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REVIEW 3 major objections 5 minor 45 references

Domain-Growth Kinetics and Scaling Laws Governing Pulse-Driven Accumulative Polarization Switching in HZO

T0 review · 3 major / 5 minor · reviewed 2026-07-13 · grok-4.5

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Pith's one-line read Sub-coercive pulse trains reverse HZO polarization through three domain-growth regimes set by a local kinetic exponent.

desk verdict Useful α_local regime maps on top of the Saha TDLG/HZO model; late-time deceleration is partly finite-size, and nothing is experiment-checked, but the pulse-parameter organization is real and worth refereeing. read the letter →

arxiv 2607.05617 v2 pith:FUCBHNIT submitted 2026-07-06 cond-mat.mtrl-sci

Manish Anand , Balram Khattar , Abhishek Sharma This is my paper · ORCID
classification cond-mat.mtrl-sci
keywords HZOaccumulativepolarizationswitchingdomain-growthkineticslocalkineticexponentphase-fieldmodelsub-coercivepulsesferroelectricneuromorphicdevicesdomain-walldynamics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that accumulative polarization switching in ferroelectric HZO under repeated sub-coercive pulses is not a single process but proceeds through three kinetic regimes of domain growth. Using a continuum phase-field model, the authors track the effective switched-domain radius and define a local kinetic exponent that measures how that radius grows with pulse number. Early on the exponent exceeds one, meaning each pulse expands domains faster than the last; later it settles near one for steady growth and then falls below one as walls hit boundaries, switchable material runs out, and relaxation during pulse-off intervals pulls walls backward. The transitions are controlled by the tug-of-war between field-driven excitation while the pulse is on and spontaneous back-switching while it is off, and they depend strongly on the starting domain geometry. Raising pulse amplitude or on-time lengthens the superlinear stage; lengthening the off-time favors relaxation and slows accumulation. The result is a scaling framework that links microscopic wall motion to macroscopic polarization build-up, offering concrete design rules for low-power multi-level memories and neuromorphic synapses based on HZO.

What carries the argument

The local kinetic exponent α_local = d ln(R − R0)/d ln n, extracted from the effective switched-domain radius in a time-dependent Landau–Ginzburg phase-field model; it classifies superlinear, self-similar, and decelerating growth and maps how pulse parameters and starting domain layout move the system between those regimes.

What would settle it

Measure switched-domain radius versus pulse number on real HZO capacitors or FeFETs under matched sub-coercive pulse trains; if the extracted local exponent never exceeds one early on or never drops below one later, or if geometry and pulse-off time do not shift the transitions as predicted, the scaling framework fails.

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Extended reading notes

Core claim

The effective switched-domain radius R obeys distinct scaling regimes characterized by the local kinetic exponent α_local = d ln(R − R0)/d ln n: initially α_local > 1 (superlinear irreversible domain-wall growth under successive pulses), then α_local ≈ 1 (steady self-similar growth), then α_local < 1 (deceleration from geometric confinement, depletion of switchable polarization, and relaxation-induced back-switching). Transitions are set by competition between pulse-on excitation and pulse-off relaxation and by initial domain geometry.

Load-bearing premise

The claim rests on a two-dimensional continuum model with fixed material coefficients, polarization locked normal to the film, no thermal noise, and no defects, grains, or electrode interfaces, assumed sufficient to capture real nanoscale HZO kinetics.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This computational paper uses a 2D time-dependent Landau–Ginzburg phase-field model of HZO to study accumulative polarization switching under trains of sub-coercive pulses. By varying initial domain geometry (single and multi-domain placements), pulse amplitude Eapp_n,max, Ton, and Toff, the authors track average polarization, accumulated polarization, an effective switched-domain radius R (and multi-domain variants Rc, Req), and a local kinetic exponent α_local = d ln(R−R0)/d ln n. They report three regimes—α_local>1 (superlinear acceleration), ≈1 (steady self-similar growth), and <1 (deceleration from confinement, depletion, and off-time back-switching)—and attribute the transitions to competition between field-driven Type-III domain-wall advance during Ton and spontaneous relaxation during Toff. The abstract and conclusion present this as a unified scaling framework with design guidelines for HZO memory and neuromorphic devices.

