TY - JOUR
T1 - Efficient data processing using tunable entropy-stabilized oxide memristors
AU - Yoo, Sangmin
AU - Chae, Sieun
AU - Chiang, Tony
AU - Webb, Matthew
AU - Ma, Tao
AU - Paik, Hanjong
AU - Park, Yongmo
AU - Williams, Logan
AU - Nomoto, Kazuki
AU - Xing, Huili G.
AU - Trolier-McKinstry, Susan
AU - Kioupakis, Emmanouil
AU - Heron, John T.
AU - Lu, Wei D.
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Limited 2024.
PY - 2024/6
Y1 - 2024/6
N2 - Memristive devices are of potential use in a range of computing applications. However, many of these devices are based on amorphous materials, where systematic control of the switching dynamics is challenging. Here we report tunable and stable memristors based on an entropy-stabilized oxide. We use single-crystalline (Mg,Co,Ni,Cu,Zn)O films grown on an epitaxial bottom electrode. By adjusting the magnesium composition (XMg = 0.11–0.27) of the entropy-stabilized oxide films, a range of internal time constants (159–278 ns) for the switching process can be obtained. We use the memristors to create a reservoir computing network that classifies time-series input data and show that the reservoir computing system, which has tunable reservoirs, offers better classification accuracy and energy efficiency than previous reservoir system implementations.
AB - Memristive devices are of potential use in a range of computing applications. However, many of these devices are based on amorphous materials, where systematic control of the switching dynamics is challenging. Here we report tunable and stable memristors based on an entropy-stabilized oxide. We use single-crystalline (Mg,Co,Ni,Cu,Zn)O films grown on an epitaxial bottom electrode. By adjusting the magnesium composition (XMg = 0.11–0.27) of the entropy-stabilized oxide films, a range of internal time constants (159–278 ns) for the switching process can be obtained. We use the memristors to create a reservoir computing network that classifies time-series input data and show that the reservoir computing system, which has tunable reservoirs, offers better classification accuracy and energy efficiency than previous reservoir system implementations.
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U2 - 10.1038/s41928-024-01169-1
DO - 10.1038/s41928-024-01169-1
M3 - Article
AN - SCOPUS:85193611071
SN - 2520-1131
VL - 7
SP - 466
EP - 474
JO - Nature Electronics
JF - Nature Electronics
IS - 6
ER -
