NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.

Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.

U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

PUBLICATIONS

Analog end-to-end adaptive training with locally-updated physical gradients on integrated photonic platform

Published

Author(s)

Zhimu Guo, Aadhi A, Adam McCaughan, Nathan Youngblood, Alexander Tait, Sonia Buckley, Bhavin Shastri
Citation
Nature Communications
Pub Type
Journals
Nanophotonics and Artificial intelligence

Citation

Guo, Z. , A, A. , McCaughan, A. , Youngblood, N. , Tait, A. , Buckley, S. and Shastri, B. (2025), Analog end-to-end adaptive training with locally-updated physical gradients on integrated photonic platform, Nature Communications (Accessed October 2, 2025)

Issues

If you have any questions about this publication or are having problems accessing it, please contact [email protected].

Created June 22, 2025, Updated July 11, 2025
Was this page helpful?