Fully CMOS Compatible Charge Trap Memory‐Based Reservoir Computing System

Abstract Reservoir computing (RC) systems have gained considerable attention for their effectiveness in temporal data processing. Although extensive research has been conducted on RC systems, studies focusing on complementary metal‐oxide semiconductor‐compatible flash memory devices remain scarce. In this study, the potential of RC systems based on TiN/Al 2 O 3 /Si 3 N 4 /SiO 2 /poly‐Si (TANOS) is explored, utilizing the high‐pressure annealing (HPA) process to enhance the performance of the device. Specifically, HPA‐treated TANOS devices are employed in the readout layer to ensure stable long‐term memory characteristics, while untreated TANOS devices are used in the reservoir layer, leveraging their short‐term memory properties induced by interfacial traps. This study also investigates the feasibility of TANOS devices for neuromorphic computing. Based on Modified National Institute of Standards and Technology simulations, the complete TANOS‐based RC system achieves a recognition rate of 84.48%, demonstrating its potential for temporal pattern recognition tasks.

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Publication Details

Journal
Advanced Materials Technologies
Published
2025-06-24
DOI
https://doi.org/10.1002/admt.202500858
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Fully CMOS Compatible Charge Trap Memory‐Based Reservoir Computing System

Woo Young Choi, Donghyun Ryu, Suyong Park, Sungjun Kim et al.
Advanced Materials Technologies
Neural Networks and Reservoir Computing
article

Fully CMOS Compatible Charge Trap Memory‐Based Reservoir Computing System

Woo Young Choi, Donghyun Ryu, Suyong Park, Sungjun Kim, Sungjoon Kim
article en

Abstract

Abstract Reservoir computing (RC) systems have gained considerable attention for their effectiveness in temporal data processing. Although extensive research has been conducted on RC systems, studies focusing on complementary metal‐oxide semiconductor‐compatible flash memory devices remain scarce. In this study, the potential of RC systems based on TiN/Al 2 O 3 /Si 3 N 4 /SiO 2 /poly‐Si (TANOS) is explored, utilizing the high‐pressure annealing (HPA) process to enhance the performance of the device. Specifically, HPA‐treated TANOS devices are employed in the readout layer to ensure stable long‐term memory characteristics, while untreated TANOS devices are used in the reservoir layer, leveraging their short‐term memory properties induced by interfacial traps. This study also investigates the feasibility of TANOS devices for neuromorphic computing. Based on Modified National Institute of Standards and Technology simulations, the complete TANOS‐based RC system achieves a recognition rate of 84.48%, demonstrating its potential for temporal pattern recognition tasks.

Advanced Materials TechnologiesVol. 10(19)
Seoul National University (KR), Korea University (KR), Dongguk University (KR)
National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 93%
Neural Networks and Reservoir Computing
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