Open-source CMOS image sensor optics platform — define your pixel stack once and run it through 9 RCWA/FDTD/TMM backends to cross-check QE, crosstalk, and field maps.
COMPASS bridges the gap between electromagnetic theory and practical CMOS image sensor design. Define your pixel stack once, run it through any solver, and compare results -- all from Python.
Run the same pixel structure through RCWA (torcwa, grcwa, meent, fmmax) and FDTD (flaport, Meep, fdtdz) solvers from a single YAML config. Compare results head-to-head.
Leverage PyTorch and JAX GPU backends for massively parallel wavelength sweeps. Achieve 10-100x speedup over CPU-only solvers on large parameter spaces.
Automatic differentiation through the solver enables gradient-based inverse design. Optimize microlens profiles, BARL stacks, and color filter thicknesses directly.
Built-in solver comparison framework ensures physics accuracy. Compare energy balance (R+T+A=1), QE spectra, and field distributions across solver backends.
Compute QE per pixel per wavelength, crosstalk matrices, energy balance, and field distributions. Plot results with built-in matplotlib and 3D PyVista viewers.
MIT licensed. Fully documented with theory guides, cookbooks, and API references. Built on established open-source EM solver ecosystems.
A clean five-stage pipeline takes you from YAML configuration to publication-ready results. Click any stage to learn more.
COMPASS provides a unified interface to 9 solver backends across three electromagnetic methods. Click any solver to see details.
In addition to the Python solver pipeline, COMPASS ships with 20+ browser-based simulators for quick exploration and intuition-building. They run entirely client-side -- no install, no Python required -- and are designed for teaching, design-space exploration, and sanity checks before committing to a full RCWA/FDTD run.
Define your simulation in a single YAML config and run it with three lines of Python:
# config.yaml
pixel:
pitch: 1.0 # um
unit_cell: [2, 2] # 2x2 Bayer pattern
solver:
name: torcwa
type: rcwa
fourier_order: 9
source:
wavelength:
mode: sweep
sweep: { start: 0.4, stop: 0.7, step: 0.01 }
polarization: unpolarizedfrom compass.runners.single_run import SingleRunner
result = SingleRunner.run("config.yaml")
for pixel, qe in result.qe_per_pixel.items():
print(f"{pixel}: peak QE = {qe.max():.2%}")