Gradient-informed Bayesian and interior point optimization for efficient inverse design in nanophotonics

Area of Science:

• Nanophotonics
• Computational electromagnetics
• Optimization algorithms

Background:

• Inverse design is crucial for high-performance nanophotonic devices.
• Existing global optimization algorithms suffer from slow convergence.
• Local search methods often get stuck in local optima, hindering performance.

Purpose of the Study:

• Introduce BONNI (Bayesian optimization through neural network ensemble surrogates with interior point optimization) to overcome limitations of current nanophotonic device design methods.
• Enhance global optimization by integrating gradient information for efficient sampling.
• Demonstrate BONNI's effectiveness in designing nanophotonic components.

Main Methods:

• Utilized Bayesian optimization coupled with neural network ensemble surrogates.
• Incorporated interior point optimization to leverage gradient information.
• Applied the BONNI algorithm to design a distributed Bragg reflector and a dual-layer grating coupler.

Main Results:

• BONNI demonstrated superior performance compared to traditional optimization algorithms.
• Efficiently incorporated gradient information to identify optimal sampling points.
• Successfully designed complex nanophotonic structures like Bragg reflectors and grating couplers.

Conclusions:

• BONNI offers a more efficient and effective approach for nanophotonic inverse design.
• The method overcomes the convergence and local optima issues of previous algorithms.
• Validated through successful application in designing key nanophotonic devices.