  Electromagnetic (EM) simulation plays a crucial role in analyzing and
designing devices with sub-wavelength scale structures such as solar cells,
semiconductor devices, image sensors, future displays and integrated photonic
devices. 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 at https://github.com/kc-ml2/meent with the MIT license to promote
the cross-polinations of ideas among academic researchers and industry
practitioners.
