SK hynix 公佈用於 AI 運算的 3D FeNAND 技術

(Source: SK hynix)

SK hynix has developed next-generation memory technology that performs artificial intelligence (AI) computations directly within NAND flash memory.

The company disclosed its research on analog computing-in-memory (A-CiM) using ferroelectric three-dimensional NAND (3D FeNAND) in a newsroom post on Oct. 14. The findings were published in a paper by the Institute of Electrical and Electronics Engineers (IEEE).

A-CiM performs computations directly inside memory using analog signals. It executes multiply-accumulate (MAC) operations, the fundamental building blocks of AI computing, within memory. By reducing data movement, the technology improves processing speed and power efficiency.

3D FeNAND stores data and controls current using the polarization states of ferroelectric materials. Conventional 3D NAND uses a charge-trap nitride (CTN) structure that traps electrons in a nitride layer. Ferroelectric materials retain their internal polarization even after an external electric field is removed.

According to SK hynix, FeNAND can produce multiple current states within a single cell, making it suitable for representing the weights required in AI computations.

SK hynix researchers co-optimized three areas — cells, non-cell circuits and pages — to improve FeNAND’s computing performance. Simulations showed that 3D FeNAND-based A-CiM achieved up to 20 times higher throughput and 5,000 times greater energy efficiency than conventional nonvolatile memory.

At the cell level, current states are distinguished by adjusting the number of programming pulses. Conventional NAND changes cell states by increasing voltage, while FeNAND increases the number of pulses at a constant voltage.

At the non-cell circuit level, researchers reduced wordline resistance. Wordlines select and control memory cells. The SK hynix team introduced air gaps between wordlines.

At the page level, researchers optimized data placement and computation sequences. They applied in-page pipelining (IPP) to efficiently execute AI neural network computations within a single page.