MoS2 Channel‐Enhanced High‐Density Charge Trap Flash Memory and Machine Learning‐Assisted Sensing Methodologies for Memory‐Centric Computing Systems

Abstract Driven by the shift of artificial intelligence (AI) workloads to edge devices, there is a growing demand for nonvolatile memory solutions that offer high‐density, low‐power consumption, and reliability. However, well‐established 3D NAND Flash using polycrystalline Si (Poly‐Si) channel encounters bottlenecks in increasing bit density due to short‐channel effects and cell‐current limitations. This study investigates molybdenum disulfide (MoS 2 ) as an alternative channel material for 3D NAND Flash cells. MoS 2 ’s low bandgap facilitates hole‐injection‐based erase, achieving a broader memory window at moderate voltages. Furthermore, adopting a low‐ k (≈2.2) tunneling layer improves the gate‐coupling ratio, reducing program/erase voltages and enhancing reliability, with endurance up to 10 4 cycles and retention of 10 5 s. Comprehensive analyses, including thickness‐dependent MoS 2 electrical measurements, temperature‐dependent conduction studies, and Technology Computer‐Aided Design (TCAD) simulations, elucidate the relationship between channel thickness and reliability metrics such as endurance and retention. Furthermore, deep reinforcement learning–driven Berkeley Short‐channel IGFET Model (BSIM) parameter calibration enables seamless integration of the MoS 2 model with a fabricated page‐buffer chip, allowing circuit‐level verification of sensing margins. This methodology can be applicable to new channel materials for next‐generation memory devices. These results demonstrate that MoS 2 ‐based nonvolatile memory effectively meets high‐density, low‐power, and reliable storage needs, presenting a promising solution for AI‐centric edge computing.