SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations

Cited 92 time in webofscience Cited 0 time in scopus
  • Hit : 63
  • Download : 0
Although several types of architecture combining memory cells and transistors have been used to demonstrate artificial synaptic arrays, they usually present limited scalability and high power consumption. Transistor-free analog switching devices may overcome these limitations, yet the typical switching process they rely on-formation of filaments in an amorphous medium-is not easily controlled and hence hampers the spatial and temporal reproducibility of the performance. Here, we demonstrate analog resistive switching devices that possess desired characteristics for neuromorphic computing networks with minimal performance variations using a single-crystalline SiGe layer epitaxially grown on Si as a switching medium. Such epitaxial random access memories utilize threading dislocations in SiGe to confine metal filaments in a defined, one-dimensional channel. This confinement results in drastically enhanced switching uniformity and long retention/high endurance with a high analog on/off ratio. Simulations using the MNIST handwritten recognition data set prove that epitaxial random access memories can operate with an online learning accuracy of 95.1%.
Publisher
NATURE PUBLISHING GROUP
Issue Date
2018-04
Language
English
Article Type
Article
Citation

NATURE MATERIALS, v.17, no.4, pp.335 - +

ISSN
1476-1122
DOI
10.1038/s41563-017-0001-5
URI
http://hdl.handle.net/10203/247667
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 92 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0