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Jan Petykiewicz 2020-01-07 21:31:16 -08:00
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13
examples/fdtd.py
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@ -20,9 +20,10 @@ 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:
Args:
a: Lattice constant.
radius: Hole radius, in units of a (lattice constant).
**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 -
@ -30,11 +31,13 @@ def perturbed_l3(a: float, radius: float, **kwargs) -> Pattern:
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].
`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
Return:
`masque.Pattern` object containing the L3 design
"""
default_args = {'hole_dose': 1,

182
meanas/fdfd/bloch.py
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@ -4,18 +4,23 @@ Bloch eigenmode solver/operators
This module contains functions for generating and solving the
3D Bloch eigenproblem. The approach is to transform the problem
into the (spatial) fourier domain, transforming the equation
1/mu * curl(1/eps * curl(H)) = (w/c)^2 H
into
conv(1/mu_k, ik x conv(1/eps_k, ik x H_k)) = (w/c)^2 H_k
where:
- the _k subscript denotes a 3D fourier transformed field
- each component of H_k corresponds to a plane wave with wavevector k
- x is the cross product
- conv denotes convolution
Since k and H are orthogonal for each plane wave, we can use each
k to create an orthogonal basis (k, m, n), with k x m = n, and
|m| = |n| = 1. The cross products are then simplified with
1/mu * curl(1/eps * curl(H)) = (w/c)^2 H
into
conv(1/mu_k, ik x conv(1/eps_k, ik x H_k)) = (w/c)^2 H_k
where:
- the `_k` subscript denotes a 3D fourier transformed field
- each component of `H_k` corresponds to a plane wave with wavevector `k`
- `x` is the cross product
- `conv()` denotes convolution
Since `k` and `H` are orthogonal for each plane wave, we can use each
`k` to create an orthogonal basis (k, m, n), with `k x m = n`, and
`|m| = |n| = 1`. The cross products are then simplified with
k @ h = kx hx + ky hy + kz hz = 0 = hk
h = hk + hm + hn = hm + hn
@ -36,15 +41,17 @@ This module contains functions for generating and solving the
= (-kk hn)(mx, my, mz) + (kk hm)(nx, ny, nz)
= |k| (hm * (nx, ny, nz) - hn * (mx, my, mz))
where h is shorthand for H_k, (...)_kmn deontes the (k, m, n) basis,
and e.g. hm is the component of h in the m direction.
where `h` is shorthand for `H_k`, `(...)_kmn` deontes the `(k, m, n)` basis,
and e.g. `hm` is the component of `h` in the `m` direction.
We can also simplify conv(X_k, Y_k) as fftn(X * ifftn(Y_k)).
We can also simplify `conv(X_k, Y_k)` as `fftn(X * ifftn(Y_k))`.
Using these results and storing `H_k` as `h = (hm, hn)`, we have
Using these results and storing H_k as h = (hm, hn), we have
e_xyz = fftn(1/eps * ifftn(|k| (hm * n - hn * m)))
b_mn = |k| (-e_xyz @ n, e_xyz @ m)
h_mn = fftn(1/mu * ifftn(b_m * m + b_n * n))
which forms the operator from the left side of the equation.
We can then use a preconditioned block Rayleigh iteration algorithm, as in
@ -120,12 +127,15 @@ def generate_kmn(k0: numpy.ndarray,
"""
Generate a (k, m, n) orthogonal basis for each k-vector in the simulation grid.
:param k0: [k0x, k0y, k0z], Bloch wavevector, in G basis.
:param G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
:param shape: [nx, ny, nz] shape of the simulation grid.
:return: (|k|, m, n) where |k| has shape tuple(shape) + (1,)
and m, n have shape tuple(shape) + (3,).
All are given in the xyz basis (e.g. |k|[0,0,0] = norm(G_matrix @ k0)).
Args:
k0: [k0x, k0y, k0z], Bloch wavevector, in G basis.
G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
shape: [nx, ny, nz] shape of the simulation grid.
Returns:
`(|k|, m, n)` where `|k|` has shape `tuple(shape) + (1,)`
and `m`, `n` have shape `tuple(shape) + (3,)`.
All are given in the xyz basis (e.g. `|k|[0,0,0] = norm(G_matrix @ k0)`).
"""
k0 = numpy.array(k0)
@ -159,21 +169,27 @@ def maxwell_operator(k0: numpy.ndarray,
) -> Callable[[numpy.ndarray], numpy.ndarray]:
"""
Generate the Maxwell operator
conv(1/mu_k, ik x conv(1/eps_k, ik x ___))
which is the spatial-frequency-space representation of
1/mu * curl(1/eps * curl(___))
The operator is a function that acts on a vector h_mn of size (2 * epsilon[0].size)
The operator is a function that acts on a vector h_mn of size `2 * epsilon[0].size`
See the module-level docstring for more information.
