Grain-scale physics to array performance: HZO-Driven FeFET modelling for neuromorphic computing
Aditya Ram ABBARAJU, Anandhan SRINIVASAN, Balasubramanian KANDASUBRAMANIAN
Abstract. Ferroelectric Hf0.5Zr0.5O2 (HZO) exhibits grain-size-dependent polarization switching that dominates variability in FeFET neuromorphic crossbars, yet the quantitative link from microstructure to array-level accuracy remains unexplored. This work bridges polycrystalline grain physics to 64×64 array performance via our proposed grain count conditioned surrogate, GrainAwareNet, developed using a 2D cross-sectional phase-field model assuming columnar grain structure typical of thin HZO films. Simulations yield 1,927 valid P-E loops spanning grain counts G = 10-100 (equivalent mean grain size ~6-19 nm), with Pr = 15-35 µC/cm² (±15%) and Ec = 80-180 MV/m (±40%). GrainAwareNet achieves R² = 0.9923, RMSE = 0.032 µC/cm², and 1000× speedup, enabling Monte Carlo exploration of grain-count variability σG = 0-40. The framework demonstrates remarkable robustness: Matrix-Vector Multiply (MVM) output error remains <1% for σG up to ~35, with <0.3% error at σG = 5 (CV < 6%). Voltage calibration further recovers ~90% accuracy at higher variability. Framework enables microstructure-aware design-technology co-optimization (DTCO) for HZO FeFETs.
Keywords
In-Memory Computing, FeFET Crossbar, Microstructure Variability, HZO Ferroelectric, Phase-Field Modelling, Physics-Informed ML
Published online 5/10/2026, 6 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Aditya Ram ABBARAJU, Anandhan SRINIVASAN, Balasubramanian KANDASUBRAMANIAN, Grain-scale physics to array performance: HZO-Driven FeFET modelling for neuromorphic computing, Materials Research Proceedings, Vol. 65, pp 74-79, 2026
DOI: https://doi.org/10.21741/9781644904138-11
The article was published as article 11 of the book Processing and Characterization of Materials
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] R. Koduru, I. Ahmed, A.K. Saha, X. Lyu, P. Ye, S.K. Gupta, Phase-field simulations of polarization variations in polycrystalline Hf0.5Zr0.5O2 based MFIM: Voltage dependence and dynamics, J. Appl. Phys. 134 (2023) 084104. https://doi.org/10.1063/5.0158997
[2] A.-L. Kévin, D. Damien, G. Brice, Ultrafast and accurate prediction of polycrystalline hafnium oxide phase-field ferroelectric hysteresis using graph neural networks, Nanoscale Adv. 6 (2024) 2350–2362. https://doi.org/10.1039/D3NA01115A
[3] C. Wang, J. Victor, A.K. Saha, X. Chen, M. Si, T. Sharma, K. Roy, P.D. Ye, S.K. Gupta, FeFET-Based Synaptic Cross-Bar Arrays for Deep Neural Networks: Impact of Ferroelectric Thickness on Device-Circuit Non-Idealities and System Accuracy, in: 2023 Device Res. Conf. DRC, IEEE, Santa Barbara, CA, USA, 2023: pp. 1–2. https://doi.org/10.1109/DRC58590.2023.10187042
[4] H. Chen, H. Luo, X. Yuan, D. Zhang, Constructing a correlation between ferroelectricity and grain sizes in Hf0.5 Zr0.5 O2 ferroelectric thin films, CrystEngComm 24 (2022) 1731–1737. https://doi.org/10.1039/D1CE01626A
[5] A.K. Saha, K. Ni, S. Dutta, S. Datta, S. Gupta, Phase field modeling of domain dynamics and polarization accumulation in ferroelectric HZO, Appl. Phys. Lett. 114 (2019) 202903. https://doi.org/10.1063/1.5092707
[6] H.J. Kim, M.H. Park, Y.J. Kim, Y.H. Lee, W. Jeon, T. Gwon, T. Moon, K.D. Kim, C.S. Hwang, Grain size engineering for ferroelectric Hf0.5Zr0.5O2 films by an insertion of Al2O3 interlayer, Appl. Phys. Lett. 105 (2014) 192903. https://doi.org/10.1063/1.4902072
[7] S. Sugathan, K. Thekkepat, S. Bandyopadhyay, J. Kim, P.-R. Cha, A phase field model combined with a genetic algorithm for polycrystalline hafnium zirconium oxide ferroelectrics, Nanoscale 14 (2022) 14997–15009. https://doi.org/10.1039/D2NR02678C
