Towards the fundamental limits of energy, time, and noise in atomic memristors

Abstract

Approved for Public Release:The quest for energy-efficient information processing systems that emulate the brain s capabilities necessitates a deep understanding of the fundamental mechanisms governing the switching energy and time in atomic memristors (atomristors). Atomristors, a pioneering discovery in the realm of two-dimensional materials, offer intrinsic analog resistance-switching properties. This research aims to probe the intrinsic mechanism(s) governing the energetic and temporal dynamics at the atomic limit, with the objective of uncovering the minimum switching energy and time required to toggle a resistive state owing to a single atom adsorption event. The proposed research will delve into the fundamental understanding of charge trapping and detrapping phenomena in atomic monolayers. The approach combines rigorous experimental studies with theoretical guidance and computational modeling. Three main thrusts underpin the research activity, namely, research on the energetics, temporal dynamics, and noise phenomena. The energeticsaims to probe the fundamental limits of switching energy, involving both atomistic and macroscopic studies. The temporal dynamics will investigate the prospects of very high-speed switching exploring high diffusivity adatoms in monolayer materials. The noise studies seek to investigate the entropy inherent in the charge trapping dynamics in atomristors. The anticipated outcomes based on successful completion of this research is expected to significantly reduce switching energy and time in atomristors, pushing the boundaries towards high-speed energy-efficient artificial intelligence (AI) and machine learning systems beneficial to the Department of Defense (DoD) science and technology. Furthermore, the stochastic nature of the trapping/detrapping phenomena holds potential for applications in security science and technology. The proposed research offers transformative potential for the DoD, enabling advancementsin technology crucial for national security. The outcome of this research effort can significantly advance the understanding, efficiency and performance of electronic systems, particularly, neuromorphic computing, facilitating the development of advanced AI and analog computing systems. Such capabilities are pivotal for real-time analytics, edge and secure computing, autonomous systems, and advanced communication networks.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 24, 2024
Source ID
N000142412080

Entities

People

  • Deji Akinwande

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Texas at Austin

Tags

Fields of Study

  • Physics

Readers

  • Neural Network Machine Learning.
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - Autonomous System Control
  • Microelectronics