Automatic Design Methods For Optical Structures

Patent metadata

Field	Value
Publication number	US-2024070353-A1
Application number	US-202318237993-A
Country	US
Kind code	A1
Filing date	Aug 25, 2023
Priority date	Aug 25, 2022
Publication date	Feb 29, 2024
Grant date	—

Abstract

A computer system includes memory hardware configured to store a multitask neural network, an optimization model, a material database, material feature vector inputs, and computer-executable instructions which include training the multitask neural network with the material feature vector inputs to generate a material structural parameter output, obtaining at least one of a target optical perception parameter and a target optical response, supplying the target optical perception parameter or target optical response and at least two of the multiple material data structures of the material database to the multitask neural network to output the at least one predicted material and the predicted structural parameter distribution, processing, by the optimization model, the predicted structural parameter distribution to generate a tuned structural parameter output, and transmitting the at least one predicted material and the tuned structural parameter output to a computing device to facilitate generation of an optical structure.

First claim

What is claimed is:

1 . A computer system comprising:
memory hardware configured to store a multitask neural network, an optimization model, a material database, material feature vector inputs, and computer-executable instructions, wherein each material feature vector input includes at least one material structural parameter associated with an optical perception output, and wherein the material database includes multiple material data structures each including one or more material structural parameters and at least one of an electromagnetic property and an optical property associated with each material structural parameter; and processor hardware configured to execute the instructions, wherein the instructions include: training the multitask neural network with the material feature vector inputs to generate a material structural parameter output, wherein the material structural parameter output includes at least one predicted material and a predicted structural parameter distribution of the at least one predicted material; obtaining at least one of a target optical perception parameter and a target optical response; supplying the at least one target optical perception parameter or target optical response and at least two of the multiple material data structures of the material database to the multitask neural network to output the at least one predicted material and the predicted structural parameter distribution for generating the at least one target optical perception parameter or target optical response; processing, by the optimization model, the predicted structural parameter distribution to generate a tuned structural parameter output for generating the at least one target optical perception parameter or target optical response; and transmitting the at least one predicted material and the tuned structural parameter output to a computing device to facilitate generation of an optical structure including the at least one predicted material having the tuned structural parameter output.

2 . The system of claim 1 , wherein the optimization model is a particle swarm optimization model.

3 . The system of claim 1 , wherein the optical structure includes at least one of a multilayer thin film, a metasurface, a metamaterial, and self-assembled colloidal particles.

4 . The system of claim 1 , wherein:
the optical structure includes a multilayer thin film; and the predicted structural parameter distribution includes a thickness distribution of at least one layer of the multilayer thin film.

5 . The system of claim 4 , wherein:
the at least one target optical perception parameter or target optical response includes a visual color; the multilayer thin film includes at least one first dielectric material and at least one second dielectric material having a different refractive index than the first dielectric material; and the at least one predicted material includes a dielectric material selected from a group comprising Si, Ge, Al 2 O 3 , Fe 2 O 3 , HfO 2 , MgF 2 , SiO 2 , Ta 2 O 5 , TiO 2 , ZnO, ZnS and ZnSe.

6 . The system of claim 4 , wherein:
the at least one target optical perception parameter or target optical response includes a visual color; the multilayer thin film includes at least one metal material and at least one dielectric material; and the at least one predicted material includes a metal material selected from a group comprising Au, Ag, Al, Cu, Cr, Ge, Ni, Ti, W and Zn, and a dielectric material selected from a group comprising Al 2 O 3 , Fe 2 O 3 , HfO 2 , MgF 2 , SiO 2 , Ta 2 O 5 , TiO 2 , ZnO, ZnS and ZnSe.

7 . The system of claim 4 , wherein the at least one predicted material includes a light absorbing material selected from a group comprising inorganic compounds and organic compounds.

8 . The system of claim 1 , wherein the multitask neural network includes:
a classification network configured to determine the at least one predicted material; and a mixture density network configured to determine the predicted structural parameter distribution.

9 . The system of claim 8 , wherein training the multitask neural network includes training the classification network together with the mixture density network.

10 . The system of claim 1 , wherein:
the at least one target optical perception parameter or target optical response is selected from an optical parameter space; and the instructions further include generating the material feature vector inputs by uniformly sampling the optical parameter space.

11 . The system of claim 1 , wherein the instructions further include generating at least one of the material feature vector inputs to include a synthetic material generated according to material structural parameters of at least two of the material data structures.

12 . The system of claim 1 , wherein the tuned structural parameter output includes at least one of a material thickness, a diameter, a distance, a periodic pattern, a pitch, and a material shape.

13 . The system of claim 1 , wherein the at least one target optical perception parameter or target optical response includes one or a filtering characteristic of an optical structure and a light absorption characteristic of an optical structure.

14 . A method for generating optical structure material parameters, the method comprising:
training a multitask neural network with material feature vector inputs to generate a material structural parameter output, wherein each material feature vector input includes at least one material structural parameter associated with at least one of an optical perception output and an optical response output, a material database includes multiple material data structures each including one or more material structural parameters, and the material structural parameter output includes at least one predicted material and a predicted structural parameter distribution of the at least one predicted material; obtaining at least one of a target optical perception parameter and a target optical response; supplying the at least one target optical perception parameter or target optical response and at least two of the multiple material data structures of the material database to the multitask neural network to output the at least one predicted material and the predicted structural parameter distribution for generating the at least one target optical perception parameter or target optical response; processing, by an optimization model, the predicted structural parameter distribution to generate a tuned structural parameter output for generating the at least one target optical perception parameter or target optical response; and transmitting the at least one predicted material and the tuned structural parameter output to a computing device to facilitate generation of an optical structure including the at least one predicted material having the tuned structural parameter output.

15 . The method of claim 14 , wherein the optimization model is a particle swarm optimization model.

16 . The method of claim 14 , wherein the optical structure includes at least one of a multilayer thin film, a metasurface and self-assembled colloidal particles.

17 . The method of claim 14 , wherein:
the optical structure includes a multilayer thin film; and the predicted structural parameter distribution includes a thickness distribution of at least one layer of the multilayer thin film.

18 . The method of claim 17 , wherein:
the at least one target optical perception parameter or target optical response includes a visual color; the multil

Assignees

• Univ Michigan Regents

Inventors

• Wang Haozhu
• Guo Lingjie Jay

Classifications

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G06F2111/06

  Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA] · CPC title