Unexpected Error Explosion in NAND Flash Memory: Observations and Prediction Scheme

Abstract

Wear-out has been a critical reliability problem in NAND flash memory. As executing repeated program and erase operations on the NAND flash chips, the number of errors increases and ultimately exceeds the ECC capability. In previous work, error characteristics of flash wear-out are observed by endurance tests on a single type of NAND flash memory. We wonder if the experimental results cover the entire error characteristics of NAND flash memory. In this paper, we tested more than 20 types of NAND flash chips with different vendors and structures and presented an overlook of test results. Through the test results, we found an unexpected error-explosion phenomenon that errors of flash blocks first increase over several cycles and then reach a high value without warning. We analyzed the features of the error-explosion and explored its influence on operation time. And we propose an error-explosion prediction scheme to find the blocks that will occur an error-explosion in the next 1000 P/E cycles. The block identifying operation is realized by the machine-learning model. The performance of six machine-learning methods is compared. The results demonstrate that the Decision Trees and Bagged Classification Trees have the best accuracy.