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Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
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The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
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Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
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Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions.

Ran Cheng1,2, Uday S Goteti3, Harrison Walker2,4

  • 1Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States.

Frontiers in Neuroscience
|November 25, 2021
PubMed
Summary
This summary is machine-generated.

Superconducting quantum phase slip junctions (QPSJs) show promise for energy-efficient neuromorphic circuits. These junctions can be used to create artificial neurons and synapses, enabling new possibilities for machine learning applications.

Keywords:
Josephson junctioncoupled synapse networksneuromorphic computingquantum phase slip junctionspike timing dependent plasticityunsupervised learning

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Area of Science:

  • Quantum Computing
  • Neuromorphic Engineering
  • Materials Science

Background:

  • Superconducting quantum phase slip junctions (QPSJs) are electromagnetic duals to Josephson Junctions (JJs).
  • QPSJs offer desirable properties for neuromorphic circuits, including ultra-low energy consumption, high speed, and natural spiking responses.
  • Their straightforward fabrication facilitates dense integration for complex neuromorphic systems.

Purpose of the Study:

  • To explore the application of QPSJs in neuromorphic circuits for machine learning.
  • To investigate both QPSJ-only and hybrid QPSJ + JJ circuit designs.
  • To develop and simulate learning mechanisms within QPSJ-based circuits.

Main Methods:

  • Simulations of QPSJ-only and hybrid QPSJ + JJ circuits for artificial synapses, neurons, and fan-in/fan-out functionalities.
  • Design and simulation of learning circuits implementing a simplified spike-timing dependent plasticity rule.
  • Exploration of QPSJ-based charge islands for programmable weights and non-volatile memory states.

Main Results:

  • Demonstrated the feasibility of QPSJ-based circuits for artificial neurons, synapses, and interconnections.
  • Successfully simulated learning circuits exhibiting spike-timing dependent plasticity.
  • Showcased an alternative approach using coupled QPSJ charge islands to achieve programmable weights and non-volatile memory states.

Conclusions:

  • QPSJs are a promising candidate for building energy-efficient and high-performance neuromorphic circuits.
  • QPSJ-based circuits can implement essential learning mechanisms like spike-timing and rate-dependent plasticity.
  • The proposed QPSJ charge island approach offers a pathway to overcome challenges and realize complex, learning-capable neuromorphic systems.