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Optimal Placement of Read Thresholds for Coded NAND Flash Memory

机译:编码NAND闪存的读取阈值的最佳位置

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Recent advances in the flash memory technology call for more efficient error-correction codes (ECCs) than the conventional, yet very popular, ones such as BCH codes. The ability to make multiple voltage reads allows one to estimate soft values at the time of decoding, which in turn makes soft-decision ECCs such as LDPC codes a suitable candidate for implementation in flash memories. On the other hand, fully utilizing the potential of soft decision based codes demands higher precision memory sensing, which introduces a trade-off between the read latency and the error probability. In this paper, we explore and compares two approaches to optimize the positioning as well as the number of read (word-line) voltages for a specified program/erase (PE) cycle.In the first approach, we aim for selecting those read thresholds that maximize the mutual information (MMI) of the equivalent discrete memoryless channel. By utilizing conventional optimization methods such as the gradient descent (GD), we are able to find the optimal read locations for any number of read probes. Our simulation results show that ~20 reads are effective. Next, we redesign our optimization problem to take the LDPC code structure into account. To do so, we use discretized density evolution (DDE) as a proxy for bit error rate (BER), which serves as our cost function in the GD search. To overcome the problem of local minima, we propose a two-step optimization: MMI for coarse optimization, followed by DDE for fine optimization. Simulation results confirm the effectiveness of this method.1
机译:闪存技术的最新进步呼叫比传统的误差码(ECC)呼叫更高效的误差码(ECC),例如非常流行,例如BCH代码。使多个电压读取的能力允许其中一个估计解码时的软值,这反过来使诸如LDPC诸如LDPC的软判决ECC用于在闪存中实现的合适候选者。另一方面,充分利用基于软判定的代码的潜力需要更高的精确存储器感测,这在读取延迟和误差概率之间引入了权衡。在本文中,我们探索并比较了两种方法来优化指定程序/擦除(PE)周期的定位和读取(字线)电压的数量。在第一种方法中,我们的目标是选择那些读取阈值最大化等同的离散记忆信道的互信息(MMI)。通过利用诸如梯度下降(GD)的传统优化方法,我们能够找到任何数量的读取探针的最佳读取位置。我们的仿真结果表明,〜20读是有效的。接下来,我们重新设计了我们的优化问题,以考虑LDPC代码结构。为此,我们将离散密度进化(DDE)用作位错误率(BER)的代理,其用作GD搜索中的成本函数。为了克服当地最小值的问题,我们提出了两步优化:MMI用于粗略优化,然后是DDE进行精细优化。仿真结果证实了这种方法的有效性。 1

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