Fluid Filled Structure With Multiple Composite Compartments Subjected To Low Velocity Impact

Abstract

Inspired by biological neural systems, neuromorphic devices may open up new computing paradigms to explore cognition, learning and limits of parallel computation. Here we report the demonstration of a synaptic transistor with SmNiO3, a correlated electron system with insulator metal transition temperature at 130 deg C in bulk form. Non-volatile resistance and synaptic multilevel analogue states are demonstrated by control over composition in ionic liquid-gated devices on silicon platforms. The extent of the resistance modulation can be dramatically controlled by the film microstructure. By simulating the time difference between postneuron and preneuron spikes as the input parameter of a gate bias voltage pulse, synaptic spike-timing-dependent plasticity learning behaviour is realized. The extreme sensitivity of electrical properties to defects in correlated oxides may make them a particularly suitable class of materials to realize artificial biological circuits that can be operated at and above room temperature and seamlessly integrated into conventional electronic circuits.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2018
Accession Number
AD1059768

Entities

People

  • Joshua D. Bowling

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Ground and Sea Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Carbon Fiber Reinforced Polymer
  • Carbon Fibers
  • Composite Materials
  • Composite Structures
  • Computer-Aided Design
  • Containers
  • Couplings
  • Data Acquisition
  • Dynamic Response
  • Engineering
  • Fabrication
  • Fibers
  • Frequency
  • Laminates
  • Materials
  • Materials Processing
  • Materials Testing
  • Mechanics
  • Numerical Analysis
  • Resonant Frequency
  • Strain Gages
  • Test Equipment
  • Vibration

Readers

  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.
  • Reinforced Composite Materials

Technology Areas

  • Microelectronics
  • Microelectronics - Graphene