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

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 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.

💡 Research Summary

The paper addresses a critical gap in ferroelectric device modeling: existing nucleation‑growth frameworks such as the Kolmogorov‑Avrami‑Ishibashi (KAI) model and the nucleation‑limited switching (NLS) model assume a constant electric field, which is rarely the case in real circuits where voltage waveforms are mixed, distorted, and span many time scales. To overcome this limitation, the authors develop a Dynamic‑Field‑Driven Nucleation‑and‑Growth (DFNG) model that explicitly incorporates the full voltage history into the switching dynamics.

The DFNG model builds on Cahn’s time‑cone concept. The transformed fraction X(t) is expressed as

 X(t)=1−exp