Investigation of inverse design of multilayer thin-films with conditional invertible Neural Networks

10/10/2022
by   Alexander Luce, et al.
12

The task of designing optical multilayer thin-films regarding a given target is currently solved using gradient-based optimization in conjunction with methods that can introduce additional thin-film layers. Recently, Deep Learning and Reinforcement Learning have been been introduced to the task of designing thin-films with great success, however a trained network is usually only able to become proficient for a single target and must be retrained if the optical targets are varied. In this work, we apply conditional Invertible Neural Networks (cINN) to inversely designing multilayer thin-films given an optical target. Since the cINN learns the energy landscape of all thin-film configurations within the training dataset, we show that cINNs can generate a stochastic ensemble of proposals for thin-film configurations that that are reasonably close to the desired target depending only on random variables. By refining the proposed configurations further by a local optimization, we show that the generated thin-films reach the target with significantly greater precision than comparable state-of-the art approaches. Furthermore, we tested the generative capabilities on samples which are outside the training data distribution and found that the cINN was able to predict thin-films for out-of-distribution targets, too. The results suggest that in order to improve the generative design of thin-films, it is instructive to use established and new machine learning methods in conjunction in order to obtain the most favorable results.

READ FULL TEXT

page 4

page 5

page 6

page 7

page 9

page 12

research
03/16/2020

Inverse design of multilayer nanoparticles using artificial neural networks and genetic algorithm

The light scattering of multilayer nanoparticles can be solved by Maxwel...
research
11/24/2021

TMM-Fast: A Transfer Matrix Computation Package for Multilayer Thin-Film Optimization

Achieving the desired optical response from a multilayer thin-film struc...
research
09/14/2019

Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks

We report a broadband diffractive optical neural network design that sim...
research
08/14/2019

Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once

Modern deep neural networks are often vulnerable to adversarial samples....
research
09/22/2018

Comment on All-optical machine learning using diffractive deep neural networks

Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable pr...
research
08/27/2018

Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks

Deep learning is known to be data-hungry, which hinders its application ...

Please sign up or login with your details

Forgot password? Click here to reset