Ferroelectric dynamic-field-driven nucleation and growth model for predictive materials-to-circuit co-design

TL;DR

This paper introduces a dynamic-field-driven nucleation and growth model for ferroelectric switching that accurately fits transient data under real voltage conditions, enabling predictive materials-to-circuit co-design.

Contribution

The novel DFNG model accounts for arbitrary voltage waveforms, allowing realistic simulation of ferroelectric switching dynamics in device and circuit design.

Findings

The model fits switching transients across multiple ferroelectric materials.

It extracts time-varying domain wall velocity and growth dimensionality.

It links nucleation and growth parameters to device performance metrics.

Abstract

Real ferroelectric devices operate under mixed and distorted time-varying voltages, yet the standard nucleation-growth frameworks used to interpret ferroelectric switching - most notably the Kolmogorov-Avrami-Ishibashi (KAI) and nucleation-limited switching models (NLS) - are derived under the critically limiting assumption of a constant electric field. Thus, the prevailing interpretation of ferroelectric switching dynamics fails under real operating conditions. Here we introduce a compact dynamic-field-driven nucleation and growth (DFNG) model that enables quantitative fits to switching transients across multiple ferroelectric materials to extract time-varying domain wall velocity and growth dimensionality, even under arbitrary voltage waveform. This capability then motivates its use in device modeling under complex signals spanning disparate time and frequency scales. Coupling the compact model to application-related waveforms and circuit-level simulation platform facilitates a predictive materials-circuit co-design framework by linking nucleation and growth parameters to memory window, disturb error, speed, and energy dissipation for next-generation ferroelectric technologies.