Abstract
Memory technologies are central to modern computing systems, performing essential functions that range from primary data storage to advanced tasks, such as in-memory computing for artificial intelligence (AI) and machine learning (ML) applications. Initially developed solely for data retention, these technologies are evolving to support new paradigms, such as in-memory computing, where processing occurs directly within the memory array. This evolution significantly enhances computational efficiency by minimizing data transfer between processors and memory, resulting in increased speed and reduced energy consumption, critical factors for AI and ML workloads. Such demanding requirements are driving innovations beyond traditional complementary metal-oxide semiconductor (CMOS) technologies. Emerging nonvolatile memories (eNVMs) represent a promising class of technologies designed to replace or augment conventional volatile memories, such as random-access memory (RAM). Unlike RAM, which loses stored information when the power is disconnected, eNVMs maintain data integrity during power interruptions and system shutdowns. This review examines a range of emerging memory materials and device architectures, including resistive random-access memories (ReRAMs), magnetic random-access memories (MRAMs), ferroelectric random-access memories (FeRAMs), and phase-change memories (PCMs). Additionally, novel eNVMs based on two-dimensional (2D) and organic materials are explored, along with a discussion of the transition from digital to synaptic computing and the potential it offers to address significant technological barriers that may impede the use of AI in accelerating discovery. The discussion encompasses a comprehensive analysis of technological advancements, current development trajectories, and the challenges that still need to be addressed.