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