Significance. If the three-regime picture is robust beyond the simulated box and idealized continuum model, the work would give a practical kinetic language for pulse-protocol design in accumulative ferroelectric switching, which is relevant to multi-level FeFET/FTJ and neuromorphic weight update. Strengths include a systematic parameter sweep across domain geometries and pulse parameters, mutual consistency among morphologies, Pacc(n), R(n), and α_local, and a clear microscopic taxonomy of excitation/relaxation pathways. The local-exponent diagnostic is a useful, falsifiable way to report nonequilibrium domain growth under pulsed drive. The main significance is therefore methodological and interpretive within continuum phase-field kinetics, not yet a validated device law for polycrystalline HZO.

major comments (3)
  1. [Model; Results Figs. 3d, 6, 11–12] Model §II and Results (Figs. 3d, 6, 11–12; L = 80 nm × 80 nm, Neumann boundaries): the late-time α_local < 1 branch is tightly tied to geometric confinement and depletion once the switched front approaches the box edge (explicitly stated for CeD/ED). Early α_local > 1 is likewise geometry-selective (free circular walls accelerate; QCoD stays near unity under identical pulses). The claim of a “unified scaling framework” and device design guidelines therefore requires either (i) a documented size-independence / continuum-limit check showing an intermediate α_local ≈ 1 window that survives larger L and different R0, or (ii) an explicit reframing that the regimes are finite-domain kinetic signatures of free radial expansion until confinement. The brief remark that other lateral sizes give “qualitatively similar” accumulation is not sufficient without R(n) and α_local(n) for at least two size
  2. [Abstract; §I; §IV] Abstract, §I motivation, and §IV conclusion assert “design guidelines for HZO-based memory and neuromorphic devices.” The model omits thermal noise, oxygen vacancies, grain boundaries, electrode interfaces, and 3D polycrystalline texture (Model, Eqs. 1–6; fixed Landau set α̂, β̂, γ̂ from prior calibration). Without quantitative comparison to measured accumulative switching (e.g., pulse-number dependence of remanent P or FeFET threshold shift under comparable Ton/Toff), or a clear statement of which predictions survive those omissions, the device-guideline claim overreaches the evidence. Either add a validation/benchmark section against published HZO pulse-train data or temper the claim to continuum-model design rules.
  3. [Eq. (7); Results Figs. 8, 10] Eq. (7) and multi-domain Results (Figs. 8, 10): R, Rc, and Req are central observables, but the manuscript does not specify the operational definition used to extract radius from the discrete polarization field (threshold on Pn, area-equivalent radius πR² = A_switched, perimeter-based, etc.), nor how α_local is differentiated numerically from discrete n (smoothing, window, handling of staircase Ton/Toff structure). For multi-domain coalescence, Req’s construction is especially load-bearing for the claimed cooperative acceleration. Please define R extraction and α_local computation precisely and show that the >1 / ≈1 / <1 classification is stable under reasonable threshold and smoothing choices.
minor comments (5)
  1. [Eq. (7); Figs. 3, 6, 11] Notation for the local exponent alternates between α_local, α_loc, and α loc across text and figure captions (e.g., Fig. 3d vs. Eq. 7). Standardize.
  2. [Results; Fig. 2] QCoD is defined as “quarter-corner domain” in the Results text but as “quasi-centre domain” in the Fig. 2 caption. Correct the inconsistency.
  3. [Figs. 2, 7, 9] Several figure panels are dense (e.g., multi-panel morphology sequences in Figs. 2, 7, 9). Adding scale bars, pulse-number labels on morphologies, and a short legend for color scale of Pn would improve readability.
  4. [Introduction; section headers; Fig. 2] Minor grammar/typos: “inducsed” (Introduction), “RESUL TS” / “SUMMAR Y” spacing artifacts, “Toff = 0.4µ.” incomplete unit in Fig. 2 caption, and mixed µs/ns units for the same quantities without a conversion note.
  5. [Model; Fig. 1] The Type-I/II/III excitation–relaxation taxonomy is useful but should be cross-referenced more clearly to the prior Saha et al. usage versus what is newly quantified here, so readers can separate nomenclature from new kinetic results.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: α_local is an observed diagnostic computed from simulated R(n), not an input or fitted target; regimes emerge from TDLG dynamics.

