Ferroelectric synaptic devices based on CMOS-compatible HfAlO x for neuromorphic and reservoir computing applications

The hafnium oxide-based ferroelectric tunnel junction (FTJ) has been actively researched because of desirable advantages such as low power and CMOS compatibility to operate as a memristor. In the case of HfAlO (HAO), the remanent polarization ( ) value is high and the atomic radius of Al is smaller...

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Veröffentlicht in:Nanoscale 2023-05, Vol.15 (18), p.8366-8376
Hauptverfasser: Kim, Dahye, Kim, Jihyung, Yun, Seokyeon, Lee, Jungwoo, Seo, Euncho, Kim, Sungjun
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container_issue 18
container_start_page 8366
container_title Nanoscale
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creator Kim, Dahye
Kim, Jihyung
Yun, Seokyeon
Lee, Jungwoo
Seo, Euncho
Kim, Sungjun
description The hafnium oxide-based ferroelectric tunnel junction (FTJ) has been actively researched because of desirable advantages such as low power and CMOS compatibility to operate as a memristor. In the case of HfAlO (HAO), the remanent polarization ( ) value is high and the atomic radius of Al is smaller than that of Hf; therefore, ferroelectricity can be better induced without mechanical force. In this paper, we propose an FTJ using HAO as a ferroelectric layer through electrical analysis and experiments; further, we experimentally demonstrate its capability as a synaptic device. Moreover, we evaluate the maximum 2 and TER value of the device according to the difference in conditions of thickness and cell area. The optimized device conditions are analyzed, and a large value of 2 (>∼43 μC cm ) is obtained. Furthermore, we show that paired-pulse facilitation, paired-pulse depression, and spike-timing-dependent plasticity can be utilized in HAO-based FTJs. In addition, this study demonstrates the use of an FTJ device as a physical reservoir to implement reservoir computing. Through a series of processes, the synaptic properties of FTJs are verified for the feasibility of their implementation as an artificial synaptic device.
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title Ferroelectric synaptic devices based on CMOS-compatible HfAlO x for neuromorphic and reservoir computing applications
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