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.