Machine Learning-Based Proactive Data Retention Error Screening In 1xnm Tlc Nand Flash
2016 IEEE INTERNATIONAL RELIABILITY PHYSICS SYMPOSIUM (IRPS)(2016)
摘要
A screening method to proactively reduce data retention, as well as program disturb errors. Repeated program disturb (P.D.) measurement indicates that 25% of P.D. errors are concentrated in 3.5% of the memory cells, called PD-weak cells. PD-weak cells have 2.4x worse data retention (D.R.) than non-PD-weak cells, therefore D.R. errors are reduced by PD-weak cell screening. Proactive D. R. detection is a new capability, because conventional retention testing time is too long for chip testing. In 1Xnm TLC NAND flash, removal of PD-weak cells with <2% overhead extends D.R. by 20%. The measurement method is described, and machine learning is applied to detect PD-weak cells. Detection rate vs. cost is also compared for 3 learning algorithms.
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关键词
NAND flash,machine learning,reliability
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