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Enhancing Adjoint Optimization-Based Photonic Inverse Designwith Explainable Machine Learning
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DOI：10.1021/acsphotonics.1c01636.png)
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A fundamental challenge in the design of photonic devices, andelectromagnetic structures more generally, is the optimization of their overall architectureto achieve a desired response. To this end, topology or shape optimizers based on theadjoint variable method have been widely adopted due to their high computationalefficiency and ability to create complex freeform geometries. However, the functionalunderstanding of such freeform structures remains a black box. Moreover, unless a designspace of high-performance devices is known in advance, such gradient-based optimizerscan get trapped in local minima valleys or saddle points, which limits performanceachievable through this inverse design process. To elucidate the relationships betweendevice performance and nanoscale structuring while mitigating the effects of local minimatrapping, we present an inverse design framework that combines adjoint optimization,automated machine learning, and explainable artificial intelligence. Integrated with anumerical electromagnetic simulation method, our framework reveals structural contributions toward afigure-of-merit (FOM) ofinterest. Through an explanation-based reoptimization process, this information is then leveraged to minimize the FOM further thanthat obtained through adjoint optimization alone, thus overcoming the optimization's local minima. We demonstrate our frameworkin the context of waveguide splitter design and achieve between 39 and 74% increases in device performance relative to state-of-the-art adjoint optimization-based inverse design across a range of telecom wavelengths. Our results highlight machine learning strategiesthat can substantially extend and enhance the capabilities of a conventional, optimization-based inverse design algorithm whilerevealing deeper insights into the algorithm's designs
Keyword:
nanophotonics
deep learning
explainability
adjoint optimization
automated machine learning
期刊
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6.7
论文数:
5.6K
被引数:
2.5W
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