The Combination of Metal Oxides as Oxide Layers for RRAM and Artificial Intelligence
It addresses the challenge of enhancing RRAM devices for next-generation memory and AI hardware, but it is incremental as it reviews existing advances rather than presenting new results.
This review paper tackles the integration of metal oxides-based resistive random-access memory (RRAM) with artificial intelligence (AI), highlighting how AI can optimize RRAM performance and RRAM can serve as a hardware accelerator for AI and neuromorphic computing.
Resistive random-access memory (RRAM) is a promising candidate for next-generation memory devices due to its high speed, low power consumption, and excellent scalability. Metal oxides are commonly used as the oxide layer in RRAM devices due to their high dielectric constant and stability. However, to further improve the performance of RRAM devices, recent research has focused on integrating artificial intelligence (AI). AI can be used to optimize the performance of RRAM devices, while RRAM can also power AI as a hardware accelerator and in neuromorphic computing. This review paper provides an overview of the combination of metal oxides-based RRAM and AI, highlighting recent advances in these two directions. We discuss the use of AI to improve the performance of RRAM devices and the use of RRAM to power AI. Additionally, we address key challenges in the field and provide insights into future research directions