Adaptive Read Thresholds for NAND Flash

TL;DR

This paper introduces an adaptive algorithm for NAND flash memory that optimizes read thresholds using limited re-reads, improving decoding accuracy without prior noise knowledge, applicable to various noise models.

Contribution

It presents a novel adaptive read threshold algorithm that dynamically adjusts based on noise characterization, enhancing decoding performance in NAND flash memory.

Findings

The method reduces read latency by minimizing re-reads.

It balances hard and soft decoding objectives effectively.

The approach is adaptable to different noise distributions.

Abstract

A primary source of increased read time on NAND flash comes from the fact that in the presence of noise, the flash medium must be read several times using different read threshold voltages for the decoder to succeed. This paper proposes an algorithm that uses a limited number of re-reads to characterize the noise distribution and recover the stored information. Both hard and soft decoding are considered. For hard decoding, the paper attempts to find a read threshold minimizing bit-error-rate (BER) and derives an expression for the resulting codeword-error-rate. For soft decoding, it shows that minimizing BER and minimizing codeword-error-rate are competing objectives in the presence of a limited number of allowed re-reads, and proposes a trade-off between the two. The proposed method does not require any prior knowledge about the noise distribution, but can take advantage of such information when it is available. Each read threshold is chosen based on the results of previous reads, following an optimal policy derived through a dynamic programming backward recursion. The method and results are studied from the perspective of an SLC Flash memory with Gaussian noise for each level but the paper explains how the method could be extended to other scenarios.