Cost‐Effective and Fully Hardware‐Oriented Reservoir Computing Based on IGZO/HZO Ferroelectric Thin‐Film Transistor with Electrically and Optically Distinguishable States

Abstract Hardware‐based reservoir computing (RC) systems provide benefits like energy efficiency and effective predictability. The implementation of different switching characteristics for the reservoir and readout layers requires the use of different types of devices or additional processing. However, an RC system with distinguishable switching characteristics obtained by changing stimulation on a single device is not identified yet, but it is appealing in terms of process simplicity and efficient processing costs. This study develops an RC system that uses ferroelectric thin‐film transistor (FeTFT) devices with an indium gallium zinc oxide channel and Hf 0.5 Zr 0.5 O 2 ferroelectric layer for both networks. The nonvolatile FeTFT utilizes the remnant polarization properties of the ferroelectric layer through electrical stimulation, showing stable retention characteristics (10 4 s) and long‐term potentiation/depression. By using optical stimulation, the volatile FeTFT demonstrates short‐term characteristics, such as paired‐pulse facilitation, and a 4‐bit RC system. This proves that it is possible to meet the functional requirements of both the reservoir and readout networks by simply varying the type of stimulation applied to a single FeTFT. Finally, the fully FeTFT‐based RC system can recognize digit patterns from the Modified National Institute of Standards and Technology database with a high accuracy of 90.5%.

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Publication Details

Journal
Advanced Functional Materials
Published
2024-09-09
DOI
https://doi.org/10.1002/adfm.202409095
Citations
34
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
11.81

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article

Cost‐Effective and Fully Hardware‐Oriented Reservoir Computing Based on IGZO/HZO Ferroelectric Thin‐Film Transistor with Electrically and Optically Distinguishable States

Seungjun Lee, Yongjin Park, Sungjun Kim, Yoon‐Seok Lee et al.
34 citations
Advanced Functional Materials
Neural Networks and Reservoir Computing
11.81
article

Cost‐Effective and Fully Hardware‐Oriented Reservoir Computing Based on IGZO/HZO Ferroelectric Thin‐Film Transistor with Electrically and Optically Distinguishable States

Seungjun Lee, Yongjin Park, Sungjun Kim, Yoon‐Seok Lee, Jung Woo Lee, Doohyung Kim
article en
34 citations

Abstract

Abstract Hardware‐based reservoir computing (RC) systems provide benefits like energy efficiency and effective predictability. The implementation of different switching characteristics for the reservoir and readout layers requires the use of different types of devices or additional processing. However, an RC system with distinguishable switching characteristics obtained by changing stimulation on a single device is not identified yet, but it is appealing in terms of process simplicity and efficient processing costs. This study develops an RC system that uses ferroelectric thin‐film transistor (FeTFT) devices with an indium gallium zinc oxide channel and Hf 0.5 Zr 0.5 O 2 ferroelectric layer for both networks. The nonvolatile FeTFT utilizes the remnant polarization properties of the ferroelectric layer through electrical stimulation, showing stable retention characteristics (10 4 s) and long‐term potentiation/depression. By using optical stimulation, the volatile FeTFT demonstrates short‐term characteristics, such as paired‐pulse facilitation, and a 4‐bit RC system. This proves that it is possible to meet the functional requirements of both the reservoir and readout networks by simply varying the type of stimulation applied to a single FeTFT. Finally, the fully FeTFT‐based RC system can recognize digit patterns from the Modified National Institute of Standards and Technology database with a high accuracy of 90.5%.

Advanced Functional MaterialsVol. 34(49)
Dongguk University (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 1%
Neural Networks and Reservoir Computing
11.81
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