full rationale

The paper solves a standard time-dependent Landau–Ginzburg phase-field model (Eqs. 1–6) with literature-calibrated Landau coefficients taken from an independent prior work (Saha et al., not co-authored by the present authors). It then defines the local kinetic exponent α_local ≡ d log(R-R0)/d log n purely as a post-processing diagnostic on the simulated domain radius R(n) and reports the three regimes (α_local >1, ≈1, <1) that appear under variation of initial geometry and pulse parameters. Nothing in the derivation chain equates an output to an input by construction, renames a fitted quantity as a prediction, or rests a uniqueness claim on a self-citation. The scaling classification is therefore an independent numerical observation within the model, not a circular restatement of its premises. Finite-size or geometry dependence of the late-time branch is a possible correctness concern, not circularity.

Assumptions & free parameters 4 free parameters · 6 assumptions · 2 invented entities

The central claim is a simulation phenomenology claim. It rests on standard continuum ferroelectric dynamics plus a small set of calibrated material numbers and modeling idealizations imported from prior HZO phase-field work. No new particles or forces are invented; the Type-I/II/III language and α_local diagnostic are organizational tools. Free parameters that move quantitative thresholds are the Landau set, gradient/viscosity scales, and the chosen pulse and geometry ensembles.

free parameters (4)
  • Normalized Landau coefficients α̂, β̂, γ̂ = −1.499, +0.498, 0.001
    Fixed at −1.499, +0.498, 0.001 by calibration to prior HZO phase-field work; they set the free-energy barrier and thus all switching thresholds.
  • Normalized gradient coefficient Kn_P and viscosity ρn
    Control domain-wall width/mobility and absolute time scale; numerical values are not fully tabulated in the text but are required for any quantitative R(n).
  • Simulation lateral size L = 80 nm × 80 nm = 80 nm × 80 nm
    Chosen for computational convenience; finite-size confinement directly produces the late-stage α_local < 1 regime the paper reports.
  • Pulse protocol set {Eapp_n,max, Ton, Toff} and initial domain radii/positions = Eapp_n,max ~0.6–1.2; Ton ~0.1–0.2 μs; Toff ~0.3–0.7 μs (representative)
    Hand-chosen scan ranges that define where regime transitions appear; not fitted to new experiments in this paper.
assumptions (6)
  • domain assumption Polarization dynamics obey the time-dependent Landau–Ginzburg equation with a sixth-order Landau free energy plus gradient and −E·P terms.
    Model section Eqs. (1)–(6); standard continuum ferroelectric modeling assumption.
  • domain assumption Only the out-of-plane polarization component is nonzero (Px = Py = 0, Pz ≠ 0).
    Stated explicitly after Eq. (2); reduces the problem to a scalar 2D field.
  • domain assumption Neumann boundary conditions at the edges of a finite 2D square adequately represent the FE grain for domain-growth kinetics.
    Model section finite-element paragraph; shapes late-stage confinement.
  • ad hoc to paper Thermal fluctuations, oxygen vacancies, grain boundaries, and electrode interfacial layers can be omitted for the kinetic regimes of interest.
    Not included in F or the evolution equation; real HZO devices are known to be defect- and interface-sensitive.
  • domain assumption Complete local switching occurs when |Eapp_n + Eint_n| > 1 in normalized units.
    Model section following Eq. (6), following Saha et al.
  • standard math Domain growth under pulse trains can be summarized by a power-law local exponent α_local = d log(R−R0)/d log n in the spirit of phase-ordering kinetics.
    Eq. (7) and citations to Bray, Grest et al., Barabási–Stanley; standard local-slope diagnostic.
invented entities (2)
  • Local kinetic exponent α_local for pulse-driven accumulative FE switching
    purpose: Instantaneous diagnostic that partitions superlinear, self-similar, and decelerating domain-growth regimes under discrete pulse trains.
    Defined in Eq. (7) for this problem; local exponents exist in growth literature, but this application and regime map are paper-specific. Independent evidence would be experimental R(n) or switched-area vs pulse number showing the same α_local transitions.
  • Type-I / Type-II / Type-III excitation and relaxation taxonomy (as used here)
    purpose: Classify soft dielectric charging vs irreversible domain-wall advance/back-switch under sub-coercive pulses.
    Adopted from Saha et al. and used throughout Results; not independently re-derived. Experimental handles exist in the broader literature but are not newly demonstrated here.

