Researching | JAD Volume 12 Issue 3,2022

Jingjing Rao, Zhen Fan, Qicheng Huang, Yongjian Luo, Xingmin Zhang, Haizhong Guo, Xiaobing Yan, Guo Tian, Deyang Chen, Zhipeng Hou, Minghui Qin, Min Zeng, Xubing Lu, Guofu Zhou, Xingsen Gao, and Jun-Ming Liu

Ferroelectric tunnel junction (FTJ) has attracted considerable attention for its potential applications in nonvolatile memory and neuromorphic computing. However, the experimental exploration of FTJs with high ON/OFF ratios is a challenging task due to the vast search space comprising of ferroelectric and electrode mat Ferroelectric tunnel junction (FTJ) has attracted considerable attention for its potential applications in nonvolatile memory and neuromorphic computing. However, the experimental exploration of FTJs with high ON/OFF ratios is a challenging task due to the vast search space comprising of ferroelectric and electrode materials, fabrication methods and conditions and so on. Here, machine learning (ML) is demonstrated to be an effective tool to guide the experimental search of FTJs with high ON/OFF ratios. A dataset consisting of 152 FTJ samples with nine features and one target attribute (i.e., ON/OFF ratio) is established for ML modeling. Among various ML models, the gradient boosting classification model achieves the highest prediction accuracy. Combining the feature importance analysis based on this model with the association rule mining, it is extracted that the utilizations of {graphene/graphite (Gra) (top), LaNiO₃ (LNO) (bottom)} and {Gra (top), Ca

_{0.96}

Ce

_{0.04}

MnO₃ (CCMO) (bottom)} electrode pairs are likely to result in high ON/OFF ratios in FTJs. Moreover, two previously unexplored FTJs: Gra/BaTiO₃ (BTO)/LNO and Gra/BTO/CCMO, are predicted to achieve ON/OFF ratios higher than 1000. Guided by the ML predictions, the Gra/BTO/LNO and Gra/BTO/CCMO FTJs are experimentally fabricated, which unsurprisingly exhibit

\geq

1000 ON/OFF ratios (

\sim

8540 and

\sim

7890, respectively). This study demonstrates a new paradigm of developing high-performance FTJs by using ML.Ferroelectric tunnel junction (FTJ) has attracted considerable attention for its potential applications in nonvolatile memory and neuromorphic computing. However, the experimental exploration of FTJs with high ON/OFF ratios is a challenging task due to the vast search space comprising of ferroelectric and electrode materials, fabrication methods and conditions and so on. Here, machine learning (ML) is demonstrated to be an effective tool to guide the experimental search of FTJs with high ON/OFF ratios. A dataset consisting of 152 FTJ samples with nine features and one target attribute (i.e., ON/OFF ratio) is established for ML modeling. Among various ML models, the gradient boosting classification model achieves the highest prediction accuracy. Combining the feature importance analysis based on this model with the association rule mining, it is extracted that the utilizations of {graphene/graphite (Gra) (top), LaNiO₃ (LNO) (bottom)} and {Gra (top), Ca

_{0.96}

Ce

_{0.04}

MnO₃ (CCMO) (bottom)} electrode pairs are likely to result in high ON/OFF ratios in FTJs. Moreover, two previously unexplored FTJs: Gra/BaTiO₃ (BTO)/LNO and Gra/BTO/CCMO, are predicted to achieve ON/OFF ratios higher than 1000. Guided by the ML predictions, the Gra/BTO/LNO and Gra/BTO/CCMO FTJs are experimentally fabricated, which unsurprisingly exhibit

\geq

1000 ON/OFF ratios (

\sim

8540 and

\sim

7890, respectively). This study demonstrates a new paradigm of developing high-performance FTJs by using ML..

