基于梯度信息的贝叶斯和内点优化用于纳米光子学中高效反向设计
Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics
AI总结：
本文提出BONNI算法，结合贝叶斯优化和内点优化，通过高效利用梯度信息提升纳米光子学反向设计效率，实现更优的分布式布拉格反射器和双层耦合器设计。
摘要：
反向设计，特别是几何形状优化，为开发高性能纳米光子学器件提供了系统方法。尽管存在多种优化算法，但先前的全局方法收敛缓慢，而局部搜索策略常陷入局部极值。为解决局部和全局方法的固有局限，我们引入BONNI：通过神经网络集合代理进行贝叶斯优化并结合内点优化。该方法通过高效整合梯度信息来确定最优采样点，使BONNI能够绕过许多纳米光子应用中的局部极值，同时利用基于梯度的优化效率。我们通过与文献中常用优化算法的全面比较，展示了BONNI在分布式布拉格反射器和双层耦合器设计中的能力。使用BONNI，我们仅以4.5%的平均频谱误差设计出10层分布式布拉格反射器，相比之前报道的7.8%误差和16层结果。进一步设计宽带波导渐变和光子晶体波导转换验证了BONNI的能力。
英文摘要：
Inverse design, particularly geometric shape optimization, provides a systematic approach for developing high-performance nanophotonic devices. While numerous optimization algorithms exist, previous global approaches exhibit slow convergence and conversely local search strategies frequently become trapped in local optima. To address the limitations inherent to both local and global approaches, we introduce BONNI: Bayesian optimization through neural network ensemble surrogates with interior point optimization. It augments global optimization with an efficient incorporation of gradient information to determine optimal sampling points. This capability allows BONNI to circumvent the local optima found in many nanophotonic applications, while capitalizing on the efficiency of gradient-based optimization. We demonstrate BONNI's capabilities in the design of a distributed Bragg reflector as well as a dual-layer grating coupler through an exhaustive comparison against other optimization algorithms commonly used in literature. Using BONNI, we were able to design a 10-layer distributed Bragg reflector with only 4.5% mean spectral error, compared to the previously reported results of 7.8% error with 16 layers. Further designs of a broadband waveguide taper and photonic crystal waveguide transition validate the capabilities of BONNI.
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