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Pith. "Pith review of Domain-Growth Kinetics and Scaling Laws Governing Pulse-Driven Accumulative Polarization Switching in HZO." pith.science (2026). https://pith.science/paper/FUCBHNIT

@misc{pith2026260705617,
  author       = {Pith},
  title        = {Pith review of: Domain-Growth Kinetics and Scaling Laws Governing Pulse-Driven Accumulative Polarization Switching in HZO},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FUCBHNIT}},
  note         = {Machine review of arXiv:2607.05617}
}
read the original abstract

Accumulative polarization switching driven by sequential sub-coercive electric-field pulses offers a promising route toward low-power ferroelectric memories and neuromorphic devices. However, the kinetic regimes governing this nonequilibrium process remain poorly understood. Here, we employ a phase-field model based on the time-dependent Landau-Ginzburg formalism to investigate pulse-driven accumulative switching in ferroelectric HZO. By systematically varying the initial domain configuration, pulse amplitude, pulse-on time, and pulse-off time, we establish a quantitative link between microscopic domain-wall dynamics and macroscopic polarization accumulation. We show that the effective switched-domain radius follows distinct scaling regimes characterized by the local kinetic exponent. Initially, a local exponent greater than 1 indicates superlinear domain growth driven by enhanced irreversible domain-wall propagation under successive pulses. As switching progresses, a local exponent close to unity marks steady self-similar growth, whereas a local exponent less than 1 signifies decelerating dynamics caused by geometric confinement, depletion of switchable polarization, and relaxation-induced back switching. The transition between these regimes is governed by the competition between field-driven excitation during the pulse-on interval and spontaneous relaxation during the pulse-off interval. The initial domain geometry further influences this transition. Increasing the pulse amplitude or pulse-on duration extends the superlinear regime, whereas longer pulse-off times promote relaxation and suppress accumulation. These findings establish a unified scaling framework for pulse-driven accumulative switching, providing quantitative insight into nonequilibrium ferroelectric domain evolution and design guidelines for HZO-based memory and neuromorphic devices.

Figures

Figures reproduced from arXiv: 2607.05617 by the authors.

