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Ferroelectric materials for neuromorphic computing

Title
Ferroelectric materials for neuromorphic computing
Authors
Oh, S.Hwang, H.Yoo, I.K.
Date Issued
Sep-2019
Publisher
AMER INST PHYSICS
Abstract
Ferroelectric materials are promising candidates for synaptic weight elements in neural network hardware because of their nonvolatile multilevel memory effect. This feature is crucial for their use in mobile applications such as inference when vector matrix multiplication is performed during portable artificial intelligence service. In addition, the adaptive learning effect in ferroelectric polarization has gained considerable research attention for reducing the CMOS circuit overhead of an integrator and amplifier with an activation function. In spite of their potential for a weight and a neuron, material issues have been pointed out for commercialization in conjunction with CMOS processing and device structures. Herein, we review ferroelectric synaptic weights and neurons from the viewpoint of materials in relation to device operation, along with discussions and suggestions for improvement. Moreover, we discuss the reliability of HfO2 as an emerging material and suggest methods to overcome the scaling issue of ferroelectrics.
URI
http://oasis.postech.ac.kr/handle/2014.oak/100226
ISSN
2166-532X
Article Type
Article
Citation
APL MATERIALS, vol. 7, no. 9, 2019-09
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 HWANG, HYUNSANG
Dept of Materials Science & Enginrg
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