STRAW：一种面向高密度NAND闪存固态硬盘的基于字线的应力感知读回收技术
STRAW: A Stress-Aware WL-Based Read Reclaim Technique for High-Density NAND Flash-Based SSDs
AI总结：
STRAW提出一种字线级读回收技术，通过跟踪每个字线的累积读干扰效应并仅回收严重受扰字线，将读回收引起的页写入减少83.6%，解决了现有块级读回收效率低下的问题。
摘要：
尽管读干扰已成为一个主要的可靠性问题，但现代NAND闪存中的读干扰管理尚未得到深入研究。通过对真实现代NAND闪存进行器件特性表征研究，我们观察到读取一个页会对每个字线（WL）产生异质性的可靠性影响，这使得现有的块级读回收效率极低。我们提出了一种新的字线级读回收技术，称为STRAW，它跟踪每个字线上累积的读干扰效应，并仅回收受到严重干扰的字线。通过避免不必要的读回收操作，STRAW将读回收引起的页写入减少了83.6%，且存储开销可忽略不计。
英文摘要：
Although read disturbance has emerged as a major reliability concern, managing read disturbance in modern NAND flash memory has not been thoroughly investigated yet. From a device characterization study using real modern NAND flash memory, we observe that reading a page incurs heterogeneous reliability impacts on each WL, which makes the existing block-level read reclaim extremely inefficient. We propose a new WL-level read-reclaim technique, called STRAW, which keeps track of the accumulated read-disturbance effect on each WL and reclaims only heavily-disturbed WLs. By avoiding unnecessary read-reclaim operations, STRAW reduces read-reclaim-induced page writes by 83.6\% with negligible storage overhead.
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