See the `meanas.fdfd.bloch` docstring for more information.
:param k0: Bloch wavevector, [k0x, k0y, k0z].
:param G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
:param epsilon: Dielectric constant distribution for the simulation.
Args:
k0: Bloch wavevector, `[k0x, k0y, k0z]`.
G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
epsilon: Dielectric constant distribution for the simulation.
All fields are sampled at cell centers (i.e., NOT Yee-gridded)
:param mu: Magnetic permability distribution for the simulation.
mu: Magnetic permability distribution for the simulation.
Default None (1 everywhere).
:return: Function which applies the maxwell operator to h_mn.
Returns:
Function which applies the maxwell operator to h_mn.
"""
shape = epsilon[0].shape + (1,)
@ -189,8 +205,11 @@ def maxwell_operator(k0: numpy.ndarray,
h is complex 2-field in (m, n) basis, vectorized
:param h: Raveled h_mn; size (2 * epsilon[0].size).
:return: Raveled conv(1/mu_k, ik x conv(1/eps_k, ik x h_mn)).
Args:
h: Raveled h_mn; size `2 * epsilon[0].size`.
Returns:
Raveled conv(1/mu_k, ik x conv(1/eps_k, ik x h_mn)).
"""
hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
@ -231,18 +250,22 @@ def hmn_2_exyz(k0: numpy.ndarray,
) -> Callable[[numpy.ndarray], fdfield_t]:
"""
Generate an operator which converts a vectorized spatial-frequency-space
h_mn into an E-field distribution, i.e.
`h_mn` into an E-field distribution, i.e.
ifft(conv(1/eps_k, ik x h_mn))
The operator is a function that acts on a vector h_mn of size (2 * epsilon[0].size)
The operator is a function that acts on a vector `h_mn` of size `2 * epsilon[0].size`.
See the module-level docstring for more information.
See the `meanas.fdfd.bloch` docstring for more information.
:param k0: Bloch wavevector, [k0x, k0y, k0z].
:param G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
:param epsilon: Dielectric constant distribution for the simulation.
Args:
k0: Bloch wavevector, `[k0x, k0y, k0z]`.
G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
epsilon: Dielectric constant distribution for the simulation.
All fields are sampled at cell centers (i.e., NOT Yee-gridded)
:return: Function for converting h_mn into E_xyz
Returns:
Function for converting `h_mn` into `E_xyz`
"""
shape = epsilon[0].shape + (1,)
epsilon = numpy.stack(epsilon, 3)
@ -266,18 +289,22 @@ def hmn_2_hxyz(k0: numpy.ndarray,
) -> Callable[[numpy.ndarray], fdfield_t]:
"""
Generate an operator which converts a vectorized spatial-frequency-space
h_mn into an H-field distribution, i.e.
`h_mn` into an H-field distribution, i.e.
ifft(h_mn)
The operator is a function that acts on a vector h_mn of size (2 * epsilon[0].size)
The operator is a function that acts on a vector `h_mn` of size `2 * epsilon[0].size`.
See the module-level docstring for more information.
See the `meanas.fdfd.bloch` docstring for more information.
:param k0: Bloch wavevector, [k0x, k0y, k0z].
:param G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
:param epsilon: Dielectric constant distribution for the simulation.
Only epsilon[0].shape is used.
:return: Function for converting h_mn into H_xyz
Args:
k0: Bloch wavevector, `[k0x, k0y, k0z]`.
G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
epsilon: Dielectric constant distribution for the simulation.
Only `epsilon[0].shape` is used.
Returns:
Function for converting `h_mn` into `H_xyz`
"""
shape = epsilon[0].shape + (1,)
_k_mag, m, n = generate_kmn(k0, G_matrix, shape)
@ -298,18 +325,23 @@ def inverse_maxwell_operator_approx(k0: numpy.ndarray,
) -> Callable[[numpy.ndarray], numpy.ndarray]:
"""
Generate an approximate inverse of the Maxwell operator,
ik x conv(eps_k, ik x conv(mu_k, ___))
which can be used to improve the speed of ARPACK in shift-invert mode.
See the module-level docstring for more information.
See the `meanas.fdfd.bloch` docstring for more information.
:param k0: Bloch wavevector, [k0x, k0y, k0z].
:param G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
:param epsilon: Dielectric constant distribution for the simulation.
Args:
k0: Bloch wavevector, `[k0x, k0y, k0z]`.
G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
epsilon: Dielectric constant distribution for the simulation.
All fields are sampled at cell centers (i.e., NOT Yee-gridded)
:param mu: Magnetic permability distribution for the simulation.
mu: Magnetic permability distribution for the simulation.
Default None (1 everywhere).
:return: Function which applies the approximate inverse of the maxwell operator to h_mn.
