Machine Learning-powered Compact Modeling of Stochastic Electronic
  Devices using Mixture Density Networks
Jack Hutchins Shamiul Alam Dana S. Rampini Bakhrom G. Oripov Adam N. McCaughan Ahmedullah Aziz
The relentless pursuit of miniaturization and performance enhancement in
electronic devices has led to a fundamental challenge in the field of circuit
design and simulation: how to accurately account for the inherent stochastic
nature of certain devices. While conventional deterministic models have served
as indispensable tools for circuit designers, they fall short when it comes to
capture the subtle yet critical variability exhibited by many electronic
components. In this paper, we present an innovative approach that transcends
the limitations of traditional modeling techniques by harnessing the power of
machine learning, specifically Mixture Density Networks (MDNs), to faithfully
represent and simulate the stochastic behavior of electronic devices. We
demonstrate our approach to model heater cryotrons, where the model is able to
capture the stochastic switching dynamics observed in the experiment. Our model
shows 0.82% mean absolute error for switching probability. This paper marks a
significant step forward in the quest for accurate and versatile compact
models, poised to drive innovation in the realm of electronic circuits.