Sneak Path Interference-Aware Adaptive Detection and Decoding for Resistive Memory Arrays

TL;DR

This work models resistive memory (ReRAM) crossbar channels with sneak path interference (SPI) via a quantized cascaded channel, and optimizes p-bit quantizers by maximizing mutual information. It then develops SPI-aware adaptive detection and decoding schemes at both array and column levels, using LDPC codes and iterative detection/decoding with per-array or per-column SPOP estimation to refine quantizer boundaries. A channel-decomposition method is proposed to analyze the nonstationary SPI channel by expressing it as a mixture of i.i.d. subchannels, enabling tractable performance predictions. Simulations demonstrate that three quantization bits with a single iteration approach the ideal performance, with column-level adaptation outperforming the array-level approach and offline quantizer design reducing online complexity. The framework provides a practical path to high-density ReRAM storage with mitigated SPI effects, compatible with existing coding schemes for across-array performance gains.

Abstract

Resistive random-access memory (ReRAM) is an emerging non-volatile memory technology for high-density and high-speed data storage. However, the sneak path interference (SPI) occurred in the ReRAM crossbar array seriously affects its data recovery performance. In this letter, we first propose a quantized channel model of ReRAM, based on which we design both the one-bit and multi-bit channel quantizers by maximizing the mutual information of the channel. A key channel parameter that affects the quantizer design is the sneak path occurrence probability (SPOP) of the memory cell. We first use the average SPOP calculated statistically to design the quantizer, which leads to the same channel detector for different memory arrays. We then adopt the SPOP estimated separately for each memory array for the quantizer design, which is generated by an effective channel estimator and through an iterative detection and decoding scheme for the ReRAM channel. This results in an array-level SPI-aware adaptive detection and decoding approach. Moreover, since there is a strong correlation of the SPI that affects memory cells in the same rows/columns than that affecting cells in different rows/columns, we further derive a column-level scheme which outperforms the array-level scheme. We also propose a channel decomposition method that enables effective ways for theoretically analyzing the ReRAM channel. Simulation results show that the proposed SPI-aware adaptive detection and decoding schemes can approach the ideal performance with three quantization bits, with only one decoding iteration.

Figures (7)

• Figure 1: Cascaded channel model of resistive memory arrays.
• Figure 2: Quantized channel model of resistive memory arrays.
• Figure 3: Average SPOP versus actual SPOP of ReRAM arrays.
• Figure 4: LDPC coded ReRAM system with adaptive and iterative detection and decoding and channel estimation.
• Figure 5: BER comparison of LDPC coded ReRAM system designed based on the array-level actual SPOP ${\epsilon}_{A_l}$ and those obtained by channel decomposition, with $M\times N=32\times 32$.