Figure 1
Figure 1. FIG. 1. The variation of [PITH_FULL_IMAGE:figures/full_fig_p027_1.png] view at source ↗
Figure 1
Figure 1. FIG. 1. The variation of [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Pulse-driven accumulative polarization switching in HZO for four distinct domain con [PITH_FULL_IMAGE:figures/full_fig_p028_2.png] view at source ↗
Figures from the paper (21 more)
Figure 2
Figure 2. Figure 2: FIG. 2. Pulse-driven accumulative polarization switching in HZO for four distinct domain configurations: quasi-centre domain [PITH_FULL_IMAGE:figures/full_fig_p006_2.png]
Figure 3
Figure 3. Figure 3: FIG. 3. Comparative analysis of pulse-driven polarization accumulation kinetics for four FE [PITH_FULL_IMAGE:figures/full_fig_p029_3.png]
Figure 3
Figure 3. Figure 3: FIG. 3. Comparative analysis of pulse-driven polarization accumulation kinetics for four FE systems with different initial [PITH_FULL_IMAGE:figures/full_fig_p007_3.png]
Figure 4
Figure 4. Figure 4: FIG. 4. (a) Temporal evolution of the normalized polarization [PITH_FULL_IMAGE:figures/full_fig_p030_4.png]
Figure 4
Figure 4. Figure 4: FIG. 4. (a) Temporal evolution of the normalized polarization [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Evolution of the effective switched-domain radius [PITH_FULL_IMAGE:figures/full_fig_p031_5.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Evolution of the effective switched-domain radius [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. Evolution of the local growth exponent [PITH_FULL_IMAGE:figures/full_fig_p032_6.png]
Figure 6
Figure 6. Figure 6: FIG. 6. Evolution of the local growth exponent [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. (a) Temporal evolution of [PITH_FULL_IMAGE:figures/full_fig_p033_7.png]
Figure 7
Figure 7. Figure 7: FIG. 7. (a) Temporal evolution of [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8. (a, b) Temporal evolution of the central-domain radius [PITH_FULL_IMAGE:figures/full_fig_p034_8.png]
Figure 8
Figure 8. Figure 8: FIG. 8. (a, b) Temporal evolution of the central-domain radius [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: FIG. 9. (a) Temporal evolution of [PITH_FULL_IMAGE:figures/full_fig_p035_9.png]
Figure 9
Figure 9. Figure 9: FIG. 9. (a) Temporal evolution of [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
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Figure 10. Figure 10: FIG. 10. (a,b) Temporal evolution of [PITH_FULL_IMAGE:figures/full_fig_p036_10.png]
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Figure 10. Figure 10: FIG. 10. (a,b) Temporal evolution of [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
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Figure 11. Figure 11: FIG. 11. Evolution of the local kinetic exponent [PITH_FULL_IMAGE:figures/full_fig_p037_11.png]
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Figure 11. Figure 11: FIG. 11. Evolution of the local kinetic exponent [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
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Figure 12. Figure 12: FIG. 12. Two-dimensional kinetic maps of [PITH_FULL_IMAGE:figures/full_fig_p038_12.png]
Figure 12
Figure 12. Figure 12: FIG. 12. Two-dimensional kinetic maps of [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]

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Works this paper leans on

45 extracted references

  1. [1]

    G. Xu, J. Wen, C. Stock, and P. Gehring, Phase instabil- ity induced by polar nanoregions in a relaxor ferroelectric system, Nature materials7, 562 (2008)

    2008

  2. [2]

    Segatto, D

    M. Segatto, D. Lizzit, and D. Esseni, An lgd model with extrinsic nucleations for polarization dynamics in fer- roelectric materials and devices, Scientific Reports15, 19619 (2025)

    2025

  3. [3]

    L. Jiao, Z. Zhou, Z. Zheng, X. Wang, J. Xie, D. Zhang, Q. Kong, Y. Feng, C. Sun, G. Liang,et al., Temperature- dependent two-phase switching of the hfo 2-based ferro- electric polarization, IEEE Transactions on Electron De- vices (2025)

    2025

  4. [4]

    Koduru, A

    R. Koduru, A. K. Saha, M. M. Frank, and S. K. Gupta, Small-signal capacitance in ferroelectric hafnium zirco- nium oxide: mechanisms and physical insights, Nanoscale 17, 6154 (2025)

    2025

  5. [5]

    Kumar, S

    R. Kumar, S. Zhang, and P. Ganesh, Modeling kinetic ef- fects of charged vacancies on electromechanical responses of ferroelectrics: Rayleighian approach, Physical Review Research7, 013059 (2025)

    2025

  6. [6]

