How do I include fabrication constraints in adjoint shape optimization?

Date	Category
2023-12-21 22:49:03	Inverse Design

To ensure reliable fabrication of a device, it is crucial to avoid using feature sizes below a certain radius of curvature when performing inverse design. To achieve this, you can use a penalty function that estimates the radius of curvature around each boundary vertex and applies a substantial penalty to the objective function if the value falls below the minimum radius. The code example below demonstrates how to use the tidy3d.plugins.autograd.invdes.make_curvature_penalty function.

from tidy3d.plugins.autograd import make_curvature_penalty

curvature_penalty = make_curvature_penalty(min_radius=0.15)

def penalty(params: np.ndarray) -> float:
    """Compute penalty for a set of parameters."""
    ys = get_ys(params)
    points = anp.array([xs, ys]).T
    return curvature_penalty(points)