Utilizing Mixed Training and Multi-Head Attention to Address Data Shift in AI-Based Electromagnetic Solvers for...

Area of Science:

• Computational electromagnetics
• Artificial intelligence in materials science
• Nanophotonics and metamaterials

Background:

• Iterative optimization methods for nano-structured metamaterials rely on numerical solvers, which are computationally expensive.
• Deep learning solvers offer a faster alternative but face challenges like data shift, impacting performance.
• Data shift, a subtle yet critical issue, can degrade the accuracy of AI models in electromagnetic simulations.

Purpose of the Study:

• To explore the data shift challenge in AI-based electromagnetic solvers for metamaterial design.
• To demonstrate the presence and impact of data shift using a case study of a 1D grating coupler.
• To propose and validate strategies for mitigating data shift effects in AI-driven electromagnetic design.

Main Methods:

• Utilized a one-dimensional grating coupler as a case study for analyzing data shift.
• Employed probability density method and principal component analysis to detect and visualize data shift.
• Implemented and evaluated three mitigation strategies: mixed training, multi-head attention, and a combined approach.

Main Results:

• Demonstrated significant performance degradation of neural networks when trained on data affected by data shift.
• Validated the efficacy of proposed strategies in addressing data shift.
• The combined approach of mixed training and multi-head attention reduced mean absolute error by approximately 36% on shifted data.

Conclusions:

• Data shift is a critical challenge in AI-based electromagnetic solvers for metamaterial design.
• Mixed training and multi-head attention are effective strategies to mitigate data shift.
• This work provides essential guidance for developing robust AI solvers for optimal nano-structured metamaterial design.