A Unified Flash Memory Platform for Mode‐Adaptive and Robust AI Computation

ABSTRACT Computing‐in‐memory (CIM) architectures offer a promising route toward energy‐efficient artificial intelligence by reducing data‐movement overhead. However, most existing CIM hardware operates at a fixed trade‐off between accuracy, energy efficiency, and robustness, limiting adaptability to diverse workloads. Here, we present a dual‐mode CIM accelerator based on an AND‐type charge‐trap flash array that enables energy‐adaptive operation without device‐level structural modification. By integrating transistor‐mode current sensing and capacitor‐mode charge sensing in the same device structure, the proposed architecture allows flexible switching between high‐precision computation and ultra‐low‐power, noise‐resilient operation within a single hardware platform through peripheral switching associated with each sensing mode. Experimental results demonstrate reliable vector–matrix multiplication, hardware neural network inference, and strong tolerance to device and voltage variations. System‐level benchmarking further confirms improved energy efficiency and reduced peripheral overhead. This work establishes a practical and scalable CIM platform that dynamically balances performance and robustness, providing a versatile foundation for energy‐adaptive artificial intelligence (AI) hardware.