A fully programmable two-dimensional photonic chip for machine learning

Tatsuhiro Onodera; Martin M. Stein; Benjamin A. Ash; Mandar M. Sohoni; Melissa J. Bosch; Ryotatsu Yanagimoto; Marc Jankowski; Timothy P. McKenna; Tianyu Wang; Gennady Shvets; Logan G. Wright; Peter L. McMahon

doi:10.1117/12.3029903

3 October 2024 A fully programmable two-dimensional photonic chip for machine learning

Tatsuhiro Onodera, Martin M. Stein, Benjamin A. Ash, Mandar M. Sohoni, Melissa J. Bosch, Ryotatsu Yanagimoto, Marc Jankowski, Timothy P. McKenna, Tianyu Wang, Gennady Shvets, Logan G. Wright, Peter L. McMahon

Author Affiliations +

Proceedings Volume PC13113, Photonic Computing: From Materials and Devices to Systems and Applications; PC1311303 (2024) https://doi.org/10.1117/12.3029903
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

On-chip photonic-neural-network processors have potential 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 relatively bulky discrete components connected by single-mode waveguides. A far more compact alternative is to avoid explicitly defining any components and instead sculpt the continuous substrate of the photonic processor to directly perform the computation using waves freely propagating in two dimensions. In this talk, I will present our recent work [1] on experimentally realizing this approach with a device whose refractive index as a function of space, n(x,z), can be rapidly reprogrammed. This device combines photoconductive gain with the electro-optic effect in a lithium niobate slab waveguide. Using this device, we performed neural-network inference with up to 49-dimensional input vectors in a single pass. [1]: T. Onodera*, M.M. Stein*, et al. arXiv:2402.17750 (2024)

Conference Presentation

Citation Download Citation

Tatsuhiro Onodera, Martin M. Stein, Benjamin A. Ash, Mandar M. Sohoni, Melissa J. Bosch, Ryotatsu Yanagimoto, Marc Jankowski, Timothy P. McKenna, Tianyu Wang, Gennady Shvets, Logan G. Wright, and Peter L. McMahon "A fully programmable two-dimensional photonic chip for machine learning", Proc. SPIE PC13113, Photonic Computing: From Materials and Devices to Systems and Applications, PC1311303 (3 October 2024); https://doi.org/10.1117/12.3029903

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