Adaptive Read Threshold Optimization for NAND Flash Memory Using Micro and Macro Parameters

This article presents a comprehensive mathematical model for the NAND Flash memory channel, designed to estimate the Raw Bit Error Rate (RBER) based on the mean and variance of Gaussian-distributed errors inherent in Flash memory. By leveraging this model, we optimize the voltage read threshold to minimize RBER, which is critical for enhancing the efficiency of soft decoding in forward error correction (FEC). The proposed approach incorporates a dynamic vector autoregression framework to track the multi-layer Gaussian statistical model of NAND Flash memory, enabling real-time adaptation to varying error conditions. Additionally, a macrolevel model is introduced to capture essential error mechanisms, including retention time degradation, program/erase (P/E) cycle wear, cross-term interactions, and nonlinear behaviors. This macro model provides a robust foundation for estimating the operational lifetime of NAND Flash memory a crucial feature for Self-Monitoring, Analysis, and Reporting Technology (SMART) systems. Simulation results illustrate the impact of biased voltage thresholds on RBER. Notably, a 10% bias in the threshold voltage reduces the operational RBER from 0.018 to 0.012, demonstrating significant reliability improvements. These findings underscore the potential of the proposed optimization techniques to enhance the performance and durability of NAND Flash memory systems.