TY - JOUR
T1 - Influence of the TiN diffusion barrier on the leakage current and ferroelectricity in an Al-doped HfOx ferroelectric memristor and its application to neuromorphic computing
AU - Lim, Eunjin
AU - Seo, Euncho
AU - Kim, Sungjun
N1 - Publisher Copyright:
© 2024 The Royal Society of Chemistry.
PY - 2024/9/23
Y1 - 2024/9/23
N2 - The HfOx-based ferroelectric memristor is in the spotlight due to its complementary metal-oxide-semiconductor compatibility and scaling compared to existing perovskite-based ferroelectric memory. However, ferroelectric properties vary depending on the coefficient of thermal expansion of the top electrode, which is caused by strain engineering. When tungsten (W) with a small coefficient of thermal expansion is used as an electrode, the ferroelectric properties are improved, although the reliability is poor due to the diffusion of W atoms. Here, TiN can be used to prevent the diffusion of W. This metal nitride successfully suppresses the leakage current and induces a larger remanent polarization of 19.7 μC cm−2, a smaller coercive voltage of 9.26 V, and a faster switching speed. W/TiN/HAO/n+ Si can also exhibit multi-level characteristics and achieve a 10% read margin in 320 × 320 arrays. Ferroelectrics can also be applied to neuromorphic computing by imitating synaptic properties such as potentiation, depression, paired-pulse facilitation, and excitatory postsynaptic current. Using short-term plasticity, successful implementation in reservoir computing is also realized, achieving 95% classification accuracy. This paper shows promise for the use of memristors in artificial neural networks.
AB - The HfOx-based ferroelectric memristor is in the spotlight due to its complementary metal-oxide-semiconductor compatibility and scaling compared to existing perovskite-based ferroelectric memory. However, ferroelectric properties vary depending on the coefficient of thermal expansion of the top electrode, which is caused by strain engineering. When tungsten (W) with a small coefficient of thermal expansion is used as an electrode, the ferroelectric properties are improved, although the reliability is poor due to the diffusion of W atoms. Here, TiN can be used to prevent the diffusion of W. This metal nitride successfully suppresses the leakage current and induces a larger remanent polarization of 19.7 μC cm−2, a smaller coercive voltage of 9.26 V, and a faster switching speed. W/TiN/HAO/n+ Si can also exhibit multi-level characteristics and achieve a 10% read margin in 320 × 320 arrays. Ferroelectrics can also be applied to neuromorphic computing by imitating synaptic properties such as potentiation, depression, paired-pulse facilitation, and excitatory postsynaptic current. Using short-term plasticity, successful implementation in reservoir computing is also realized, achieving 95% classification accuracy. This paper shows promise for the use of memristors in artificial neural networks.
UR - http://www.scopus.com/inward/record.url?scp=85205915730&partnerID=8YFLogxK
U2 - 10.1039/d4nr02961e
DO - 10.1039/d4nr02961e
M3 - Article
C2 - 39350693
AN - SCOPUS:85205915730
SN - 2040-3364
VL - 16
SP - 19445
EP - 19452
JO - Nanoscale
JF - Nanoscale
IS - 41
ER -