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 language | English |
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Pages (from-to) | 22430-22435 |
Number of pages | 6 |
Journal | ACS Applied Nano Materials |
Volume | 7 |
Issue number | 19 |
DOIs | |
State | Published - 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