Journal of Advanced Dielectrics

Publication Date: Jan. 01, 1900
Vol. 12, Issue 3, 2250005 (2022)

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Modeling the effect of uniform and nonuniform dispersion of nanofillers on electrical tree propagation in polyethylene dielectric

Khola Azhar, and Salman Amin

A phase-field model is developed in this paper based on the similarity between mechanical fracture and dielectric breakdown. Electrical treeing is associated with the dielectric breakdown in solid dielectrics by the application of high voltages. Instead of explicitly tracing the propagation of conductive channel, this A phase-field model is developed in this paper based on the similarity between mechanical fracture and dielectric breakdown. Electrical treeing is associated with the dielectric breakdown in solid dielectrics by the application of high voltages. Instead of explicitly tracing the propagation of conductive channel, this model initializes a continuous phase field to characterize the extent of damage. So far, limited research has been conducted for simulating the effect of nanofiller dispersion on electrical treeing. No study has modeled the effect of uniform and nonuniform dispersion of nanofillers with varying filler concentration on treeing. Since electrical treeing tends to decrease the breakdown strength of solid dielectrics therefore, nanofillers are widely used to distract the tree from a straight channel to distribute its energy in multiple paths. Diverting a straight treeing channel into multiple paths reduces the chances of its propagation from live to dead-end hence, improving the breakdown strength. The physical and chemical nature of nanofillers has a crucial impact on increasing the resistance to treeing. In this paper, phase-field model is developed and used to simulate electrical treeing in polyethylene for varying concentrations of alumina nanofiller using COMSOL Multiphysics. Tree inception time, tree-growth patterns, and corresponding changes in dielectric strength is studied for both dispersions. Electrical treeing under different concentrations of alumina nanofillers with uniform and nonuniform dispersion is investigated in polyethylene as a base material. It is observed that fillers with uniform dispersion increases the resistance to treeing and tree inception time. Highest resistance to treeing is observed by adding 1% nanoalumina uniformly in raw polyethylene. Moreover, in uniform dispersion the tree deflects into multiple branches earlier than nonuniform dispersion impeding the damage speed as well.A phase-field model is developed in this paper based on the similarity between mechanical fracture and dielectric breakdown. Electrical treeing is associated with the dielectric breakdown in solid dielectrics by the application of high voltages. Instead of explicitly tracing the propagation of conductive channel, this model initializes a continuous phase field to characterize the extent of damage. So far, limited research has been conducted for simulating the effect of nanofiller dispersion on electrical treeing. No study has modeled the effect of uniform and nonuniform dispersion of nanofillers with varying filler concentration on treeing. Since electrical treeing tends to decrease the breakdown strength of solid dielectrics therefore, nanofillers are widely used to distract the tree from a straight channel to distribute its energy in multiple paths. Diverting a straight treeing channel into multiple paths reduces the chances of its propagation from live to dead-end hence, improving the breakdown strength. The physical and chemical nature of nanofillers has a crucial impact on increasing the resistance to treeing. In this paper, phase-field model is developed and used to simulate electrical treeing in polyethylene for varying concentrations of alumina nanofiller using COMSOL Multiphysics. Tree inception time, tree-growth patterns, and corresponding changes in dielectric strength is studied for both dispersions. Electrical treeing under different concentrations of alumina nanofillers with uniform and nonuniform dispersion is investigated in polyethylene as a base material. It is observed that fillers with uniform dispersion increases the resistance to treeing and tree inception time. Highest resistance to treeing is observed by adding 1% nanoalumina uniformly in raw polyethylene. Moreover, in uniform dispersion the tree deflects into multiple branches earlier than nonuniform dispersion impeding the damage speed as well..

Journal of Advanced Dielectrics

Publication Date: Jan. 01, 1900
Vol. 12, Issue 3, 2250006 (2022)

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Enhanced ferroelectric and piezoelectric characteristics in Ca-substituted

{BaTi}_{0.88} {Zr}_{0.12} O_{3}

ceramics

S. B. Chen, S. Y. Wu, and X. M. Chen

In this work, Ba1−xCaxTi0.88Zr0.12O3 (x= 0.00–0.25) ceramics were prepared by a solid-state reaction method, and the variation of dielectric, ferroelectric and piezoelectric characteristics has been investigated together with the structure evolution. The crystal structure varies from rhombohedral to tetragonal at room-

Journal of Advanced Dielectrics

Publication Date: Jan. 01, 1900
Vol. 12, Issue 3, 2250007 (2022)

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Temperature-activation mechanism of the temperature dependence of the dielectric constant of ferroelectric ceramics PZT

D. V. Kuzenko

The theoretical possibility of the temperature-activation process of the temperature dependence of the dielectric constant of samples of ferroelectric ceramics lead zirconate titanate (PZT) at temperatures below the Curie point is considered. The model takes into account the 180° motion of the domain wall, which i

Journal of Advanced Dielectrics

Publication Date: Jan. 01, 1900
Vol. 12, Issue 3, 2250010 (2022)

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