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Ultraprecision, high-capacity, and wide-gamut structural colors enabled by a mixture probability sampling network.

Zeyong Wei^1,2,3,4,5, Weijie Xu^1,2,3,4,5, Siyu Dong^1,2,3,4,5

¹School of Physics Science and Engineering, Tongji University, Shanghai, China.

Light, Science & Applications

|March 11, 2026

Summary

This summary is machine-generated.

A new Mixture Probability Sampling Network (MPSN) improves nanophotonic inverse design by enhancing accuracy for complex structures. This method optimizes structural configurations for advanced device performance.

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Area of Science:

Nanophotonics
Computational electromagnetics
Materials science

Background:

High-precision inverse design is crucial for advancing nanophotonic devices.
Existing methods like mixture density networks (MDN) struggle with complex structures due to the one-to-many mapping between optical performance and structure.
Higher degrees of freedom in structural configurations limit the accuracy of current inverse design approaches.

Purpose of the Study:

To develop a novel, high-precision inverse design framework for nanophotonic devices.
To overcome the accuracy limitations of existing methods when dealing with complex structural designs.
To enable the creation of enhanced and multifunctional nanophotonic devices through improved inverse design.

Main Methods:

Proposing a sampling-enhanced MDN, termed Mixture Probability Sampling Network (MPSN).
Utilizing an end-to-end framework that outputs mixture Gaussian distributions (MGDs) of structural parameters.
Implementing a selection strategy where samples minimizing error relative to real data are used for network training.

Main Results:

Achieved high precision up to 99.9% in nanophotonic structural color design.
Obtained a mean absolute error of less than 0.002.
Demonstrated superior performance in inverse design for structures with higher degrees of freedom.

Conclusions:

The proposed MPSN effectively addresses the challenges of one-to-many mapping in nanophotonic inverse design.
This work provides a pathway for resolving intricate inverse design problems in nanophotonics.
The developed framework facilitates the design of sophisticated nanophotonic devices with enhanced functionalities.