An Optimal Machine Learning Classification Model for Flash Memory Bit Error Prediction

  • Barry FitzgeraldEmail author
  • Conor Ryan
  • Joe Sullivan
Part of the Studies in Computational Intelligence book series (SCI, volume 801)


NAND flash memory is now almost ubiquitous in the world of data storage. However, NAND wears out as it is used, and manufacturers specify the number of times a device can be rewritten (known as program-erase cycles) very conservatively to account for quality variations within and across devices. This research uses machine learning to predict the true cycling level each part of a NAND device can tolerate, based on measurements taken from the device as it is used. Custom-designed hardware is used to gather millions of data samples and eight machine learning classification methods are compared. The classifier is then optimised using ensemble and knowledge-based techniques. Two new subsampling methods based on the error probability density function are also proposed.


Flash memory Machine learning Error rate prediction Classification Subsampling Classifier ensemble 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Limerick Institute of TechnologyLimerickIreland
  2. 2.University of LimerickLimerickIreland

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