Photonic-Electronic Memristors for Neuromorphic Applications

Details are subject to change.

  • Organiser

    Alexandros Emboras, ETH Zurich, Switzerland

  • Day & Time

    21.09.2022, 15:45 – 17:30

  • Location

    Room Kairo

  • Description

    Today’s artificial intelligence (AI) performance has been significantly improved thanks to the CMOS technology and the high computational power brought by graphics processing units (GPUs) and application specific integrated circuits (ASICs). However, to keep up with this trend, a critical problem should be solved, the inherent high energy consumption induced by the continuous exchange of data between the memory and computing units, which are physically separated. This issue is known as the “von Neuman bottleneck”.

    Several innovations in the field of information technology have shown promise in overcoming this fundamental limit. For example, recent developments of memristors, a class of two-terminal nano-devices with a variable resistance, enables the collocation of the computing and storing functionalities, thus circumventing the limitations of current von Neumann designs. On the other hand, progress in standard photonic circuits allows for high-bandwidth optical data communication. Ideally, a photonic-electronic platform is desired that can simultaneously take advantage of the high density and non-volatility of electronic memristors and of the high-speed communication capabilities provided by photonics/plasmonics components. In this symposium, we will discuss the challenges and opportunities of this platform.

    The symposium is divided in three sessions: Session 1 will cover the theoretical aspects related to the understanding of the interplay between photonic, electronic, phononic and ionic interactions within memristors. Session 2 will focus on the materials needed for novel memristive material stacks. Session 3 will be related to the device engineering and novel opto-electronic applications

  • Speakers

    Speaker 1: Timoleon Moraitis, Huawei Technologies – Zurich Research Center, Switzerland
    Title: Outperforming machine learning, through biological models with memristive analogues

    Speaker 2: Ilia Valov, Research Centre Jülich, Germany

    Speaker 3: Jacquiline Geler-KremerIBM Research−Europe, Switzerland
    A BaTiO3 ferroelectric multilevel non-volatile photonic phase shifter

    Speaker 4: Melika Payvand, University of Zurich, Switzerland & ETH Zurich, Switzerland

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