Lithium based memristive device

Wang, Shu; Wang, Weisong; Yakopcic, Chris; Shin, Eunsung; Kim, Richard S.; Subramanyam, Guru; Taha, Tarek M.

doi:10.1109/naecon.2015.7443092

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…One of the defining features that positions LiNbO 3 as a frontrunner in neuromorphic computing is its non-volatile nature, which allows for the retention of synaptic weights even when power is removed. Xu et al (2023) and Wang et al (2015) discuss how this characteristic allows for the retention of synaptic weights even in the absence of power, mimicking the persistent connectivity of biological synapses. Besides, Wang et al (2017) show that the ferroelectric property of LiNbO 3 enables the retention of synaptic weights even when power is removed.…”

Section: Non-volatility and Low Power Consumptionmentioning

confidence: 99%

“…LiNbO 3 -based memristors also exhibit low power consumption, addressing the demand for energy-efficient computing systems, as revealed by Zaman et al (2017), Xu et al (2023), Wang et al (2015), and Nakajima et al (2022). Examining the energy efficiency of LiNbO 3 , Zaman (2020) discusses how the material's non-volatility contributes to low power consumption during synaptic emulation.…”

Section: Non-volatility and Low Power Consumptionmentioning

confidence: 99%

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LiNbO3-based memristors for neuromorphic computing applications: a review

Kibebe,

Liu

2024

Front. Electron. Mater

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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.

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Section: Non-volatility and Low Power Consumptionmentioning

confidence: 99%