Abstract
Resistive random access memory ReRAM has attracted great attention due to its potential for flash memory replacement in next generation nonvolatile memory applications. Among the main characteristics of this type of memory, we have: low energy consumption, high-speed switching, durability, scalability and friendly manufacturing process. This device is based on resistive switching phenomenon for operation, which is reversible and can be played back repeatedly. In this work, eight different devices are developed and fabrication is made as follows: thin films are obtained by dip coating technique. The dip coating apparatus basically consists of a clamp which holds the substrate is dipped in a GO solution (graphene oxide) which containing dopant (cupper, iron or silver) or CuO (copper oxide). ITO (indium tin oxide) and aluminum contacts were evaporated. The devices were developed with purpose: intention is record and read information dynamically with appropriate algorithm. There is even the possibility of storing images. With these functions, it would be promising to enter the neuromorphic computing area that is one of the resistive memory applications. ReRAM technology advent represents a paradigm shift for artificial neural networks, being the best candidate for emulation of synaptic plasticity and learning mode.
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Sparvoli, M., Silva, M.F.P., Gazziro, M. (2017). Development of Doped Graphene Oxide Resistive Memories for Applications Based on Neuromorphic Computing. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_50
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DOI: https://doi.org/10.1007/978-3-319-59153-7_50
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