Large permittivity increments for efficient predictive photonic devices optimization

Salim Boutami; Nathan Zhao; Shanhui Fan

doi:10.1117/12.2545703

26 February 2020 Large permittivity increments for efficient predictive photonic devices optimization

Salim Boutami, Nathan Zhao, Shanhui Fan

Proceedings Volume 11290, High Contrast Metastructures IX; 112900Q (2020) https://doi.org/10.1117/12.2545703
Event: SPIE OPTO, 2020, San Francisco, California, United States

Abstract

In In this paper, we show two novel approaches for photonic device optimization. Both approaches exploit the Lippmann- Schwinger equation, and can be applied with significant gains in computational efficiency when used with adjoint variable method. The first method optimizes a binarized device by greedily proposing and evaluating the effect of changing different pixels in the design region. Using the update of the Green’s function of the system with Dyson’s equation, one can guarantee the improvement of the figure of merit even for a large discrete binary change. The final structure is binary and guarantees fabricability with varying minimum feature sizes. In the second approach, we develop a fast algorithm to perform a line search for continuous optimization with gradient descent. The algorithm enables the line search to be executed faster than evaluating a new gradient, making such a line search extremely valuable. This line search is based on a Shanks transformation of the series expansion of the Lippmann-Schwinger equation, which enables us to determine the optimal learning rate in the search direction and minimize the number of separate iterations needed to achieve an optimal device.

Conference Presentation

Citation Download Citation

Salim Boutami, Nathan Zhao, and Shanhui Fan "Large permittivity increments for efficient predictive photonic devices optimization", Proc. SPIE 11290, High Contrast Metastructures IX, 112900Q (26 February 2020); https://doi.org/10.1117/12.2545703

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