Ejemplo n.º 1
0
    def solve(self, wls):
        """Isotropic solver.

        INPUT
        wls = wavelengths to scan (any asarray-able object).

        OUTPUT
        self.DE1, self.DE3 = power reflected and transmitted.

        NOTE
        see:
        Moharam, "Formulation for stable and efficient implementation
        of the rigorous coupled-wave analysis of binary gratings",
        JOSA A, 12(5), 1995
        Lalanne, "Highly improved convergence of the coupled-wave
        method for TM polarization", JOSA A, 13(4), 1996
        Moharam, "Stable implementation of the rigorous coupled-wave
        analysis for surface-relief gratings: enhanced trasmittance
        matrix approach", JOSA A, 12(5), 1995
        """

        self.wls = S.atleast_1d(wls)

        LAMBDA = self.LAMBDA
        n = self.n
        multilayer = self.multilayer
        alpha = self.alpha
        delta = self.delta
        psi = self.psi
        phi = self.phi

        nlayers = len(multilayer)
        i = S.arange(-n, n + 1)
        nood = 2 * n + 1
        hmax = nood - 1

        # grating vector (on the xz plane)
        # grating on the xy plane
        K = 2 * pi / LAMBDA * np.array(
            [S.sin(phi), 0., S.cos(phi)], dtype=complex)

        DE1 = S.zeros((nood, self.wls.size))
        DE3 = S.zeros_like(DE1)

        dirk1 = np.array([
            S.sin(alpha) * S.cos(delta),
            S.sin(alpha) * S.sin(delta),
            S.cos(alpha)
        ])

        # usefull matrices
        I = S.eye(i.size)
        I2 = S.eye(i.size * 2)
        ZERO = S.zeros_like(I)

        X = S.zeros((2 * nood, 2 * nood, nlayers), dtype=complex)
        MTp1 = S.zeros((2 * nood, 2 * nood, nlayers), dtype=complex)
        MTp2 = S.zeros_like(MTp1)

        EPS2 = S.zeros(2 * hmax + 1, dtype=complex)
        EPS21 = S.zeros_like(EPS2)

        dlt = (i == 0).astype(int)

        for iwl, wl in enumerate(self.wls):

            # free space wavevector
            k = 2 * pi / wl

            n1 = multilayer[0].mat.n(wl).item()
            n3 = multilayer[-1].mat.n(wl).item()

            # incident plane wave wavevector
            k1 = k * n1 * dirk1

            # all the other wavevectors
            tmp_x = k1[0] - i * K[0]
            tmp_y = k1[1] * S.ones_like(i)
            tmp_z = dispersion_relation_ordinary(tmp_x, tmp_y, k, n1)
            k1i = S.r_[[tmp_x], [tmp_y], [tmp_z]]

            # k2i = S.r_[[k1[0] - i*K[0]], [k1[1] - i * K[1]], [-i * K[2]]]

            tmp_z = dispersion_relation_ordinary(tmp_x, tmp_y, k, n3)
            k3i = S.r_[[k1i[0, :]], [k1i[1, :]], [tmp_z]]

            # aliases for constant wavevectors
            kx = k1i[0, :]
            ky = k1[1]

            # angles of reflection
            # phi_i = S.arctan2(ky,kx)
            phi_i = S.arctan2(ky, kx.real)  # OKKIO

            Kx = S.diag(kx / k)
            Ky = ky / k * I
            Z1 = S.diag(k1i[2, :] / (k * n1**2))
            Y1 = S.diag(k1i[2, :] / k)
            Z3 = S.diag(k3i[2, :] / (k * n3**2))
            Y3 = S.diag(k3i[2, :] / k)
            # Fc = S.diag(S.cos(phi_i))
            fc = S.cos(phi_i)
            # Fs = S.diag(S.sin(phi_i))
            fs = S.sin(phi_i)

            MR = S.asarray(
                S.bmat([[I, ZERO], [-1j * Y1, ZERO], [ZERO, I],
                        [ZERO, -1j * Z1]]))

