SK hynix Unveils 3D FeNAND A-CiM Technology, Enabling AI Computing Directly Within NAND

As large-scale AI models continue to grow, the power consumption and latency associated with data movement are emerging as major bottlenecks in AI infrastructure. To address these challenges, SK hynix's research team focused on analog compute-in-memory(A-CiM), an architecture that combines data storage and computation, and proposed an approach based on 3D FeNAND that co-optimizes cell, non-cell, and page schemes.  3D NAND architecture incorporates ferroelectric properties, enabling analog computing directly within the memory structure.

Ferroelectric Material Properties: Low-Power Multi-Level Current Control via Polarization State

The research team identified 3D FeNAND as a memory architecture well suited for A-CiM. Commercial 3D NAND primarily uses a charge trap nitride(CTN) structure, which controls cell states by trapping or releasing electrons within a nitride layer. While this structure offers advantages in terms of density, it has limitations when it comes to precisely distinguishing the multiple current levels required for analog computing.

FeNAND, by contrast, controls cell current by leveraging changes in the electrical state of a ferroelectric material. Ferroelectric materials retain their internal polarization state even after an external electric field is removed. By changing this polarization state, data can be stored and the state of a memory cell can be controlled. These properties give FeNAND an advantage over conventional 3D CTN NAND in precisely controlling multiple current levels at a lower power.

Collaborative Optimization of Schemes: 10x to 20x Higher Throughput, 4,000x to 5,000x Higher Energy Efficiency

However, applying 3D FeNAND to A-CiM requires more than simply changing the cell structure alone. The cell, non-cell, and page schemes need to be co-optimized. To translate the potential of 3D FeNAND into practical A-CiM efficiency, current-level control at the cell level, word line switching overhead, and page utilization must be improved together within a single integrated architecture.

What distinguishes this research is SK hynix's holistic approach to optimizing the cell, non-cell, and page schemes together rather than improving each separately.

3D FeNAND can store more weights at once and use them directly for computation, reducing the need for reloading. The optimized 3D FeNAND architecture significantly improved throughput and energy efficiency compared with conventional 3D NAND-based architectures. Under the same chip-area constraints, it also delivered approximately 10 to 20 times higher throughput and around 4,000 to 5,000 times greater energy efficiency than conventional 3D arrays.  These gains reflect the high integration density of 3D FeNAND and the reduction in data movement achieved through co-optimization of the cell, non-cell, and page schemes.

NAND No Longer Just a "Warehouse": Potential to Evolve from Storage Medium to Computing Platform

With its high integration density, low power consumption, and multi-level control capabilities, 3D FeNAND-based A-CiM demonstrates the potential for NAND to expand beyond a storage medium into a computing medium. This study suggests that NAND could evolve beyond a storage medium into a computing platform. FeNAND based on ferroelectric materials demonstrated higher energy efficiency than conventional 2D nonvolatile memory arrays and 3D CTN NAND, supporting its potential as a low-power, high-efficiency A-CiM platform.

This approach could extend beyond AI computation to other data-centric computing applications, including in-memory search. From the perspective of developing an ultra-high-density, low-power in-memory computing platform for large-scale AI models, this research presents a new direction for the evolution of NAND technology.