micromode 0.1.0a3

Rust-backed photonics mode solver with a small Python API.

micromode

An electromagnetic mode solver using the FDFD method on a regular Yee-grid, written in native Rust.

pip install micromode

Why Use It?

• Grid-first API: pass arrays directly, with no required geometry model.
• Fast, portable Rust sparse backend: one production solve path.
• Practical outputs: fields, n_eff, k_eff, mode area, polarization fractions, Lorentz overlaps, plotting, dataframe export, and HDF5 save/load.
• Tensor-aware: supports scalar, diagonal anisotropic, and full tensor material grids.
• Works for both 2D cross sections and 1D slices.

You give it a material grid. It returns guided modes: effective indices, six-component fields, polarization metrics, mode area, overlaps, diagnostics, plots, and HDF5 output. MicroMode is intentionally not a CAD or geometry package. It is the solver piece you use after geometry has already been rasterized onto a mode-plane grid.

Micromode is the default mode solver in the BEAMZ FDTD engine.

Quick Start

import micromode as mm

wavelength_um = 1.55
freq = mm.C_0 / wavelength_um

# Arrays from your own rasterizer.
eps_xx, x_edges, y_edges = mode_plane_arrays(...)

materials = mm.Materials.from_diagonal(
    eps_xx=eps_xx,
    x_edges=x_edges,
    y_edges=y_edges,
)

data = mm.solve_modes(
    material_grid=materials,
    freqs=[freq],
    num_modes=2,
    target_neff=2.5,
)

print(data.n_eff.values)
data.plot_field("Ex", mode_index=0)
data.to_hdf5("modes.h5")

High Performance

MicroMode is designed to make high-performance mode solving available without requiring users to install external solver stacks. The production backend is a portable Rust sparse shift-invert eigensolver, so source installs and wheels do not depend on ARPACK, UMFPACK, SuiteSparse, BLAS/ LAPACK, or a Fortran compiler. That matters for simulation workflows that need to run in CI, notebooks, container images, FDTD plugins, and cross-platform design tools.

The native solver is not a dense fallback. It uses sparse finite-difference operators throughout, applies AMD fill-reducing ordering before sparse LU factorization, stores LU factors in a packed format for repeated triangular solves, and runs an Arnoldi iteration targeted around the requested effective index. The Arnoldi stage uses shift-invert, adaptive Ritz-pair checkpointing, early stopping once requested modes are stable, and selective Ritz vector reconstruction so work is spent on the modes that will actually be returned.

On the repository benchmark problem, the pure Rust backend solves larger grids in the same performance class as the previous optional UMFPACK-backed path while remaining much easier to install and distribute. For example, a release build on an Apple Silicon development machine solves an 80x50 diagonal benchmark grid in roughly 90 ms for two modes with residuals around 1e-12. Exact timings depend on hardware and problem shape, but the important point is architectural: MicroMode keeps the deployability of a pure Rust package without giving up the sparse-solver performance expected for practical waveguide grids.