Returns:
Function which applies the approximate inverse of the maxwell operator to `h_mn`.
"""
shape = epsilon[0].shape + (1,)
epsilon = numpy.stack(epsilon, 3)
@ -325,8 +357,11 @@ def inverse_maxwell_operator_approx(k0: numpy.ndarray,
h is complex 2-field in (m, n) basis, vectorized
:param h: Raveled h_mn; size (2 * epsilon[0].size).
:return: Raveled ik x conv(eps_k, ik x conv(mu_k, h_mn))
Args:
h: Raveled h_mn; size `2 * epsilon[0].size`.
Returns:
Raveled ik x conv(eps_k, ik x conv(mu_k, h_mn))
"""
hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
@ -376,16 +411,20 @@ def find_k(frequency: float,
"""
Search for a bloch vector that has a given frequency.
:param frequency: Target frequency.
:param tolerance: Target frequency tolerance.
:param direction: k-vector direction to search along.
:param G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
:param epsilon: Dielectric constant distribution for the simulation.
Args:
frequency: Target frequency.
tolerance: Target frequency tolerance.
direction: k-vector direction to search along.
G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
epsilon: Dielectric constant distribution for the simulation.
All fields are sampled at cell centers (i.e., NOT Yee-gridded)
:param mu: Magnetic permability distribution for the simulation.
mu: Magnetic permability distribution for the simulation.
Default None (1 everywhere).
:param band: Which band to search in. Default 0 (lowest frequency).
return: (k, actual_frequency) The found k-vector and its frequency
band: Which band to search in. Default 0 (lowest frequency).
Returns:
`(k, actual_frequency)`
The found k-vector and its frequency.
"""
direction = numpy.array(direction) / norm(direction)
@ -419,16 +458,19 @@ def eigsolve(num_modes: int,
Find the first (lowest-frequency) num_modes eigenmodes with Bloch wavevector
k0 of the specified structure.
:param k0: Bloch wavevector, [k0x, k0y, k0z].
:param G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
:param epsilon: Dielectric constant distribution for the simulation.
Args:
k0: Bloch wavevector, `[k0x, k0y, k0z]`.
G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
epsilon: Dielectric constant distribution for the simulation.
All fields are sampled at cell centers (i.e., NOT Yee-gridded)
:param mu: Magnetic permability distribution for the simulation.
Default None (1 everywhere).
:param tolerance: Solver stops when fractional change in the objective
trace(Z.H @ A @ Z @ inv(Z Z.H)) is smaller than the tolerance
:return: (eigenvalues, eigenvectors) where eigenvalues[i] corresponds to the
vector eigenvectors[i, :]
mu: Magnetic permability distribution for the simulation.
Default `None` (1 everywhere).
tolerance: Solver stops when fractional change in the objective
`trace(Z.H @ A @ Z @ inv(Z Z.H))` is smaller than the tolerance
Returns:
`(eigenvalues, eigenvectors)` where `eigenvalues[i]` corresponds to the
vector `eigenvectors[i, :]`
"""
h_size = 2 * epsilon[0].size

50
meanas/fdfd/farfield.py
View File

@ -21,26 +21,30 @@ def near_to_farfield(E_near: fdfield_t,
The input fields should be complex phasors.
:param E_near: List of 2 ndarrays containing the 2D phasor field slices for the transverse
Args:
E_near: List of 2 ndarrays containing the 2D phasor field slices for the transverse
E fields (e.g. [Ex, Ey] for calculating the farfield toward the z-direction).
:param H_near: List of 2 ndarrays containing the 2D phasor field slices for the transverse
H_near: List of 2 ndarrays containing the 2D phasor field slices for the transverse
H fields (e.g. [Hx, hy] for calculating the farfield towrad the z-direction).
:param dx: Cell size along x-dimension, in units of wavelength.
:param dy: Cell size along y-dimension, in units of wavelength.
:param padded_size: Shape of the output. A single integer `n` will be expanded to `(n, n)`.
dx: Cell size along x-dimension, in units of wavelength.
dy: Cell size along y-dimension, in units of wavelength.
padded_size: Shape of the output. A single integer `n` will be expanded to `(n, n)`.
Powers of 2 are most efficient for FFT computation.
Default is the smallest power of 2 larger than the input, for each axis.
:returns: Dict with keys
'E_far': Normalized E-field farfield; multiply by
Returns:
Dict with keys
- `E_far`: Normalized E-field farfield; multiply by
(i k exp(-i k r) / (4 pi r)) to get the actual field value.
'H_far': Normalized H-field farfield; multiply by
- `H_far`: Normalized H-field farfield; multiply by
(i k exp(-i k r) / (4 pi r)) to get the actual field value.
'kx', 'ky': Wavevector values corresponding to the x- and y- axes in E_far and H_far,
- `kx`, `ky`: Wavevector values corresponding to the x- and y- axes in E_far and H_far,
normalized to wavelength (dimensionless).
