175 lines
6.1 KiB
Python
175 lines
6.1 KiB
Python
"""
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Example code for running an OpenCL FDTD simulation
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See main() for simulation setup.
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"""
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import sys
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import time
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import numpy
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import h5py
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from meanas import fdtd
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from meanas.fdtd import cpml_params, updates_with_cpml
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from masque import Pattern, shapes
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import gridlock
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import pcgen
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def perturbed_l3(a: float, radius: float, **kwargs) -> Pattern:
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"""
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Generate a masque.Pattern object containing a perturbed L3 cavity.
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Args:
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a: Lattice constant.
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radius: Hole radius, in units of a (lattice constant).
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**kwargs: Keyword arguments:
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hole_dose, trench_dose, hole_layer, trench_layer: Shape properties for Pattern.
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Defaults *_dose=1, hole_layer=0, trench_layer=1.
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shifts_a, shifts_r: passed to pcgen.l3_shift; specifies lattice constant (1 -
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multiplicative factor) and radius (multiplicative factor) for shifting
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holes adjacent to the defect (same row). Defaults are 0.15 shift for
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first hole, 0.075 shift for third hole, and no radius change.
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xy_size: [x, y] number of mirror periods in each direction; total size is
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`2 * n + 1` holes in each direction. Default `[10, 10]`.
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perturbed_radius: radius of holes perturbed to form an upwards-driected beam
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(multiplicative factor). Default 1.1.
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trench width: Width of the undercut trenches. Default 1.2e3.
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Return:
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`masque.Pattern` object containing the L3 design
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"""
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default_args = {'hole_dose': 1,
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'trench_dose': 1,
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'hole_layer': 0,
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'trench_layer': 1,
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'shifts_a': (0.15, 0, 0.075),
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'shifts_r': (1.0, 1.0, 1.0),
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'xy_size': (10, 10),
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'perturbed_radius': 1.1,
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'trench_width': 1.2e3,
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}
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kwargs = {**default_args, **kwargs}
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xyr = pcgen.l3_shift_perturbed_defect(mirror_dims=kwargs['xy_size'],
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perturbed_radius=kwargs['perturbed_radius'],
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shifts_a=kwargs['shifts_a'],
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shifts_r=kwargs['shifts_r'])
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xyr *= a
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xyr[:, 2] *= radius
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pat = Pattern()
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pat.name = f'L3p-a{a:g}r{radius:g}rp{kwargs["perturbed_radius"]:g}'
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pat.shapes += [shapes.Circle(radius=r, offset=(x, y),
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dose=kwargs['hole_dose'],
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layer=kwargs['hole_layer'])
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for x, y, r in xyr]
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maxes = numpy.max(numpy.fabs(xyr), axis=0)
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pat.shapes += [shapes.Polygon.rectangle(
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lx=(2 * maxes[0]), ly=kwargs['trench_width'],
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offset=(0, s * (maxes[1] + a + kwargs['trench_width'] / 2)),
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dose=kwargs['trench_dose'], layer=kwargs['trench_layer'])
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for s in (-1, 1)]
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return pat
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def main():
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dtype = numpy.float32
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max_t = 8000 # number of timesteps
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dx = 40 # discretization (nm/cell)
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pml_thickness = 8 # (number of cells)
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wl = 1550 # Excitation wavelength and fwhm
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dwl = 200
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# Device design parameters
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xy_size = numpy.array([10, 10])
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a = 430
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r = 0.285
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th = 170
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# refractive indices
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n_slab = 3.408 # InGaAsP(80, 50) @ 1550nm
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n_air = 1.0 # air
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# Half-dimensions of the simulation grid
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xy_max = (xy_size + 1) * a * [1, numpy.sqrt(3)/2]
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z_max = 1.6 * a
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xyz_max = numpy.hstack((xy_max, z_max)) + pml_thickness * dx
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# Coordinates of the edges of the cells. The fdtd package can only do square grids at the moment.
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half_edge_coords = [numpy.arange(dx/2, m + dx, step=dx) for m in xyz_max]
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edge_coords = [numpy.hstack((-h[::-1], h)) for h in half_edge_coords]
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# #### Create the grid, mask, and draw the device ####
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grid = gridlock.Grid(edge_coords)
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epsilon = grid.allocate(n_air**2, dtype=dtype)
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grid.draw_slab(epsilon,
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surface_normal=2,
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center=[0, 0, 0],
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thickness=th,
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eps=n_slab**2)
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mask = perturbed_l3(a, r)
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grid.draw_polygons(epsilon,
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surface_normal=2,
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center=[0, 0, 0],
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thickness=2 * th,
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eps=n_air**2,
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polygons=mask.as_polygons())
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print(grid.shape)
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dt = .99/numpy.sqrt(3)
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e = [numpy.zeros_like(epsilon[0], dtype=dtype) for _ in range(3)]
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h = [numpy.zeros_like(epsilon[0], dtype=dtype) for _ in range(3)]
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dxes = [grid.dxyz, grid.autoshifted_dxyz()]
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# PMLs in every direction
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pml_params = [[cpml_params(axis=dd, polarity=pp, dt=dt,
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thickness=pml_thickness, epsilon_eff=1.0**2)
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for pp in (-1, +1)]
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for dd in range(3)]
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update_E, update_H = updates_with_cpml(cpml_params=pml_params, dt=dt,
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dxes=dxes, epsilon=epsilon)
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# Source parameters and function
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w = 2 * numpy.pi * dx / wl
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fwhm = dwl * w * w / (2 * numpy.pi * dx)
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alpha = (fwhm ** 2) / 8 * numpy.log(2)
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delay = 7/numpy.sqrt(2 * alpha)
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def field_source(i):
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t0 = i * dt - delay
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return numpy.sin(w * t0) * numpy.exp(-alpha * t0**2)
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# #### Run a bunch of iterations ####
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output_file = h5py.File('simulation_output.h5', 'w')
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start = time.perf_counter()
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for t in range(max_t):
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update_E(e, h, epsilon)
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e[1][tuple(grid.shape//2)] += field_source(t)
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update_H(e, h)
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print('iteration {}: average {} iterations per sec'.format(t, (t+1)/(time.perf_counter()-start)))
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sys.stdout.flush()
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if t % 20 == 0:
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r = sum([(f * f * e).sum() for f, e in zip(e, epsilon)])
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print('E sum', r)
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# Save field slices
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if (t % 20 == 0 and (max_t - t <= 1000 or t <= 2000)) or t == max_t-1:
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print('saving E-field')
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for j, f in enumerate(e):
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output_file['/E{}_t{}'.format('xyz'[j], t)] = f[:, :, round(f.shape[2]/2)]
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if __name__ == '__main__':
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main()
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