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The cultivation of environmental microorganisms has long been hindered by the inability to replicate complex native conditions in vitro. The isolation chip (iChip) addresses this limitation by facilitating the growth of previously uncultivable microorganisms through in situ incubation. Designed for high-throughput microbial cultivation, the iChip comprises hundreds of microchambers, each capable of housing a single microbial cell. These microchambers are loaded with a mixture of molten agar and...
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Emulating learning behavior in a flexible device with self-formed Ag dewetted nanostructure as active element.

Bhupesh Yadav1, Indrajit Mondal1, Bharath Bannur1

  • 1Chemistry & Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore-560064, India.

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Summary

This study introduces a flexible, self-formed silver-based neuromorphic device. This device mimics brain functions like synaptic plasticity in both flat and bent states, enabling applications in flexible electronics.

Keywords:
classical conditioningflexibleneuromorphicquantum conductancesynaptic functionalities

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Traditional von Neumann architecture faces limitations in AI.
  • Neuromorphic devices offer high-speed, efficient, low-power AI.
  • Flexible electronics are crucial for robotics and wearables.

Purpose of the Study:

  • To develop a flexible neuromorphic device.
  • To emulate brain-inspired synaptic activities in flexible devices.
  • To assess device performance in both flat and bent states.

Main Methods:

  • Fabrication of a self-formed Ag-based neuromorphic device.
  • Testing of synaptic plasticity (STP and LTP) emulation.
  • Evaluation of quantum conductance jumps.
  • Assessment of device switching, endurance, and classical conditioning capabilities.

Main Results:

  • The flexible device successfully emulated STP and LTP in both flat and bent states.
  • Observed half and full-integer quantum conductance jumps.
  • Demonstrated excellent switching and endurance characteristics.
  • Successfully emulated classical conditioning even in the bent state.

Conclusions:

  • Flexible neuromorphic devices can effectively emulate brain functions.
  • The developed Ag-based device shows potential for flexible electronics applications.
  • Device performance is maintained under mechanical strain, broadening application scope.