    Wang and T.-Y

    J. Wang and T.-Y. Zhang, Size effects in epitaxial ferroelectric islands and thin films, Physical Review B—Condensed Matter and Materials Physics73, 144107 (2006)

    2006

  7. [7]

    Gaddam, G

    V. Gaddam, G. Kim, T. Kim, M. Jung, C. Kim, and S. Jeon, Novel approach to highκ( 59) and low eot ( 3.8 ˚ a) near the morphotrophic phase boundary with afe/fe (zro2/hzo) bilayer heterostructures and high-pressure an- nealing, ACS applied materials & interfaces14, 43463 (2022)

    2022

  8. [8]

    Chen, Y.-S

    H.-Y. Chen, Y.-S. Jiang, C.-H. Chuang, C.-L. Mo, T.-Y. Wang, H.-C. Lin, and M.-J. Chen, Impact of asymmetric electrodes on ferroelectricity of sub-10 nm hzo thin films, Nanotechnology35, 105201 (2024)

    2024

Show all 45 references
  1. [9]

    E. Yu, X. Lyu, M. Si, P. D. Ye, and K. Roy, Interfa- cial layer engineering in sub-5-nm hzo: Enabling low- temperature process, low-voltage operation, and high ro- bustness, IEEE Transactions on Electron Devices70, 2962 (2023). 20

    2023

  2. [10]

    J. Yoo, H. Song, H. Lee, S. Lim, S. Kim, K. Heo, and H. Bae, Recent research for hzo-based ferroelectric mem- ory towards in-memory computing applications, Elec- tronics12, 2297 (2023)

    2023

  3. [11]

    Wu and C.-Y

    Y.-C. Wu and C.-Y. Wei, Review of recent hzo-based ferroelectric transistors for non-volatile memory applica- tions, IEEE Electron Devices Reviews (2025)

    2025

  4. [12]

    Y. Qin, V. Gaddam, T. Jung, and S. Jeon, Hzo (¿ 10 nm) films for achieving high-κnear morphotropic phase boundary at low-temperature furnace annealing process, IEEE Transactions on Electron Devices71, 5618 (2024)

    2024

  5. [13]

    A. Jan, T. Rembert, S. Taper, J. Symonowicz, N. Strkalj, T. Moon, Y. S. Lee, H. Bae, H. J. Lee, D.-H. Choe, et al., In operando optical tracking of oxygen vacancy migration and phase change in few nanometers ferro- electric hzo memories, Advanced functional materials33, 2214970 (2023)

    2023

  6. [14]

    Khattar, A

    B. Khattar, A. Tripathi, M. Brahma, and A. Sharma, Asymmetric resonant ferroelectric tunnel junctions for simultaneous high tunnel electroresistance and low resistance-area product, APL Electronic Devices1 (2025)

    2025

  7. [15]

    A. K. Saha, K. Ni, S. Dutta, S. Datta, and S. Gupta, Phase field modeling of domain dynamics and polariza- tion accumulation in ferroelectric hzo, Applied Physics Letters114(2019)

    2019

  8. [16]

    K. Ni, B. Grisafe, W. Chakraborty, A. Saha, S. Dutta, M. Jerry, J. Smith, S. Gupta, and S. Datta, In-memory computing primitive for sensor data fusion in 28 nm hkmg fefet technology, in2018 IEEE International Elec- tron Devices Meeting (IEDM)(IEEE, 2018) pp. 16–1

    2018

  9. [17]

    Mulaosmanovic, T

    H. Mulaosmanovic, T. Mikolajick, and S. Slesazeck, Ac- cumulative polarization reversal in nanoscale ferroelectric transistors, ACS applied materials & interfaces10, 23997 (2018)

    2018

  10. [18]

    Gao, Y.-C

    J. Gao, Y.-C. Chien, J. Huo, L. Li, H. Zheng, H. Xi- ang, and K.-W. Ang, Reconfigurable neuromorphic func- tions in antiferroelectric transistors through coupled po- larization switching and charge trapping dynamics, Na- ture Communications16, 4368 (2025)