            MT = S.asarray(
                S.bmat([[I, ZERO], [1j * Y3, ZERO], [ZERO, I], [ZERO,
                                                                1j * Z3]]))

            # internal layers (grating or layer)
            X.fill(0.0)
            MTp1.fill(0.0)
            MTp2.fill(0.0)
            for nlayer in range(nlayers - 2, 0, -1):  # internal layers

                layer = multilayer[nlayer]
                d = layer.thickness

                EPS2, EPS21 = layer.getEPSFourierCoeffs(wl,
                                                        n,
                                                        anisotropic=False)

                E = toeplitz(EPS2[hmax::-1], EPS2[hmax:])
                E1 = toeplitz(EPS21[hmax::-1], EPS21[hmax:])
                E11 = inv(E1)
                # B = S.dot(Kx, linsolve(E,Kx)) - I
                B = kx[:, S.newaxis] / k * linsolve(E, Kx) - I
                # A = S.dot(Kx, Kx) - E
                A = S.diag((kx / k)**2) - E

                # Note: solution bug alfredo
                # randomizzo Kx un po' a caso finche' cond(A) e' piccolo (<1e10)
                # soluzione sporca... :-(
                # per certi kx, l'operatore di helmholtz ha 2 autovalori nulli e A, B
                # non sono invertibili --> cambio leggermente i kx... ma dovrei invece
                # trattare separatamente (analiticamente) questi casi
                if cond(A) > 1e10:
                    warning("BAD CONDITIONING: randomization of kx")
                    while cond(A) > 1e10:
                        Kx = Kx * (1 + 1e-9 * S.rand())
                        B = kx[:, S.newaxis] / k * linsolve(E, Kx) - I
                        A = S.diag((kx / k)**2) - E

                if S.absolute(K[2] / k) > 1e-10:

                    raise ValueError(
                        "First Order Helmholtz Operator not implemented, yet!")

                elif ky == 0 or S.allclose(S.diag(Ky / ky * k), 1):

                    # lalanne
                    # H_U_reduced = S.dot(Ky, Ky) + A
                    H_U_reduced = (ky / k)**2 * I + A
                    # H_S_reduced = S.dot(Ky, Ky) + S.dot(Kx, linsolve(E, S.dot(Kx, E11))) - E11
                    H_S_reduced = ((ky / k)**2 * I + kx[:, S.newaxis] / k *
                                   linsolve(E, kx[:, S.newaxis] / k * E11) -
                                   E11)

                    q1, W1 = eig(H_U_reduced)
                    q1 = S.sqrt(q1)
                    q2, W2 = eig(H_S_reduced)
                    q2 = S.sqrt(q2)

                    # boundary conditions

                    # V11 = S.dot(linsolve(A, W1), S.diag(q1))
                    V11 = linsolve(A, W1) * q1[S.newaxis, :]
                    V12 = (ky / k) * S.dot(linsolve(A, Kx), W2)
                    V21 = (ky / k) * S.dot(linsolve(B, Kx), linsolve(E, W1))
                    # V22 = S.dot(linsolve(B, W2), S.diag(q2))
                    V22 = linsolve(B, W2) * q2[S.newaxis, :]

                    # Vss = S.dot(Fc, V11)
                    Vss = fc[:, S.newaxis] * V11
                    # Wss = S.dot(Fc, W1)  + S.dot(Fs, V21)
                    Wss = fc[:, S.newaxis] * W1 + fs[:, S.newaxis] * V21
                    # Vsp = S.dot(Fc, V12) - S.dot(Fs, W2)
                    Vsp = fc[:, S.newaxis] * V12 - fs[:, S.newaxis] * W2
                    # Wsp = S.dot(Fs, V22)
                    Wsp = fs[:, S.newaxis] * V22
                    # Wpp = S.dot(Fc, V22)
                    Wpp = fc[:, S.newaxis] * V22
                    # Vpp = S.dot(Fc, W2)  + S.dot(Fs, V12)
                    Vpp = fc[:, S.newaxis] * W2 + fs[:, S.newaxis] * V12
                    # Wps = S.dot(Fc, V21) - S.dot(Fs, W1)
                    Wps = fc[:, S.newaxis] * V21 - fs[:, S.newaxis] * W1
                    # Vps = S.dot(Fs, V11)
                    Vps = fs[:, S.newaxis] * V11

