Elucidating structure-property correlations in ferroelectric Hf0.5Zr0.5O2 films using variational autoencoders
While Hf0.5Zr0.5O2 (HZO) thin films hold significant promise for modern nanoelectronic devices, a comprehensive understanding of the interplay between their polycrystalline structure and electrical properties remains elusive. Here, we present a novel framework combining phase-field (PF) modeling wit...
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Elsevier
2025-06-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S026412752500440X |
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| author | Kévin Alhada-Lahbabi Brice Gautier Damien Deleruyelle Grégoire Magagnin |
| author_facet | Kévin Alhada-Lahbabi Brice Gautier Damien Deleruyelle Grégoire Magagnin |
| author_sort | Kévin Alhada-Lahbabi |
| collection | DOAJ |
| description | While Hf0.5Zr0.5O2 (HZO) thin films hold significant promise for modern nanoelectronic devices, a comprehensive understanding of the interplay between their polycrystalline structure and electrical properties remains elusive. Here, we present a novel framework combining phase-field (PF) modeling with Variational Autoencoders (VAEs) to uncover structure-property correlations in polycrystalline HZO. Leveraging PF simulations, we constructed a high-fidelity dataset of P-V loops by systematically varying critical material parameters, including grain size, polar grain fraction, and crystalline orientation. The VAEs effectively encoded hysteresis loops into a low-dimensional latent space, capturing electrical properties while disentangling complex material parameters' interdependencies. We further demonstrate a VAE-based inverse design approach to optimize P-V loop features, enabling the tailored design of device-specific key performance indicators (KPIs), including coercive field, remanent polarization, and loop area. The proposed approach offers a pathway to systematically explore and optimize the material design space for ferroelectric nanoelectronics. |
| format | Article |
| id | doaj-art-ebd6a26ccb404c6188515f545883e54b |
| institution | OA Journals |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-ebd6a26ccb404c6188515f545883e54b2025-08-20T02:35:36ZengElsevierMaterials & Design0264-12752025-06-0125411402010.1016/j.matdes.2025.114020Elucidating structure-property correlations in ferroelectric Hf0.5Zr0.5O2 films using variational autoencodersKévin Alhada-Lahbabi0Brice Gautier1Damien Deleruyelle2Grégoire Magagnin3INSA Lyon, CNRS, Ecole Centrale de Lyon, Université Claude Bernard Lyon 1, CPE Lyon, INL, UMR5270, 69621 Villeurbanne, France; Corresponding authors.INSA Lyon, CNRS, Ecole Centrale de Lyon, Université Claude Bernard Lyon 1, CPE Lyon, INL, UMR5270, 69621 Villeurbanne, FranceINSA Lyon, CNRS, Ecole Centrale de Lyon, Université Claude Bernard Lyon 1, CPE Lyon, INL, UMR5270, 69621 Villeurbanne, FranceCNRS, Ecole Centrale de Lyon, INSA Lyon, Université Claude Bernard Lyon 1, CPE Lyon, INL, UMR5270, 69621 Villeurbanne, France; Corresponding authors.While Hf0.5Zr0.5O2 (HZO) thin films hold significant promise for modern nanoelectronic devices, a comprehensive understanding of the interplay between their polycrystalline structure and electrical properties remains elusive. Here, we present a novel framework combining phase-field (PF) modeling with Variational Autoencoders (VAEs) to uncover structure-property correlations in polycrystalline HZO. Leveraging PF simulations, we constructed a high-fidelity dataset of P-V loops by systematically varying critical material parameters, including grain size, polar grain fraction, and crystalline orientation. The VAEs effectively encoded hysteresis loops into a low-dimensional latent space, capturing electrical properties while disentangling complex material parameters' interdependencies. We further demonstrate a VAE-based inverse design approach to optimize P-V loop features, enabling the tailored design of device-specific key performance indicators (KPIs), including coercive field, remanent polarization, and loop area. The proposed approach offers a pathway to systematically explore and optimize the material design space for ferroelectric nanoelectronics.http://www.sciencedirect.com/science/article/pii/S026412752500440XFerroelectricsPhase-fieldMachine learningVAEHafnium oxide |
| spellingShingle | Kévin Alhada-Lahbabi Brice Gautier Damien Deleruyelle Grégoire Magagnin Elucidating structure-property correlations in ferroelectric Hf0.5Zr0.5O2 films using variational autoencoders Materials & Design Ferroelectrics Phase-field Machine learning VAE Hafnium oxide |
| title | Elucidating structure-property correlations in ferroelectric Hf0.5Zr0.5O2 films using variational autoencoders |
| title_full | Elucidating structure-property correlations in ferroelectric Hf0.5Zr0.5O2 films using variational autoencoders |
| title_fullStr | Elucidating structure-property correlations in ferroelectric Hf0.5Zr0.5O2 films using variational autoencoders |
| title_full_unstemmed | Elucidating structure-property correlations in ferroelectric Hf0.5Zr0.5O2 films using variational autoencoders |
| title_short | Elucidating structure-property correlations in ferroelectric Hf0.5Zr0.5O2 films using variational autoencoders |
| title_sort | elucidating structure property correlations in ferroelectric hf0 5zr0 5o2 films using variational autoencoders |
| topic | Ferroelectrics Phase-field Machine learning VAE Hafnium oxide |
| url | http://www.sciencedirect.com/science/article/pii/S026412752500440X |
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