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  3. Ferroelectric Dynamic-field-driven Nucleation And Growth Model For Predictive Materials-to-circuit Co-design.
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  3. Ferroelectric Dynamic-field-driven Nucleation And Growth Model For Predictive Materials-to-circuit Co-design.

Related Experiment Video

A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy
10:40

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Published on: April 8, 2018

Ferroelectric Dynamic-Field-Driven Nucleation and Growth Model for Predictive Materials-To-Circuit Co-Design.

Yi Liang1,2, Soohyeon Kim3, Tony Chiang1,2

  • 1Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA.

Advanced Materials (Deerfield Beach, Fla.)
|June 13, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Existing ferroelectric switching models fail under real operating conditions. A new dynamic-field-driven nucleation and growth (DFNG) model accurately captures ferroelectric switching dynamics under arbitrary voltage waveforms.

Keywords:
ferroelectricnucleation and growthswitching

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Electrical Engineering

Background:

  • Standard ferroelectric switching models (Kolmogorov-Avrami-Ishibashi, nucleation-limited switching) assume constant electric fields, which is unrealistic for real devices.
  • Real ferroelectric devices operate under mixed and distorted time-varying voltages, rendering current models inadequate for accurate interpretation of switching dynamics.

Purpose of the Study:

  • Introduce a novel compact dynamic-field-driven nucleation and growth (DFNG) model.
  • Enable quantitative analysis of ferroelectric switching transients under arbitrary voltage waveforms.
  • Facilitate predictive materials-circuit co-design for next-generation ferroelectric technologies.

Main Methods:

  • Developed a compact dynamic-field-driven nucleation and growth (DFNG) model.
  • Applied the DFNG model to fit switching transients across multiple ferroelectric materials.
  • Coupled the DFNG model with application-related waveforms and a circuit-level simulation platform.

    • Main Results:

    • The DFNG model enables quantitative fits to switching transients, extracting time-varying domain wall velocity and growth dimensionality.
    • The model successfully operates under arbitrary voltage waveforms, overcoming limitations of previous frameworks.
    • Demonstrated linking of nucleation and growth parameters to device performance metrics like memory window, disturb error, speed, and energy dissipation.

    Conclusions:

    • The DFNG model provides a robust framework for understanding and predicting ferroelectric switching dynamics under realistic operating conditions.
    • This model is crucial for advancing materials-circuit co-design, optimizing performance for next-generation ferroelectric devices.
    • The developed model enhances the predictive capability for ferroelectric device behavior across diverse applications and operating scales.