Temporal versatility from intercalation-based neuromorphic devices exhibiting 150 mV non-volatile operation

Abstract

Memristors are a promising technology to surpass the limitations of the current silicon complementary metal-oxide-semiconductor architecture via the realization of neuromorphic computing. Here, we demonstrate intercalation-based non-volatile lithium niobite (Li1 – xNbO2) memristors for highly scalable, efficient, and dense neuromorphic circuitry. Volatile, semi-volatile, and non-volatile operation is achieved using a single material, where each operational mode provides a timescale that enables short-term, medium-term, and long-term memory in conjunction with computation-in-memory. The two-terminal non-volatile devices exhibit conductance changes of up to ∼2000% and have inherent non-binary operations proportional to flux linkage, allowing for analog neuromorphic functions mimicking synaptic weight updates. It is shown that Li1 – xNbO2 devices are highly scalable due to the intercalation-based mechanism, with non-volatile operation requiring a mere 150 mV for a 4 μm2 device, the lowest reported operating voltage for an inorganic non-volatile memristor. The programming voltage scales linearly with device size, projecting millivolt operation and attojoule energy consumption for nanoscale devices.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 24, 2020
Source ID
10.1063/1.5138193

Entities

People

  • Aheli Ghosh
  • Alex S. Weidenbach
  • Bill Zivasatienraj
  • M Brooks Tellekamp
  • Timothy M. Mccrone
  • W. Alan Doolittle

Organizations

  • Air Force Office of Scientific Research
  • Georgia Tech

Tags

Fields of Study

  • Materials science

Readers

  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.

Technology Areas

  • Biotechnology
  • Microelectronics