LiNbO3-based memristors for neuromorphic computing applications: a review

Abstract

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Neuromorphic computing is a promising paradigm for developing energy-efficient and high-performance artificial intelligence systems. The unique properties of lithium niobate-based (LiNbO3)-based memristors, such as low power consumption, non-volatility, and high-speed switching, make them ideal candidates for synaptic emulation in neuromorphic systems. This study investigates the potential of LiNbO3-based memristors to revolutionize neuromorphic computing by exploring their synaptic behavior and optimizing device parameters, as well as harnessing the potential of LiNbO3-based memristors to create efficient and high-performance neuromorphic computing systems. By realizing efficient and high-speed neural networks, this literature review aims to pave the way for innovative artificial intelligence systems capable of addressing complex real-world challenges. The results obtained from this investigation will be crucial for future researchers and engineers working on designing and implementing LiNbO3-based neuromorphic computing architectures.

Keywords

• neuromorphic computing
• LiNbO3-based memristors
• synaptic behavior
• nonlinear optical properties
• artificial intelligence
• advanced fabrication techniques