Triple‐Level Content‐Addressable Vertical NAND Flash Memory

Content-addressable memory (CAM) enables massively parallel search by directly comparing input data with stored entries, offering a powerful paradigm for accelerating data-intensive tasks. Here, we present a triple-level content-addressable vertical NAND flash memory (V-NAND TLCAM) that exploits the eight threshold-voltage states of commercial triple-level-cell (TLC) V-NAND flash devices to achieve compact, high-throughput, and energy-efficient in-memory search. A parity-split (PS) scheme, in which odd and even word-lines are accessed separately, enhances the sensing margin and mitigates worst-case errors while preserving storage density and supporting don't-care matching. Measurements on >100-layer TLC V-NAND flash memory confirm stable operation with up to a 24× improvement in read-operation throughput and a 54.4× reduction in the estimated read energy compared with conventional readout. Furthermore, few-shot learning simulations on the Omniglot dataset demonstrate that the proposed TLCAM maintains over 90% accuracy in 5-shot learning, approaching GPU-based cosine similarity baselines. These results highlight V-NAND TLCAM as a scalable and energy-efficient hardware platform for bridging device-level innovations with practical machine learning applications.