# Reverse Designing Ferroelectric Capacitors with Machine Learning-based Compact Modeling

## TL;DR

This paper presents two machine learning-based reverse-design algorithms for ferroelectric capacitors that efficiently identify device parameters to meet specific electrical performance goals, significantly reducing computational time.

## Contribution

Introduction of novel reverse-design algorithms leveraging machine learning-based compact models for ferroelectric capacitors, enabling rapid parameter optimization compared to traditional methods.

## Key findings

- Algorithms effectively identify device parameters for desired electrical characteristics.
- Machine learning models drastically reduce computation time over phase-field modeling.
- The methods maintain high accuracy while improving efficiency.

## Abstract

Machine learning-based compact models provide a rapid and efficient approach for estimating device behavior across multiple input parameter variations. In this study, we introduce two reverse-design algorithms that utilize these compact models to identify device parameters corresponding to desired electrical characteristics. The algorithms effectively determine parameter sets, such as layer thicknesses, required to achieve specific device performance criteria. Significantly, the proposed methods are uniquely enabled by machine learning-based compact modeling; alternative computationally intensive approaches, such as phase-field modeling, would impose impractical time constraints for iterative design processes. Our comparative analysis demonstrates a substantial reduction in computation time when employing machine learning-based compact models compared to traditional phase-field methods, underscoring a clear and substantial efficiency advantage. Additionally, the accuracy and computational efficiency of both reverse-design algorithms are evaluated and compared, highlighting the practical advantages of machine learning-based compact modeling approaches.