                    Mc2bar = S.asarray(
                        S.bmat([
                            [Vss, Vsp, Vss, Vsp],
                            [Wss, Wsp, -Wss, -Wsp],
                            [Wps, Wpp, -Wps, -Wpp],
                            [Vps, Vpp, Vps, Vpp],
                        ]))

                    x = S.r_[S.exp(-k * q1 * d), S.exp(-k * q2 * d)]

                    # Mc1 = S.dot(Mc2bar, S.diag(S.r_[S.ones_like(x), x]))
                    xx = S.r_[S.ones_like(x), x]
                    Mc1 = Mc2bar * xx[S.newaxis, :]

                    X[:, :, nlayer] = S.diag(x)

                    MTp = linsolve(Mc2bar, MT)
                    MTp1[:, :, nlayer] = MTp[0:2 * nood, :]
                    MTp2 = MTp[2 * nood:, :]

                    MT = S.dot(
                        Mc1,
                        S.r_[I2,
                             S.
                             dot(MTp2,
                                 linsolve(MTp1[:, :, nlayer], X[:, :,
                                                                nlayer])), ],
                    )

                else:

                    ValueError(
                        "Second Order Helmholtz Operator not implemented, yet!"
                    )

            # M = S.asarray(S.bmat([-MR, MT]))
            M = S.c_[-MR, MT]
            b = S.r_[S.sin(psi) * dlt,
                     1j * S.sin(psi) * n1 * S.cos(alpha) * dlt,
                     -1j * S.cos(psi) * n1 * dlt,
                     S.cos(psi) * S.cos(alpha) * dlt, ]

            x = linsolve(M, b)
            R, T = S.split(x, 2)
            Rs, Rp = S.split(R, 2)
            for ii in range(1, nlayers - 1):
                T = S.dot(linsolve(MTp1[:, :, ii], X[:, :, ii]), T)
            Ts, Tp = S.split(T, 2)

            DE1[:, iwl] = (k1i[2, :] / (k1[2])).real * S.absolute(Rs)**2 + (
                k1i[2, :] / (k1[2] * n1**2)).real * S.absolute(Rp)**2
            DE3[:, iwl] = (k3i[2, :] / (k1[2])).real * S.absolute(Ts)**2 + (
                k3i[2, :] / (k1[2] * n3**2)).real * S.absolute(Tp)**2

        # save the results
        self.DE1 = DE1
        self.DE3 = DE3

        return self
Ejemplo n.º 2
0
    def solve(self, wls):
        """Isotropic solver.

        INPUT
        wls = wavelengths to scan (any asarray-able object).

        OUTPUT
        self.DE1, self.DE3 = power reflected and transmitted.

        NOTE
        see:
        Moharam, "Formulation for stable and efficient implementation
        of the rigorous coupled-wave analysis of binary gratings",
        JOSA A, 12(5), 1995
        Lalanne, "Highly improved convergence of the coupled-wave
        method for TM polarization", JOSA A, 13(4), 1996
        Moharam, "Stable implementation of the rigorous coupled-wave
        analysis for surface-relief gratings: enhanced trasmittance
        matrix approach", JOSA A, 12(5), 1995
        """

        self.wls = S.atleast_1d(wls)

        LAMBDA = self.LAMBDA
        n = self.n
        multilayer = self.multilayer
        alpha = self.alpha
        delta = self.delta
        psi = self.psi
        phi = self.phi

        nlayers = len(multilayer)
        i = S.arange(-n, n + 1)
        nood = 2 * n + 1
        hmax = nood - 1

        # grating vector (on the xz plane)
        # grating on the xy plane
        K = 2 * pi / LAMBDA * \
            S.array([S.sin(phi), 0., S.cos(phi)], dtype=complex)

