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Optical Memristors: Review of Switching Mechanisms and New Computing Paradigms

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Memristor Computing Systems

Abstract

Memristors are known for their low-power non-volatile memory operation, high scalability and simple two-terminal geometry. Their ability to emulate the analogue switching and learning properties of biological synapses has also emerged as a significant area of importance that has the potential to usher in a new generation of bio-inspired neuromorphic computing systems. Recently, there has been a drive towards the realization of memristor devices that can be controlled by light. The integration of non-volatile electronic memory with high speed and high bandwidth optical signalling provides a natural platform for applications in optical telecommunications and photonic computing. There are also significant advantages to be gained by utilizing the light tuneable properties of these artificial synapses and their already wide range of demonstrated neuronal functions, an important topic especially relevant as we enter the era of post von Neumann computing and the need for increased computational power to fuel new Artificial Intelligence applications, the Internet of Things, Big Data and Edge Computing. A variety of different physical mechanisms have been exploited in the development of optical memristors, including barrier modification, photo-induced molecular switching processes, plasmonic interactions with nanoscale conductive filaments and photogating mechanisms in 2-D materials. This review will examine the different methods used to achieve optical memristor switching and discuss their potential future applications in photonic and neuromorphic computing.

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  1. Department of Physics and Mathematics, University of Hull, Hull, HU6 7RX, UK

    Alex Gee, Ayoub H. Jaafar & N. T. Kemp

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  1. Alex Gee
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  2. Ayoub H. Jaafar
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  3. N. T. Kemp
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  1. Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkley, CA, USA

    Leon O. Chua

  2. Fundamentals of Electrical Engineering, TU Dresden, Dresden, Germany

    Ronald Tetzlaff

  3. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Angela Slavova

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Gee, A., Jaafar, A.H., Kemp, N.T. (2022). Optical Memristors: Review of Switching Mechanisms and New Computing Paradigms. In: Chua, L.O., Tetzlaff, R., Slavova, A. (eds) Memristor Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-90582-8_10

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