Reply To: lumopt errors

[ashish.singh]

# Define the geometry function to keep each ring separate
def concentric_rings_function(params):
    central_radius = params[0]  # in meters
    gap_width = params[1]       # in meters
    period = params[2]          # in meters

    verts = []
    for i in range(n_rings):
        # Calculate inner and outer radii for each ring
        r_inner = central_radius + i * period - gap_width / 2
        r_outer = central_radius + i * period + gap_width / 2
        num_sides = 64000 # Increased number of points for smoother rings
        angles = np.linspace(0, 2 * np.pi, num_sides, endpoint=False)
        
        # Define outer and inner vertices for each ring
        outer_vertices = np.array([[r_outer * np.cos(theta), r_outer * np.sin(theta)] for theta in angles])
        inner_vertices = np.array([[r_inner * np.cos(theta), r_inner * np.sin(theta)] for theta in angles])
        
        # Close the ring by appending the first point at the end of each ring
        ring_vertices = np.vstack((outer_vertices, inner_vertices[::-1], outer_vertices[0:1]))
        verts.append(ring_vertices)  # Keep each ring separate in the list

    return verts  # Returns a list of arrays, each array representing a ring

    return np.array(verts)  # Convert the list to a numpy array with consistent shape
# Create the FunctionDefinedPolygon geometry
geometry = FunctionDefinedPolygon(
    func=concentric_rings_function,
    initial_params=initial_params,
    bounds=bounds,
    z=gold_thickness+sio2_thickness+(gaas_thickness/2),
    depth=gaas_thickness,
    eps_out=1.0 ** 2,         # Dielectric constant outside the rings (e.g., air)
    eps_in=3.47668 ** 2,      # Dielectric constant inside the rings (e.g., GaAs)
    edge_precision=5,         # Precision of the polygon edges
    dx=1.0e-5,   
    unfold_symmetry=False,              # Spatial resolution for the polygon
)

# === Define ModeMatch FOM ===
# Initialize ModeMatch with the specified monitor name
mode_match_fom = ModeMatch(
    monitor_name='fom',                  # Exact monitor name as in Lumerical FDTD simulation
    mode_number=1,                       # Mode number to calculate overlap with
    direction='Forward',                 # Propagation direction ('Forward' or 'Backward')
    multi_freq_src=False,                # False for single frequency mode calculation
    target_T_fwd=lambda wl: np.ones(wl.size),  # Target transmission function
    norm_p=1,                            # p-norm for figure of merit calculation (1 for standard FOM)
    target_fom=0                         # Target value for FOM (optional)
)

# === Define Wavelength Settings ===
wavelengths = Wavelengths(start=1.2e-6, stop=1.6e-6, points=100)  # Define the wavelength range and points

# === Define Optimization Algorithm ===
optimizer = ScipyOptimizers(
    max_iter=50,
    method='L-BFGS-B',
    scaling_factor=1e9,                # Scales parameters from meters to nanometers
    pgtol=1e-6,
    ftol=1e-6,
    scale_initial_gradient_to=0.0,
    penalty_fun=None,
    penalty_jac=None
)

# === Setup and Run Optimization ===
# Define paths and parameters for optimization
base_script_path = 'cbg2.lsf'          # Path to the Lumerical script file
geometry = None                        # Define your specific geometry here

opt = Optimization(
    base_script=base_script_path,      # Path to Lumerical script for simulation setup
    wavelengths=wavelengths,           # Wavelength settings for simulation
    fom=mode_match_fom,                # Figure of Merit (FOM) configuration
    geometry=geometry,                 # Geometry configuration, like concentric rings
    optimizer=optimizer,               # Optimizer configuration
    use_var_fdtd=False,                # Use standard FDTD rather than VarFDTD
    hide_fdtd_cad=False,               # Whether to hide the FDTD CAD window
    use_deps=True,                     # Use FDTD’s numerical derivatives
    plot_history=True,                 # Plot optimization history
    store_all_simulations=True,        # Store all simulation results from each iteration
    save_global_index=False,           # Save global index monitor results (optional)
    label='Bullseye Structure Optimization',  # Label for the optimization project
    source_name='source'               # Name of the source in the Lumerical simulation
)

# Run the optimization, saving results to the specified working directory
working_directory = os.path.join(os.path.dirname(__file__), 'optimization_results')
opt.run(working_directory=working_directory)

this is the code i am using i want to optimize a structure using RCWA which as multiple parameters which i need to optimize. Any insight or help will be greatly appreciated