Machine Learning-powered Compact Modeling of Stochastic Electronic Devices using Mixture Density Networks
This addresses the problem of device variability for circuit designers, representing an incremental improvement over deterministic models.
The paper tackled the challenge of accurately modeling the stochastic behavior of electronic devices in circuit design by using Mixture Density Networks, achieving a 0.82% mean absolute error for switching probability in heater cryotrons.
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.