Memristive devices with high-density and high-speed performance have considerable potential for neuromorphic computing applications in data storage and artificial synapses. However, current memristive devices that are based on conductive filaments, such as silver, are unstable owing to the high mobility and low thermodynamic stability of the filaments. A high-quality SnSe film was deposited using the pulsed laser deposition technology, and high-performance Pd/SnSe/NSTO devices were fabricated. High-stability memristive devices can not only implement simple arithmetic function but also exhibit the centralized distribution of SET/RESET voltage and cellcell uniformity. The SET/RESET power can achieve approximately 4.1 and 61 μW power. The possibility of Pd filament formation and Pd diffusion in SnSe thin films is first confirmed by combining high-resolution transmission electron microscopy, energy-dispersive spectrometer mapping, and first principle calculation. The formation and destruction process of Pd filaments can simulate the influx and extrusion kinetics of K, Ca, or Na in biological synapses and implements considerable synaptic functions. This study thus provides a new idea for improving device performance using different filament materials, which can greatly facilitate the development of neuromorphic computing.
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http://dx.doi.org/10.1021/acsami.1c01076 | DOI Listing |
Nano Lett
January 2025
Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States.
Controlling the Mott transition through strain engineering is crucial for advancing the development of memristive and neuromorphic computing devices. Yet, Mott insulators are heterogeneous due to intrinsic phase boundaries and extrinsic defects, posing significant challenges to fully understanding the impact of microscopic distortions on the local Mott transition. Here, using a synchrotron-based scanning X-ray nanoprobe, we studied the real-space structural heterogeneity during the structural phase transition in a VO thin film.
View Article and Find Full Text PDFResearch (Wash D C)
January 2025
Key Laboratory for UV Light-Emitting Materials and Technology (Ministry of Education), College of Physics, Northeast Normal University, Changchun, China.
The optoelectronic memristor integrates the multifunctionalities of image sensing, storage, and processing, which has been considered as the leading candidate to construct novel neuromorphic visual system. In particular, memristive materials with all-optical modulation and complementary metal oxide semiconductor (CMOS) compatibility are highly desired for energy-efficient image perception. As a p-type oxide material, CuO exhibits outstanding theoretical photoelectric conversion efficiency and broadband photoresponse.
View Article and Find Full Text PDFAdv Mater
January 2025
Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
Advancements in artificial intelligence (AI) and big data have highlighted the limitations of traditional von Neumann architectures, such as excessive power consumption and limited performance improvement with increasing parameter numbers. These challenges are significant for edge devices requiring higher energy and area efficiency. Recently, many reports on memristor-based neural networks (Mem-NN) using resistive switching memory have shown efficient computing performance with a low power requirement.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, I-16145, Genoa, Italy.
Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-physically realistic dynamics of biological neural systems in real-time. However, similar to their biological counterparts, these circuits have limited resolution and are affected by a high degree of variability. By developing a recurrent spiking neural network model of the retinocortical visual pathway, we show how such noisy and heterogeneous computing substrate can produce linear receptive fields tuned to visual stimuli with specific orientations and spatial frequencies.
View Article and Find Full Text PDFSci Rep
December 2024
Chair of Applied Electrodynamics and Plasma Technology, Ruhr University Bochum, Universitätsstraße 150, 44780, Bochum, Germany.
Nonlinearity is a crucial characteristic for implementing hardware security primitives or neuromorphic computing systems. The main feature of all memristive devices is this nonlinear behavior observed in their current-voltage characteristics. To comprehend the nonlinear behavior, we have to understand the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in these devices.
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