'dkx', 'dky': step size for kx and ky, normalized to wavelength.
'theta': arctan2(ky, kx) corresponding to each (kx, ky).
- `dkx`, `dky`: step size for kx and ky, normalized to wavelength.
- `theta`: arctan2(ky, kx) corresponding to each (kx, ky).
This is the angle in the x-y plane, counterclockwise from above, starting from +x.
'phi': arccos(kz / k) corresponding to each (kx, ky).
- `phi`: arccos(kz / k) corresponding to each (kx, ky).
This is the angle away from +z.
"""
@ -129,23 +133,27 @@ def far_to_nearfield(E_far: fdfield_t,
The input fields should be complex phasors.
:param E_far: List of 2 ndarrays containing the 2D phasor field slices for the transverse
Args:
E_far: List of 2 ndarrays containing the 2D phasor field slices for the transverse
E fields (e.g. [Ex, Ey] for calculating the nearfield toward the z-direction).
Fields should be normalized so that
E_far = E_far_actual / (i k exp(-i k r) / (4 pi r))
:param H_far: List of 2 ndarrays containing the 2D phasor field slices for the transverse
H_far: List of 2 ndarrays containing the 2D phasor field slices for the transverse
H fields (e.g. [Hx, hy] for calculating the nearfield toward the z-direction).
Fields should be normalized so that
H_far = H_far_actual / (i k exp(-i k r) / (4 pi r))
:param dkx: kx discretization, in units of wavelength.
:param dky: ky discretization, in units of wavelength.
:param padded_size: Shape of the output. A single integer `n` will be expanded to `(n, n)`.
dkx: kx discretization, in units of wavelength.
dky: ky discretization, in units of wavelength.
padded_size: Shape of the output. A single integer `n` will be expanded to `(n, n)`.
Powers of 2 are most efficient for FFT computation.
Default is the smallest power of 2 larger than the input, for each axis.
:returns: Dict with keys
'E': E-field nearfield
'H': H-field nearfield
'dx', 'dy': spatial discretization, normalized to wavelength (dimensionless)
Returns:
Dict with keys
- `E`: E-field nearfield
- `H`: H-field nearfield
- `dx`, `dy`: spatial discretization, normalized to wavelength (dimensionless)
"""
if not len(E_far) == 2:

29
meanas/fdmath/vectorization.py
View File

@ -1,6 +1,6 @@
"""
Functions for moving between a vector field (list of 3 ndarrays, [f_x, f_y, f_z])
and a 1D array representation of that field [f_x0, f_x1, f_x2,... f_y0,... f_z0,...].
Functions for moving between a vector field (list of 3 ndarrays, `[f_x, f_y, f_z]`)
and a 1D array representation of that field `[f_x0, f_x1, f_x2,... f_y0,... f_z0,...]`.
Vectorized versions of the field use row-major (ie., C-style) ordering.
"""
@ -17,11 +17,14 @@ def vec(f: fdfield_t) -> vfdfield_t:
"""
Create a 1D ndarray from a 3D vector field which spans a 1-3D region.
Returns None if called with f=None.
Returns `None` if called with `f=None`.
:param f: A vector field, [f_x, f_y, f_z] where each f_ component is a 1 to
3D ndarray (f_* should all be the same size). Doesn't fail with f=None.
:return: A 1D ndarray containing the linearized field (or None)
Args:
f: A vector field, `[f_x, f_y, f_z]` where each `f_` component is a 1- to
3-D ndarray (`f_*` should all be the same size). Doesn't fail with `f=None`.
Returns:
1D ndarray containing the linearized field (or `None`)
"""
if numpy.any(numpy.equal(f, None)):
return None
@ -31,15 +34,17 @@ def vec(f: fdfield_t) -> vfdfield_t:
def unvec(v: vfdfield_t, shape: numpy.ndarray) -> fdfield_t:
"""
Perform the inverse of vec(): take a 1D ndarray and output a 3D field
of form [f_x, f_y, f_z] where each of f_* is a len(shape)-dimensional
of form `[f_x, f_y, f_z]` where each of `f_*` is a len(shape)-dimensional
ndarray.
Returns None if called with v=None.
Returns `None` if called with `v=None`.
:param v: 1D ndarray representing a 3D vector field of shape shape (or None)
:param shape: shape of the vector field
:return: [f_x, f_y, f_z] where each f_ is a len(shape) dimensional ndarray
(or None)
Args:
v: 1D ndarray representing a 3D vector field of shape shape (or None)
shape: shape of the vector field
Returns:
`[f_x, f_y, f_z]` where each `f_` is a `len(shape)` dimensional ndarray (or `None`)
"""
if numpy.any(numpy.equal(v, None)):
return None