发表机构
Georgia Institute of Technology; Applied Materials; University of Modena and Reggio Emilia; Colorado State University; Purdue University; Samsung Electronics Co., Ltd.; University of California San Diego（佐治亚理工学院; 应用材料公司; 摩德纳和雷焦艾米利亚大学; 科罗拉多州立大学; 普渡大学; 三星电子株式会社; 加利福尼亚大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文探讨3D NAND闪存扩展瓶颈，指出其受电荷存储物理限制，主张应用特定协同优化而非单一器件路线图，以支撑AI时代存储需求。
AI 中文摘要
3D NAND闪存已成为人工智能时代的基础性非易失性存储平台，支撑着从模型训练到大规模推理和冷数据归档的各种工作负载。随着路线图现在瞄准千层堆叠和每个芯片数万亿个器件，扩展不再主要由工艺集成或光刻技术主导。相反，它日益受到电荷存储本身物理特性的限制：横向电荷迁移、静电耦合、读取干扰和传输限制正进入一个余量受限的机制。在这种机制下，它们的集体相互作用（而非任何单一机制）随着每一代产品压缩工作余量。在这篇展望文章中，我们审视这些汇聚的瓶颈，并主张维持NAND扩展将需要针对特定应用进行协同优化，而非单一化的器件路线图。在此框架下，传统电荷陷阱闪存、铁电存储、替代沟道材料和系统级集成应被视为针对数据中心计算不同层级的互补解决方案。
英文摘要
3D NAND flash has become the foundational non-volatile storage platform of the AI era, underpinning workloads from model training to large-scale inference and cold data archival. With roadmaps now targeting kilolayer stacks and tens of trillions of devices per die, scaling is no longer governed primarily by process integration or lithography. Instead, it is increasingly constrained by the physics of charge storage itself: lateral charge migration, electrostatic coupling, read disturb, and transport limitations are entering a margin-limited regime. In this regime, their collective interaction, not any single mechanism, compresses operating margins with each generation. In this Perspective, we examine these converging bottlenecks and argue that sustaining NAND scaling will require application-specific co-optimization rather than a monolithic device roadmap. In this framework, conventional charge-trap flash, ferroelectric storage, alternative channel materials, and system-level integration are best viewed as complementary solutions targeted to distinct tiers of data-centric computing.
