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

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.