# Machine Learning-powered Compact Modeling of Stochastic Electronic Devices using Mixture Density Networks

arXiv: https://arxiv.org/abs/2311.05820 · HTML: https://mlsanity.com/paper/2311.05820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Cryogenic Neuromorphic Hardware](https://mlsanity.com/paper/2204.07503/) (2022)
- [Cryogenic Reconfigurable Logic with Superconducting Heater Cryotron: Enhancing Area Efficiency and Enabling Camouflaged Processors](https://mlsanity.com/paper/2306.10244/) (2023)
- [A Deep Dive into the Design Space of a Dynamically Reconfigurable Cryogenic Spiking Neuron](https://mlsanity.com/paper/2308.15754/) (2023)

## Built on by (most cited first, 1 of 1)

- [Embedding-Enhanced Probabilistic Modeling of Ferroelectric Field Effect Transistors (FeFETs)](https://mlsanity.com/paper/2508.02737/) (2025)

## Abstract (arXiv)

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.