Ferroelectric materials for neuromorphic computing

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 portabl...

Full description

Saved in:
Bibliographic Details
Published in:APL materials 2019-09, Vol.7 (9), p.091109-091109-14
Main Authors: Oh, S., Hwang, H., Yoo, I. K.
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2166-532X
2166-532X
DOI:10.1063/1.5108562