@inproceedings{dd5c0b3f11114d9cb2b64d4638fd92e2,
title = "The characterisation and optimisation of TLC NAND flash memory using machine learning: A position paper",
abstract = "Flash memory is non-volatile and, while it is becoming ever more commonplace, it is not yet a complete replacement for hard disk drives. The physical layout of Flash means that it is more susceptible to degradation over time, leading to a limited lifetime of use. This paper will give an introduction to NAND Flash memory, followed by an overview of the relevant research on the reliability of MLC memory, conducted using Machine Learning (ML). The results obtained will then be used to characterise and optimise the reliability of TLC memory.",
keywords = "Endurance, Flash memory, Machine learning (ML), Multi-level cell (MIX), NAND, NOR, Non-volatile memory, Reliability, Retention, Triple-level cell (TLC), Wearout",
author = "Sorcha Bennett and Joe Sullivan",
year = "2013",
language = "English",
isbn = "9789898565389",
series = "ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence",
pages = "559--564",
booktitle = "ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence",
note = "5th International Conference on Agents and Artificial Intelligence, ICAART 2013 ; Conference date: 15-02-2013 Through 18-02-2013",
}