Evidence map›Paper›PMID 42287234›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2026

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

Yi Liang, Soohyeon Kim, Tony Chiang, Megan K Lenox, Ian Mercer, John J Plombon, Jon-Paul Maria, Jon F Ihlefeld, Wenhao Sun, Wei Lu and 1 more

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In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yi LiangDepartment of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0009-0002-8324-343X
Soohyeon KimDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA.
Tony ChiangDepartment of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Megan K LenoxDepartment of Materials Science and Engineering, University of Virginia, Charlottesville, Virginia, USA.
Ian MercerDepartment of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA.
John J PlombonTechnology Research, Intel Corporation, Hillsboro, Oregon, USA.
Jon-Paul MariaDepartment of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-3604-4761
Jon F IhlefeldDepartment of Materials Science and Engineering, University of Virginia, Charlottesville, Virginia, USA.ORCID https://orcid.org/0000-0003-0166-8136
Wenhao SunDepartment of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Wei LuDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA.
John T HeronDepartment of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA.

Funding

Intel Corporation FEINMAN 2.0 programONR grant N000142612047
6 · The paper itself

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 waveforms. 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 a 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.

Indexed as

ferroelectricnucleation and growthswitching

Identifiers

PMID42287234
PMCPMC13378263

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.