    2025

  11. [19]

    Jiang, C

    S. Jiang, C. Wu, J. Sun, S. Xu, S. Li, Y. Lai, N. Wang, H. Guo, J. Qiu, and Y. Li, Ferroelectric memcapaci- tive dynamics from nanoseconds to milliseconds for bio- inspired neuromorphic computing and control, Journal of Materials Chemistry C14, 5196 (2026)

    2026

  12. [20]

    Mulaosmanovic, E

    H. Mulaosmanovic, E. Chicca, M. Bertele, T. Mikola- jick, and S. Slesazeck, Mimicking biological neurons with a nanoscale ferroelectric transistor, Nanoscale10, 21755 (2018)

    2018

  13. [21]

    Dutta, C

    S. Dutta, C. Schafer, J. Gomez, K. Ni, S. Joshi, and S. Datta, Supervised learning in all fefet-based spiking neural network: opportunities and challenges, Frontiers in neuroscience14, 634 (2020)

    2020

  14. [22]

    Mulaosmanovic, S

    H. Mulaosmanovic, S. D¨ unkel, M. Trentzsch, S. Beyer, E. T. Breyer, T. Mikolajick, and S. Slesazeck, Inves- tigation of accumulative switching in ferroelectric fets: Enabling universal modeling of the switching behavior, IEEE Transactions on Electron Devices67, 5804 (2020)

    2020

  15. [23]

    C. Han, B. Kwak, K.-R. Kwon, S. Jeong, J.-H. Kim, R. Choi,et al., Tunable coercive voltage and polarization of hzo through field-induced phase transitions, Materials Science in Semiconductor Processing195, 109615 (2025)

    2025

  16. [24]

    F. Xue, X. He, Z. Wang, J. R. D. Retamal, Z. Chai, L. Jing, C. Zhang, H. Fang, Y. Chai, T. Jiang,et al., Gi- ant ferroelectric resistance switching controlled by a mod- ulatory terminal for low-power neuromorphic in-memory computing, Advanced Materials33, 2008709 (2021)

    2021

  17. [25]

    F. Xiao, D. Tan, L. Tian, Z.-D. Luo, Q. Yang, X. Gan, D. Zhang, M. Alexe, Z. Chu, J. Zhang,et al., Multi- terminal operability in van der waals ferroelectric field- effect memtransistors for logic-in-memory and neuro- morphic computing, Advanced Functional Materials36, e23298 (2026)

    2026

  18. [26]

    J. Yoo, M. Park, S. Oh, S. Kim, H. Lee, S. Jung, S. Park, S. Lim, J. M. Song, J. W. Heo,et al., Reconfigurable adaptive synapse and logic device by ambipolar ferro- electric semiconductor, Small , e14124 (2026)

    2026

  19. [27]

    B. Shen, X. Hu, X. Wang, W. Zhang, and A. Jiang, Mul- tilevel ferroelectric hzo memory devices for neuromorphic computing in very fast operation speeds, Advanced De- vices & Instrumentation (2026)

    2026

  20. [28]

    S¨ unb¨ ul, T

    A. S¨ unb¨ ul, T. Ali, K. Mertens, R. Revello, D. Lehninger, F. M¨ uller, M. Lederer, K. K¨ uhnel, M. Rudolph, S. Oehler,et al., Optimizing ferroelectric and interface layers in hzo-based ftjs for neuromorphic applications, IEEE Transactions on Electron Devices69, 808 (2021)

    2021

  21. [29]

    B. Max, M. Hoffmann, H. Mulaosmanovic, S. Slesazeck, and T. Mikolajick, Hafnia-based double-layer ferroelec- tric tunnel junctions as artificial synapses for neuromor- phic computing, ACS Applied Electronic Materials2, 4023 (2020)

    2020

  22. [30]

    S. Oh, H. Hwang, and I. Yoo, Ferroelectric materials for neuromorphic computing, Apl Materials7(2019)

    2019

  23. [31]

