Leaky 2T Dynamic Random-Access Memory Devices Based on Nanometer-Thick Indium-Gallium-Zinc-Oxide Films for Reservoir Computing
- Authors
- Jang, Junwon; Kim, Seongmin; Park, Suyong; Kim, Soomin; Kim, Sungjun; Cho, 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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Collections - College of Engineering > Department of Electronics and Electrical Engineering > 1. Journal Articles

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