期刊文献+

Nonlinear encoding in diffractive information processing using linear optical materials

原文传递
导出
摘要 Nonlinear encoding of optical information can be achieved using various forms of data representation.Here,we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks.For a comprehensive evaluation,we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve,e.g.,phase encoding,against data repetition-based nonlinear encoding strategies.We show that data repetition within a diffractive volume(e.g.,through an optical cavity or cascaded introduction of the input data)causes the loss of the universal linear transformation capability of a diffractive optical processor.Therefore,data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks.However,they can still be effectively trained for specific inference tasks and achieve enhanced accuracy,benefiting from the nonlinear encoding of the input information.Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors.Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.
出处 《Light(Science & Applications)》 SCIE EI CSCD 2024年第8期1675-1688,共14页 光(科学与应用)(英文版)
基金 supported by the U.S.Department of Energy(DOE),Office of Basic Energy Sciences,Division of Materials Sciences and Engineering under Award#DE-SC0023088.
  • 相关文献

参考文献12

二级参考文献21

共引文献75

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部