Proceedings of MATSUS Spring 2025 Conference (MATSUSSpring25)
DOI: https://doi.org/10.29363/nanoge.matsusspring.2025.320
Publication date: 16th December 2024
The ever-growing prevalence of AI and the IoT necessitates substantial increases in power consumption and data storage capacity. Memory storage devices that are fast, power-efficient, and have a high packing density are thereby gaining interest in the data storage industry. Neuromorphic computing (NC) fits resistive random-access memory (RRAM) devices well because of its decision-making and picture-recognition capabilities. A two-terminal resistive switching device based on Cu/CuO/FTO demonstrated excellent non-volatile RRAM appearances with 150 repeatable cycles, LRS and HRS stability in terms of retention is 1,000 s, and durability for 5000 cycles. The device was designed with inspiration from the human biological brain and was able to be synthesized at a low cost by thermal oxidation of Cu. The active material was CuO, with Cu and FTO serving as the top electrode and bottom contact. We investigated these devices' potential applications in neuromorphic computing. In addition, the devices show impressive mimicking abilities, displaying features such as synaptic weight, learning, and forgetting characteristics, spike time-dependent plasticity (STDP), and pulse-paired facilitation (PPF). In addition, the synaptic artificial neural network shows outstanding short-term (STP) and long-term (LTP) potentiation for six cycles in a row. Therefore, the current study on devices based on Cu/CuO/FTO offers a comprehensive analysis of resistive switching based on CuO active materials, which could lead to neuromorphic computing that goes beyond the von Neumann architecture.
Ambesh Dixit acknowledges SERB, DST, Government of India, through Project No. CRG/2020/004023 and Chandra Prakash acknowledge Advance Materials and Device Laboratory, Indian Institute of Technology, Jodhpur, for carrying out this work.
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