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Polarity-dependent ferroelectric modulations in two-dimensional hybrid perovskite heterojunction transistors
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Polarity-dependent ferroelectric modulations in two-dimensional hybrid perovskite heterojunction transistors
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Journal Article
Polarity-dependent ferroelectric modulations in two-dimensional hybrid perovskite heterojunction transistors
2025
Overview
The non-volatile spontaneous ferroelectric polarization field serves as a cornerstone for applying ferroelectric materials in electronic devices, yet it is frequently mitigated by charge trapping at defect sites. Achieving an effective transition between ferroelectric polarization and charge trapping is challenging due to the inherent opposition of the two mechanisms and the uncontrollable charge trapping types in ferroelectric materials. Here, we realized a polarity-dependent ferroelectric transition in two-dimensional ferroelectric heterojunction transistor by integrating a hybrid organic-inorganic ferroelectric layer embedded with electron trapping sites. Through theoretical calculations and experimental validation, we demonstrate a ferroelectric manifestation and elimination mechanism based on the polarity of the semiconductor layer. The electron-majority n-type semiconductor exhibits charge trapping behavior, while the electron-minority p-type transistor exhibits the ferroelectric control mechanism. Leveraging the mechanism transition, our bipolar heterojunction transistor enables synergistic heterogeneous control of non-volatile memory and volatile synaptic weight modulation within a single bipolar ferroelectric transistor. Based on the experimentally extracted parameters from the transistors, the device-informed simulation achieves a recognition accuracy of 92.9% and a 20.7-fold improvement in training efficiency of the transfer learning network.
The authors demonstrate a two-dimensional ferroelectric heterojunction transistor that exploits polarity-dependent transitions between ferroelectricity and charge trapping, enabling both memory and synaptic functions, and enhancing AI training efficiency.
