Memristors and non-volatile memory devices
- Submission status
- Open
- Submission deadline
In the era of big data and machine learning technology, performing low-power in-memory computing operations is becoming an increasingly important requisite. While much advance has been achieved in recent years in terms of software and processing algorithms based on artificial neural networks, the development of hardware architectures from on-chip memory and storage to in-memory computing hardware – and ultimately brain-inspired computing – is still facing significant challenges. In this context, resistive-switching random-access memory devices such as memristors are key elements in the realization of memory integrated processing, as well as artificial synapses and in-memory processing, where memory and processing units are no longer physically separated.
This Collection brings together the latest developments in the realization and optimization of memristive technologies for modern applications that take advantage of neural networks and neuromorphic computing.
We welcome the submission of any paper related to memristors and non-volatile memory devices. Authors will have the option to submit primary research Articles to either Communications Materials or Scientific Reports, and all submissions will be subject to the same review process and editorial standards as regular Articles in their respective journals. Other article types, such as Reviews and Perspectives, can be considered for inclusion in the Collection but only for publication in Communications Materials.
This Collection supports and amplifies research related to the United Nations Sustainable Development Goal 9: Industry, Innovation & Infrastructure.
Editors
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Fabien Alibart
University of Lille, France & University of Sherbrooke, Canada
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Saptarshi Das
Penn State University, USA
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Stephan Menzel
Forschungszentrum Jülich, Germany
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Dashan Shang
Institute of Microelectronics, China
