TY - JOUR
T1 - Interface engineering in ZnO/CdO hybrid nanocomposites to enhanced resistive switching memory for neuromorphic computing
AU - Ghafoor, Faisal
AU - Kim, Honggyun
AU - Ghafoor, Bilal
AU - Rehman, Shania
AU - Asghar Khan, Muhammad
AU - Aziz, Jamal
AU - Rabeel, Muhammad
AU - Faheem Maqsood, Muhammad
AU - Dastgeer, Ghulam
AU - Lee, Myoung Jae
AU - Farooq Khan, Muhammad
AU - Kim, Deok kee
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2024/4
Y1 - 2024/4
N2 - Resistive random-access memory (RRAMs) has attracted significant interest for their potential applications in embedded storage and neuromorphic computing. Materials based on metal chalcogenides have emerged as promising candidates for the fulfilment of these requirements. Due to its ability to manipulate electronic states and control trap states through controlled compositional dynamics, metal chalcogenide RRAM has excellent non-volatile resistive memory properties. In the present we have synthesized ZnO-CdO hybrid nanocomposite by using hydrothermal method as an active layer. The Ag/C15ZO/Pt hybrid nanocomposite structure memristors showed electrical properties similar to biological synapses. The device exhibited remarkably stable resistive switching properties that have a low SET/RESET (0.41/−0.2) voltage, a high RON/OFF ratio of approximately 105, a high retention stability, excellent endurance reliability up to 104 cycles and multilevel device storage performance by controlling the compliance current. Furthermore, they exhibited an impressive performance in terms of emulating biological synaptic functions, which include long-term potentiation (LTP), long-term depression (LTD), and paired-pulse facilitation (PPF), via the continuous modulation of conductance. The hybrid nanocomposite memristors notably achieved an impressive recognition accuracy of up to 92.6 % for handwritten digit recognition under artificial neural network (ANN). This study shows that hybrid-nanocomposite memristor performance could lead to efficient future neuromorphic architectures.
AB - Resistive random-access memory (RRAMs) has attracted significant interest for their potential applications in embedded storage and neuromorphic computing. Materials based on metal chalcogenides have emerged as promising candidates for the fulfilment of these requirements. Due to its ability to manipulate electronic states and control trap states through controlled compositional dynamics, metal chalcogenide RRAM has excellent non-volatile resistive memory properties. In the present we have synthesized ZnO-CdO hybrid nanocomposite by using hydrothermal method as an active layer. The Ag/C15ZO/Pt hybrid nanocomposite structure memristors showed electrical properties similar to biological synapses. The device exhibited remarkably stable resistive switching properties that have a low SET/RESET (0.41/−0.2) voltage, a high RON/OFF ratio of approximately 105, a high retention stability, excellent endurance reliability up to 104 cycles and multilevel device storage performance by controlling the compliance current. Furthermore, they exhibited an impressive performance in terms of emulating biological synaptic functions, which include long-term potentiation (LTP), long-term depression (LTD), and paired-pulse facilitation (PPF), via the continuous modulation of conductance. The hybrid nanocomposite memristors notably achieved an impressive recognition accuracy of up to 92.6 % for handwritten digit recognition under artificial neural network (ANN). This study shows that hybrid-nanocomposite memristor performance could lead to efficient future neuromorphic architectures.
KW - Cadmium oxide (CdO)
KW - Conductive filament (CF)
KW - Nanocomposite (NC)
KW - Oxygen vacancies (Vo)
KW - Zinc oxide (ZnO)
UR - http://www.scopus.com/inward/record.url?scp=85181157325&partnerID=8YFLogxK
U2 - 10.1016/j.jcis.2023.12.084
DO - 10.1016/j.jcis.2023.12.084
M3 - Article
C2 - 38157721
AN - SCOPUS:85181157325
SN - 0021-9797
VL - 659
SP - 1
EP - 10
JO - Journal of Colloid and Interface Science
JF - Journal of Colloid and Interface Science
ER -