    X. Wang, X. Chen, Y. Long, J. Liu, F. Lin, J. Yin, Y. Liu, and W. Guo, Manipulating thousands of non-volatile po- larization states within one sliding ferroelectric transistor at room temperature, Nature Electronics , 1 (2026)

    2026

  24. [32]

    Jadhav, K

    S. Jadhav, K. Roy, L. M. Amaro, T. Basavarajappa, M. Ramesh, S. Siddique, J. J. Cha, D. Jena, H. G. Xing, and A. Lal, Lorentzian switching dynamics in hzo-based femems synapses for neuromorphic weight storage, Nano Letters26, 5379 (2026)

    2026

  25. [33]

    Lee and S

    Y. Lee and S. Lee, Polarization-controlled memris- tive synapse characteristics of hfzro2-based ferroelectric switchable diode, Journal of Alloys and Compounds , 182700 (2025)

    2025

  26. [34]

    Xiang, L

    H. Xiang, L. Li, Y.-C. Chien, H. Zheng, J. Gao, and K.-W. Ang, Ferroelectric hf0. 5zr0. 5o2 with enhanced intermediate polarization: A platform for neuromorphic and logic-in-memory computing, ACS Applied Materials & Interfaces17, 32575 (2025)

    2025

  27. [35]

    Hoffmann, A

    M. Hoffmann, A. I. Khan, C. Serrao, Z. Lu, S. Salahud- din, M. Peˇ si´ c, S. Slesazeck, U. Schroeder, and T. Miko- lajick, Ferroelectric negative capacitance domain dynam- ics, Journal of Applied Physics123(2018)

    2018

  28. [36]

    Bandyopadhyay, P

    A. Bandyopadhyay, P. Ray, and V. Gopalan, An ap- proach to the klein–gordon equation for a dynamic study in ferroelectric materials, Journal of Physics: Condensed Matter18, 4093 (2006)

    2006

  29. [37]

    Nambu and D

    S. Nambu and D. A. Sagala, Domain formation and elastic long-range interaction in ferroelectric perovskites, Physical Review B50, 5838 (1994)

    1994

  30. [38]

    K. M. Rabe and K. M. Rabe, Physics of ferroelectrics: a modern perspective (2007)

    2007

  31. [39]

    O’Connor, M

    ´E. O’Connor, M. Halter, F. Eltes, M. Sousa, A. Kellock, S. Abel, and J. Fompeyrine, Stabilization of ferroelectric hfxzr1- xo2 films using a millisecond flash lamp annealing 21 technique, Apl Materials6(2018)

    2018

  32. [40]

    Materlik, C

    R. Materlik, C. K¨ unneth, and A. Kersch, The origin of ferroelectricity in hf1- xzrxo2: A computational investi- gation and a surface energy model, Journal of Applied Physics117(2015)

    2015

  33. [41]

    Cano and D

    A. Cano and D. Jim´ enez, Multidomain ferroelectricity as a limiting factor for voltage amplification in ferroelec- tric field-effect transistors, Applied Physics Letters97 (2010)

    2010

  34. [42]

    E. A. Eliseev, A. N. Morozovska, G. S. Svechnikov, P. Maksymovych, and S. V. Kalinin, Domain wall con- duction in multiaxial ferroelectrics, Physical Review B—Condensed Matter and Materials Physics85, 045312 (2012)

    2012

  35. [43]

    A. J. Bray, Theory of phase-ordering kinetics, Advances in Physics43, 357 (1994)

    1994

  36. [44]

    G. S. Grest, D. J. Srolovitz, and M. P. Anderson, Kinet- ics of domain growth: universality of kinetic exponents, Physical review letters52, 1321 (1984)

    1984

  37. [45]

    Barab´ asi and H

    A.-L. Barab´ asi and H. E. Stanley,Fractal concepts in surface growth(Cambridge University Press, 1995)

    1995

Pith tools

Reviewed July 13, 2026 · model on record in the stance chip above.