Dynamic Control of Synaptic Plasticity by Competing Ferroelectric and Trap‐Assisted Switching in IGZO Transistors with Al <sub>2</sub> O <sub>3</sub> /HfO <sub>2</sub> Dielectrics

Abstract Ferroelectric synaptic transistors have gained significant attention for neuromorphic applications owing to their low power consumption and high‐speed modulation. However, charge trapping in ferroelectric films introduces nonlinearity, instability, and poor reproducibility during synaptic weight updates. A buried gate‐structured InGaZnO synaptic transistor with an Al 2 O 3 /HfO 2 dielectric stack is demonstrated in this study to achieve unprecedented conductance modulation by leveraging the frequency‐dependent coupling between ferroelectric polarization and charge trapping. The versatile conductance‐tuning capability of double switching modes enables even single‐polarity voltage‐driven potentiation and depression while simultaneously achieving highly linear conductance modulation. The diffusion of Al atoms into the HfO 2 layer promotes additional oxygen vacancies, which contribute to the emergence of deep‐level trap states. These defects also facilitate noncentrosymmetric crystal‐phase stabilization through spontaneous symmetry breaking, thereby inducing ferroelectricity, which is corroborated by density‐functional‐theory simulations and piezoelectric‐force‐microscopy measurements. By fully utilizing dual‐mode operation, the device achieves highly linear conductance updates with a minimum nonlinearity of 0.47 and enables potentiation and depression under a pulse‐train configuration with a single voltage polarity. The system attains a high pattern‐recognition accuracy of 97.03% for handwritten digit classification. This dual‐mode operation strategy provides a compelling pathway for simplifying the architecture of integrated neuromorphic circuits and enhancing their computational efficiency.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-09-18
DOI
https://doi.org/10.1002/adfm.202513449
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

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article

Dynamic Control of Synaptic Plasticity by Competing Ferroelectric and Trap‐Assisted Switching in IGZO Transistors with Al 2 O 3 /HfO 2 Dielectrics

Han Seul Kim, Byungjin Cho, Ji‐Hoon Ahn, Jongwon Yoon et al.
1 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
0.73
article

Dynamic Control of Synaptic Plasticity by Competing Ferroelectric and Trap‐Assisted Switching in IGZO Transistors with Al 2 O 3 /HfO 2 Dielectrics

Han Seul Kim, Byungjin Cho, Ji‐Hoon Ahn, Jongwon Yoon, Hyo‐Bae Kim, Woojin Park, Se‐Young Oh, Ojun Kwon, Dong Ho Lee
article en
1 citations

Abstract

Abstract Ferroelectric synaptic transistors have gained significant attention for neuromorphic applications owing to their low power consumption and high‐speed modulation. However, charge trapping in ferroelectric films introduces nonlinearity, instability, and poor reproducibility during synaptic weight updates. A buried gate‐structured InGaZnO synaptic transistor with an Al 2 O 3 /HfO 2 dielectric stack is demonstrated in this study to achieve unprecedented conductance modulation by leveraging the frequency‐dependent coupling between ferroelectric polarization and charge trapping. The versatile conductance‐tuning capability of double switching modes enables even single‐polarity voltage‐driven potentiation and depression while simultaneously achieving highly linear conductance modulation. The diffusion of Al atoms into the HfO 2 layer promotes additional oxygen vacancies, which contribute to the emergence of deep‐level trap states. These defects also facilitate noncentrosymmetric crystal‐phase stabilization through spontaneous symmetry breaking, thereby inducing ferroelectricity, which is corroborated by density‐functional‐theory simulations and piezoelectric‐force‐microscopy measurements. By fully utilizing dual‐mode operation, the device achieves highly linear conductance updates with a minimum nonlinearity of 0.47 and enables potentiation and depression under a pulse‐train configuration with a single voltage polarity. The system attains a high pattern‐recognition accuracy of 97.03% for handwritten digit classification. This dual‐mode operation strategy provides a compelling pathway for simplifying the architecture of integrated neuromorphic circuits and enhancing their computational efficiency.

Advanced Functional MaterialsVol. 36(13)
Chungbuk National University (KR), Korea Institute of Materials Science (KR), Chungbuk National University Hospital (KR), Hanyang University (KR), Anyang University (KR)
National Research Foundation, Ministry of Trade, Industry and Energy, Korea Institute for Advancement of Technology, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
0.73
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