MEENT: DIFFERENTIABLE ELECTROMAGNETIC SIMULATOR FOR MACHINE LEARNING
Keywords: computational physics, optics, electromagnetic simulation, reinforcement learning, neural operator, automatic differentiation, metasurface optimization, semiconductor metrology
TL;DR: Here we present meent, a Python-native differentiable electromagnetic simulation software, along with its application as an ML dataset generator, an RL environment, and an inverse problem solver.
Abstract: Electromagnetic (EM) simulation plays a crucial role in analyzing and designing devices with sub-wavelength scale structures such as semiconductor devices and future displays.
Specifically, optics problems such as estimating semiconductor device structures and designing nanophotonic devices provide intriguing research topics with far-reaching real world impact.
Traditional algorithms for such tasks require iteratively refining parameters through simulations, which often yield sub-optimal results due to the high computational cost of both the algorithms and EM simulations.
Machine learning (ML) emerged as a promising candidate to mitigate these challenges, and optics research community has increasingly adopted ML algorithms to obtain results surpassing classical methods across various tasks.
To foster a synergistic collaboration between the optics and ML communities, it is essential to have an EM simulation software that is user-friendly for both research communities.
To this end, we present meent, an EM simulation software that employs rigorous coupled-wave analysis (RCWA). Developed in Python and equipped with automatic differentiation (AD) capabilities, meent serves as a versatile platform for integrating ML into optics research and vice versa.
To demonstrate its utility as a research platform, we present three applications of meent: 1) generating a dataset for training neural operator, 2) serving as an environment for the reinforcement learning of nanophotonic device optimization, and 3) providing a solution for inverse problems with gradient-based optimizers.
These applications highlight meent's potential to advance both EM simulation and ML methodologies.
The code is available on our Github repository with the MIT license to promote the cross-polinations of ideas among academic researchers and industry practitioners.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 8631
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