Machine learning with complex wave propagation in inverse-designed photonic device

Tatsuhiro Onodera; Martin Stein; Benjamin Ash; Logan Wright; Peter McMahon

doi:10.1117/12.3002762

13 March 2024 Machine learning with complex wave propagation in inverse-designed photonic device

Tatsuhiro Onodera, Martin Stein, Benjamin Ash, Logan Wright, Peter McMahon

Proceedings Volume PC12903, AI and Optical Data Sciences V; PC1290303 (2024) https://doi.org/10.1117/12.3002762
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Deep learning acceleration with integrated photonics has typically employed a circuit-centric approach with Mach-Zehnder interferometers. This requires a large spatial footprint, which has motivated the direct training of spatial refractive index distribution within a slab waveguide. Here, we demonstrate through simulations that nonlinear optical material platforms with large electro-optic coefficients can capitalize on this approach. We show that a linear device with realistic device parameters can perform 50 by 50 unitary matrix multiplications. We also performed MNIST digit classification, achieving 90.5% classification accuracy with minimal digital preprocessing. Finally, we comment on device implementation with Lithium Niobate or Barium Titanate slab waveguides.

Conference Presentation

Citation Download Citation

Tatsuhiro Onodera, Martin Stein, Benjamin Ash, Logan Wright, and Peter McMahon "Machine learning with complex wave propagation in inverse-designed photonic device", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC1290303 (13 March 2024); https://doi.org/10.1117/12.3002762

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available