Fully CMOS Compatible Charge Trap Memory‐Based Reservoir Computing System
Source abstract
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/Al2O3/Si3N4/SiO2/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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Neural Networks and Reservoir Computing · Advanced Memory and Neural Computing · Machine Learning and ELM
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Electronics and Electrical Engineering Dongguk University Seoul 04620 Republic of Korea
Inter‐University Semiconductor Research Center Department of Electrical and Computer Engineering Seoul National University Seoul 08826 South Korea
Department of AI Semiconductor Engineering Korea University Sejong 30019 Republic of Korea
Funding
National Research Foundation of Korea · Ministry of Science and ICT, South Korea
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