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

Junwon Jang, Seongmin Kim, Suyong Park, Soomin Kim, Sungjun Kim, Seongjae Cho

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)22430-22435
Number of pages6
JournalACS Applied Nano Materials
Volume7
Issue number19
DOIs
StatePublished - 11 Oct 2024

Bibliographical note

Publisher Copyright:
© 2024 American Chemical Society.

Keywords

  • artificial synaptic array
  • capacitorless dynamic random-access memory
  • In−Ga−Zn-O
  • neuromorphic system
  • reservoir computing
  • two-transistor DRAM

Fingerprint

Dive into the research topics of 'Leaky 2T Dynamic Random-Access Memory Devices Based on Nanometer-Thick Indium-Gallium−Zinc-Oxide Films for Reservoir Computing'. Together they form a unique fingerprint.

Cite this