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Multiple reads of the same Flash memory cell with distinct word-line voltages provide enhanced precision for LDPC decoding. In this paper, the word-line voltages are optimized by maximizing the mutual information (MI) of the quantized channel. The enhanced precision from a few additional reads allows FER performance to approach that of full-precision soft information and enables an LDPC code to significantly outperform a BCH code. A constant-ratio constraint provides a significant simplification in the optimization with no noticeable loss in performance. For a well-designed LDPC code, the quantization that maximizes the mutual information also minimizes the frame error rate in our simulations. However, for an example LDPC code with a high error floor caused by small absorbing sets, the MMI quantization does not provide the lowest frame error rate. The best quantization in this case introduces more erasures than would be optimal for the channel MI in order to mitigate the absorbing sets of the poorly designed code. The paper also identifies a trade-off in LDPC code design when decoding is performed with multiple precision levels; the best code at one level of precision will typically not be the best code at a different level of precision., Comment: arXiv admin note: substantial text overlap with arXiv:1210.0149

This paper investigates the application of low-density parity-check (LDPC) codes to Flash memories. Multiple cell reads with distinct word-line voltages provide limited-precision soft information for the LDPC decoder. The values of the word-line voltages (also called reference voltages) are optimized by maximizing the mutual information (MI) between the input and output of the multiple-read channel. Constraining the maximum mutual-information (MMI) quantization to enforce a constant-ratio constraint provides a significant simplification with no noticeable loss in performance. Our simulation results suggest that for a well-designed LDPC code, the quantization that maximizes the mutual information will also minimize the frame error rate. However, care must be taken to design the code to perform well in the quantized channel. An LDPC code designed for a full-precision Gaussian channel may perform poorly in the quantized setting. Our LDPC code designs provide an example where quantization increases the importance of absorbing sets thus changing how the LDPC code should be optimized. Simulation results show that small increases in precision enable the LDPC code to significantly outperform a BCH code with comparable rate and block length (but without the benefit of the soft information) over a range of frame error rates.

High-capacity NAND flash memories use multi-level cells (MLCs) to store multiple bits per cell and achieve high storage densities. Higher densities cause increased raw bit error rates (BERs), which demand powerful error correcting codes. Low-density parity-check (LDPC) codes are a well-known class of capacity-approaching codes in AWGN channels. However, LDPC codes traditionally use soft information while the flash read channel provides only hard information. Low resolution soft information may be obtained by performing multiple reads per cell with distinct word-line voltages. We select the values of these word-line voltages to maximize the mutual information between the input and output of the equivalent multiple-read channel under any specified noise model. Our results show that maximum mutual-information (MMI) quantization provides better soft information for LDPC decoding given the quantization level than the constant-pdf-ratio quantization approach. We also show that adjusting the LDPC code degree distribution for the quantized setting provides a significant performance improvement.