Results 81 to 90 of about 9,376 (179)
Advanced Materials Interfaces, Volume 13, Issue 6, 16 March 2026.Mixed halide perovskites suffer from photo‐induced phase segregation, leading to compositional instability and degraded device performance. This review summarizes the dynamic behavior and kinetics of phase segregation in thin films under external stimuli, analyzes compositional and environmental effects, and proposes suppression strategies to enhance ...
Yong Hyun Kim, Hyojung Kim
wiley +1 more source
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2020 Nonvolatile computing-in-memory (nvCIM) exhibits high potential for neuromorphic computing involving massive parallel computations and for achieving high energy efficiency.
Wei-Chen Wei +9 more
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, 2019 Deep Neural Networks (DNNs) have gained immense success in cognitive applications and greatly pushed today's artificial intelligence forward. The biggest challenge in executing DNNs is their extremely data-extensive computations. The computing efficiency
Liu, C., Liu, Fuqiang
core +1 more source
Review of Memristors for In‐Memory Computing and Spiking Neural Networks
Advanced Intelligent Systems, Volume 8, Issue 3, March 2026.Memristors uniquely enable energy‐efficient, brain‐inspired computing by acting as both memory and synaptic elements. This review highlights their physical mechanisms, integration in crossbar arrays, and role in spiking neural networks. Key challenges, including variability, relaxation, and stochastic switching, are discussed, alongside emerging ...
Mostafa Shooshtari +2 more
wiley +1 more source
Designing Memristive Materials for Artificial Dynamic Intelligence
Advanced Intelligent Discovery, Volume 2, Issue 1, February 2026.Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley +1 more source
Advanced Materials Technologies, Volume 11, Issue 1, 8 January 2026.Recent efforts of memristor array‐based hardware neuromorphic computing are discussed for efficient application of VMM on‐chip level in terms of circuit integration and actual application of AI algorithms. The parallel data processing principle of VMM operation is briefly reviewed, and hardware VMM is presented including convolutional transformation ...
Jingon Jang, Sang‐gyun Gi
wiley +1 more source
Applied Sciences, 2019 Integration of metamaterial and nonvolatile memory devices with tunable characteristics is an enthusing area of research. Designing a unique nanoscale prototype to achieve a metasurface with reliable resistive switching properties is an elusive goal.
Niloufar Raeis-Hosseini, Junsuk Rho
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Characterization and Modeling of Multilevel Analog ReRAM Synapses in the Sky130 Process
IEEE Journal on Exploratory Solid-State Computational Devices and CircuitsNonvolatile memory devices play a key role in enabling energy-efficient computing. Among them, analog nonvolatile memories such as resistive random access memory (ReRAM) offer high density and low power compared to conventional digital memories. However,
Irem Didin +3 more
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Advanced Electronic MaterialsResistive random‐access memories (ReRAM) are promising candidates for next‐generation non‐volatile memory, logic components, and bioinspired neuromorphic computing applications.
Manvendra Chauhan +2 more
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GeSeTe-based OTS selector integrated with ReRAM for high-density 1S-1R memory arrays
AIP AdvancesIn this study, we developed a high-performance binary Ovonic Threshold Switching (OTS) selector based on GeSeTe and a resistive random access memory (ReRAM) device based on TaOx.
Hyun Kyu Seo +2 more
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