January 2025
Kwangwoon University, 20 Kwangwoonro Nowon-Gu Seoul, Nowon-gu, 01897, Korea (the Republic of).
To implement a neuromorphic computing system capable of efficiently processing vast amounts of unstructured data, a significant number of synapse and neuron devices are needed, resulting in increased area demands. Therefore, we developed a nanoscale vertically structured synapse device that supports high-density integration. To realize this synapse device, the interface effects between the resistive switching layer and the electrode were investigated and utilized.
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