Nested deep transfer learning for modeling of multilayer thin films  

作　　者：Rohit Unni Kan Yao Yuebing Zheng 

机构地区：[1]aUniversity of Texas at Austin,Walker Department of Mechanical Engineering,Austin,Texas,United States [2]bUniversity of Texas at Austin,Texas Materials Institute,Austin,Texas,United States

出　　处：《Advanced Photonics》2024年第5期95-103,共9页先进光子学(英文)

基　　金：support of the National Institute of General Medical Sciences of the National Institutes of Health(1R01GM146962-01).

摘　　要：Machine-learning techniques have gained popularity in nanophotonics research,being applied to predict optical properties,and inversely design structures.However,one limitation is the cost of acquiring training data,as complex structures require time-consuming simulations.To address this,researchers have explored using transfer learning,where pretrained networks can facilitate convergence with fewer data for related tasks,but application to more difficult tasks is still limited.In this work,a nested transfer learning approach is proposed,training models to predict structures of increasing complexity,with transfer between each model and few data used at each step.This allows modeling thin film stacks with higher optical complexity than previously reported.For the forward model,a bidirectional recurrent neural network is utilized,which excels in modeling sequential inputs.For the inverse model,a convolutional mixture density network is employed.In both cases,a relaxed choice of materials at each layer is introduced,making the approach more versatile.The final nested transfer models display high accuracy in retrieving complex arbitrary spectra and matching idealized spectra for specific application-focused cases,such as selective thermal emitters,while keeping data requirements modest.Our nested transfer learning approach represents a promising avenue for addressing data acquisition challenges.

关 键 词：artificial neural networks multilayer structures NANOPHOTONICS inverse design transfer learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O43[自动化与计算机技术—控制科学与工程]