Effect of conductance linearity and multi-level cell characteristics of TaOx-based synapse device on pattern recognition accuracy of neuromorphic system

To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC characteristics of TaOx-based filamentary resistive rando...

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Bibliographic Details
Published in:Nanotechnology 2018-03, Vol.29 (11), p.115203-115203
Main Authors: Sung, Changhyuck, Lim, Seokjae, Kim, Hyungjun, Kim, Taesu, Moon, Kibong, Song, Jeonghwan, Kim, Jae-Joon, Hwang, Hyunsang
Format: Article
Language:English
Subjects:
conductance linearity
neuromorphic system
resistive random access memory (RRAM)
synapse device
TaO
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Summary:To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC characteristics of TaOx-based filamentary resistive random access memory (RRAM) to implement the synapse device in neural network hardware. Our findings show that the number of oxygen vacancies in the filament constriction region of the RRAM directly controls the CL and MLC characteristics. By adopting a Ta electrode (instead of Ti) and the hot-forming step, we could form a dense conductive filament. As a result, a wide range of conductance levels with CL is achieved and significantly improved image classification accuracy is confirmed.
ISSN:0957-4484
1361-6528
DOI:10.1088/1361-6528/aaa733