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Leaky 2T Dynamic Random-Access Memory Devices Based on Nanometer-Thick Indium-Gallium-Zinc-Oxide Films for Reservoir Computing

Authors
Jang, JunwonKim, SeongminPark, SuyongKim, SoominKim, SungjunCho, Seongjae
Issue Date
Oct-2024
Publisher
American Chemical Society
Keywords
neuromorphic system; reservoir computing; capacitorlessdynamic random-access memory; two-transistor DRAM; artificial synaptic array; In-Ga-Zn-O
Citation
ACS Applied Nano Materials, v.7, no.19, pp 22430 - 22435
Pages
6
Indexed
SCIE
SCOPUS
Journal Title
ACS Applied Nano Materials
Volume
7
Number
19
Start Page
22430
End Page
22435
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/26461
DOI
10.1021/acsanm.4c04501
ISSN
2574-0970
2574-0970
Abstract
This paper explores the integration of indium-gallium-zinc oxide (IGZO)-based 2-transistor 0-capacitor dynamic random-access memory (2T0C DRAM, or shortly, 2T DRAM) into reservoir computing for advanced semiconductor artificial intelligence (AI) applications. The short-term memory characteristics of IGZO 2T DRAM enable rapid read-write speeds essential for processing time-varying input data. Experimental results confirm high on/off ratios and leaky retention behaviors. The study also examines paired-pulse facilitation (PPF) phenomena, offering insights into reinforcement mechanisms for cognitive computing. Finally, the reservoir computing approach achieves notable pattern recognition accuracy with a 4-bit pulse scheme, showcasing its effectiveness in complex data sets. [GRAPHICS]
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