Yesilyurt Omer, Peana Samuel, Mkhitaryan Vahagn, Pagadala Karthik, Shalaev Vladimir M, Kildishev Alexander V, Boltasseva Alexandra
Elmore Family School of Electrical and Computer Engineering, Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN 47907, USA.
The Quantum Science Center (QSC), a National Quantum Information Science Research Center of the U.S. Department of Energy (DOE), Oak Ridge, TN 37931, USA.
Nanophotonics. 2023 Jan 30;12(5):993-1006. doi: 10.1515/nanoph-2022-0537. eCollection 2023 Mar.
Multilayer films with continuously varying indices for each layer have attracted great deal of attention due to their superior optical, mechanical, and thermal properties. However, difficulties in fabrication have limited their application and study in scientific literature compared to multilayer films with fixed index layers. In this work we propose a neural network based inverse design technique enabled by a differentiable analytical solver for realistic design and fabrication of single material variable-index multilayer films. This approach generates multilayer films with excellent performance under ideal conditions. We furthermore address the issue of how to translate these ideal designs into practical useful devices which will naturally suffer from growth imperfections. By integrating simulated systematic and random errors just as a deposition tool would into the optimization process, we demonstrated that the same neural network that produced the ideal device can be retrained to produce designs compensating for systematic deposition errors. Furthermore, the proposed approach corrects for systematic errors even in the presence of random fabrication imperfections. The results outlined in this paper provide a practical and experimentally viable approach for the design of single material multilayer film stacks for an extremely wide variety of practical applications with high performance.
每层折射率连续变化的多层膜因其优异的光学、机械和热性能而备受关注。然而，与具有固定折射率层的多层膜相比，制造上的困难限制了它们在科学文献中的应用和研究。在这项工作中，我们提出了一种基于神经网络的逆向设计技术，该技术由一个可微分析求解器实现，用于单材料可变折射率多层膜的实际设计和制造。这种方法在理想条件下生成具有优异性能的多层膜。我们还解决了如何将这些理想设计转化为实际有用器件的问题，因为实际器件自然会存在生长缺陷。通过将模拟的系统误差和随机误差（就像沉积工具那样）整合到优化过程中，我们证明了用于生成理想器件的同一个神经网络可以重新训练，以生成能够补偿系统沉积误差的设计。此外，所提出的方法即使在存在随机制造缺陷的情况下也能校正系统误差。本文概述的结果为设计用于极其广泛的高性能实际应用的单材料多层膜堆栈提供了一种实用且在实验上可行的方法。
