Neuromorphic Technology Based on Charge Storage Memory Devices

Four synaptic devices are introduced for spiking neural networks (SNNs) and deep neural networks (DNNs). Unsupervised learning is successfully demonstrated by applying the STDP learning rule reflecting the LTP/LTD characteristics of the fabricated TFT-type NOR flash memory cells. Gated Schottky diod...

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Bibliographic Details
Published in2018 IEEE Symposium on VLSI Technology pp. 169 - 170
Main Authors Lee, Sung-Tae, Lim, Suhwan, Choi, Nagyong, Bae, Jong-Ho, Kim, Chul-Heung, Lee, Soochang, Lee, Dong Hwan, Lee, Tackhwi, Chung, Sungyong, Park, Byung-Gook, Lee, Jong-Ho
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Computer architecture
Flash memories
Logic gates
Memristors
Microprocessors
Neurons
Synapses
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ISSN2158-9682
DOI10.1109/VLSIT.2018.8510667

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Summary:Four synaptic devices are introduced for spiking neural networks (SNNs) and deep neural networks (DNNs). Unsupervised learning is successfully demonstrated by applying the STDP learning rule reflecting the LTP/LTD characteristics of the fabricated TFT-type NOR flash memory cells. Gated Schottky diode (GSD) and vertical NAND flash cell are proposed as synaptic device for DNNs. Using matched simulation, we obtained higher learning accuracy with GSD and NAND synaptic devices compared to that with a memristor-based synapse. Measured synaptic properties of the vertical NAND cells are reported for the first time.
ISSN:2158-9682
DOI:10.1109/VLSIT.2018.8510667