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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Computational Science

Background:

  • Miniaturization and performance demands in electronics necessitate accurate modeling of device variability.
  • Conventional deterministic models fail to capture the stochastic nature of many electronic components.
  • Variability in electronic components presents a significant challenge for circuit design and simulation.

Purpose of the Study:

  • To develop an innovative approach for modeling the stochastic behavior of electronic devices.
  • To overcome the limitations of traditional deterministic modeling techniques.
  • To enhance the accuracy and versatility of compact models for electronic circuits.

Main Methods:

  • Utilized machine learning, specifically Mixture Density Networks (MDNs).
  • Applied MDNs to represent and simulate the stochastic dynamics of electronic devices.
  • Demonstrated the approach on heater cryotrons to model their switching behavior.

Main Results:

  • The developed model successfully captured the stochastic switching dynamics of heater cryotrons.
  • Achieved a mean absolute error of 0.82% for switching probability.
  • Validated the effectiveness of MDNs in simulating device variability.

Conclusions:

  • The MDN-based approach offers a significant advancement in modeling stochastic electronic device behavior.
  • This method provides accurate and versatile compact models for improved circuit simulation.
  • The findings pave the way for enhanced innovation in electronic circuit design.