The objective of the proposed research is to engineer ferroelectric gate stacks for high-performance, high-density 3D NAND storage solutions. Artificial intelligence (AI) is generating unprecedented volumes of data, with machine-generated content surpassing human-generated data by more than 100-fold in 2025. Managing this data explosion requires advances in digital storage technologies that go beyond the limits of existing solutions. Traditional NAND flash memory, which plays a central role in sustaining AI data flows alongside high-bandwidth memory (HBM), is now approaching fundamental scaling barriers as devices near the 1000-layer milestone and exceed 40 trillion transistors. To continue meeting density, reliability, and efficiency requirements, new materials and architectures are essential. Hafnia-based ferroelectric materials present a promising pathway, offering fast switching, non-volatile states, and CMOS compatibility suitable for integration into 3D NAND. Recent progress in engineering ferroelectric field-effect transistors (FEFETs) demonstrates that challenges such as disturb and retention can be mitigated through material and device-level innovations. By engineering device performance and reliability metrics, we position ferroelectric NAND (FE-NAND) as a breakthrough solution to extend scaling beyond the limits of charge-trap NAND and provide the storage capabilities demanded by the AI-driven era.