Histogram-Based Flash Channel Estimation

TL;DR

This paper introduces a framework for ongoing Flash channel estimation using limited histogram measurements and a model-based approach, improving accuracy and speed in tracking degradation over P/E cycles.

Contribution

It proposes a novel channel estimation method leveraging histogram binning and the Levenberg-Marquardt algorithm for efficient degradation tracking in Flash memory.

Findings

Histogram binning with ten bins approximates the original distribution well.

Levenberg-Marquardt algorithm offers a good balance of speed and accuracy.

The framework enables effective ongoing channel estimation in Flash devices.

Abstract

Current generation Flash devices experience significant read-channel degradation from damage to the oxide layer during program and erase operations. Information about the read-channel degradation drives advanced signal processing methods in Flash to mitigate its effect. In this context, channel estimation must be ongoing since channel degradation evolves over time and as a function of the number of program/erase (P/E) cycles. This paper proposes a framework for ongoing model-based channel estimation using limited channel measurements (reads). This paper uses a channel model characterizing degradation resulting from retention time and the amount of charge programmed and erased. For channel histogram measurements, bin selection to achieve approximately equal-probability bins yields a good approximation to the original distribution using only ten bins (i.e. nine reads). With the channel model and binning strategy in place, this paper explores candidate numerical least squares algorithms and ultimately demonstrates the effectiveness of the Levenberg-Marquardt algorithm which provides both speed and accuracy.