Machine learning-based proactive data retention error screening in 1Xnm TLC NAND flash

Abstract

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 2.4× 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.