Error Generation for 3D NAND Flash Memory

Abstract

Three-dimension (3D) NAND flash memory is the preferred storage component of solid-state drive (SSD) for its high ratio of capacity and cost. Optimizing the reliability of modern SSD needs to test and collect a large amount of real-world error data from 3D NAND flash memory. However, the test costs have surged dozens of times as its capacity increases. It's imperative to reduce the costs of testing denser and high-capacity flash memory. To facilitate it, in this paper, we aim to enable reproducing error data efficiently for 3D NAND flash memory. We use a conditional generative adversarial network (cGAN) to learn the error distribution with multiple interferences and generate diverse error data comparable to the real-world. Evaluation results demonstrate it is feasible and efficient for error generation with cGAN.