Presentation + Paper
18 June 2024 Arbitrarily programmable wave propagation on a photonic chip
Author Affiliations +
Abstract
On-chip photonic-neural-network processors promise benefits in both speed and energy efficiency but have not yet reached the scale to compete with electronic processors. The dominant paradigm is to build integrated-photonic processors using discrete components connected by single-mode waveguides. A far more compact alternative is to avoid discrete components and instead sculpt a complex and continuous microphotonic medium in which computations are performed by multimode waves controllably propagating in two dimensions. We show our realization of this approach with a device whose refractive index as a function of space can be rapidly reprogrammed. We demonstrate optical computations much larger and more error-resilient than previous photonic chips relying on discrete components. We argue that beyond photonic-neural-network processors, devices with such arbitrarily programmable index distributions enable the realization of a wide range of photonic functionality.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Martin M. Stein, Tatsuhiro Onodera, Benjamin A. Ash, Mandar M. Sohoni, Melissa Bosch, Ryotatsu Yanagimoto, Marc Jankowski, Timothy P. McKenna, Tianyu Wang, Gennady Shvets, Maxim R. Shcherbakov, Logan G. Wright, and Peter L. McMahon "Arbitrarily programmable wave propagation on a photonic chip", Proc. SPIE 13017, Machine Learning in Photonics, 130170M (18 June 2024); https://doi.org/10.1117/12.3017660
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KEYWORDS
Waveguides

Wave propagation

Education and training

Neural networks

Lithium niobate

Modulation

Integrated optics

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