        DE1 = S.zeros((nood, self.wls.size))
        DE3 = S.zeros_like(DE1)

        dirk1 = S.array([S.sin(alpha) * S.cos(delta),
                         S.sin(alpha) * S.sin(delta),
                         S.cos(alpha)])

        # usefull matrices
        I = S.eye(i.size)
        I2 = S.eye(i.size * 2)
        ZERO = S.zeros_like(I)

        X = S.zeros((2 * nood, 2 * nood, nlayers), dtype=complex)
        MTp1 = S.zeros((2 * nood, 2 * nood, nlayers), dtype=complex)
        MTp2 = S.zeros_like(MTp1)

        EPS2 = S.zeros(2 * hmax + 1, dtype=complex)
        EPS21 = S.zeros_like(EPS2)

        dlt = (i == 0).astype(int)

        for iwl, wl in enumerate(self.wls):

            # free space wavevector
            k = 2 * pi / wl

            n1 = multilayer[0].mat.n(wl).item()
            n3 = multilayer[-1].mat.n(wl).item()

            # incident plane wave wavevector
            k1 = k * n1 * dirk1

            # all the other wavevectors
            tmp_x = k1[0] - i * K[0]
            tmp_y = k1[1] * S.ones_like(i)
            tmp_z = dispersion_relation_ordinary(tmp_x, tmp_y, k, n1)
            k1i = S.r_[[tmp_x], [tmp_y], [tmp_z]]

            # k2i = S.r_[[k1[0] - i*K[0]], [k1[1] - i * K[1]], [-i * K[2]]]

            tmp_z = dispersion_relation_ordinary(tmp_x, tmp_y, k, n3)
            k3i = S.r_[[k1i[0, :]], [k1i[1, :]], [tmp_z]]

            # aliases for constant wavevectors
            kx = k1i[0, :]
            ky = k1[1]

            # angles of reflection
            # phi_i = S.arctan2(ky,kx)
            phi_i = S.arctan2(ky, kx.real)  # OKKIO

            Kx = S.diag(kx / k)
            Ky = ky / k * I
            Z1 = S.diag(k1i[2, :] / (k * n1 ** 2))
            Y1 = S.diag(k1i[2, :] / k)
            Z3 = S.diag(k3i[2, :] / (k * n3 ** 2))
            Y3 = S.diag(k3i[2, :] / k)
            # Fc = S.diag(S.cos(phi_i))
            fc = S.cos(phi_i)
            # Fs = S.diag(S.sin(phi_i))
            fs = S.sin(phi_i)

            MR = S.asarray(S.bmat([[I, ZERO],
                                   [-1j * Y1, ZERO],
                                   [ZERO, I],
                                   [ZERO, -1j * Z1]]))

            MT = S.asarray(S.bmat([[I, ZERO],
                                   [1j * Y3, ZERO],
                                   [ZERO, I],
                                   [ZERO, 1j * Z3]]))

            # internal layers (grating or layer)
            X.fill(0.0)
            MTp1.fill(0.0)
            MTp2.fill(0.0)
            for nlayer in range(nlayers - 2, 0, -1):  # internal layers

                layer = multilayer[nlayer]
                d = layer.thickness

                EPS2, EPS21 = layer.getEPSFourierCoeffs(
                    wl, n, anisotropic=False)

                E = toeplitz(EPS2[hmax::-1], EPS2[hmax:])
                E1 = toeplitz(EPS21[hmax::-1], EPS21[hmax:])
                E11 = inv(E1)
                # B = S.dot(Kx, linsolve(E,Kx)) - I
                B = kx[:, S.newaxis] / k * linsolve(E, Kx) - I
                # A = S.dot(Kx, Kx) - E
                A = S.diag((kx / k) ** 2) - E

                # Note: solution bug alfredo
                # randomizzo Kx un po' a caso finche' cond(A) e' piccolo (<1e10)
                # soluzione sporca... :-(
                # per certi kx, l'operatore di helmholtz ha 2 autovalori nulli e A, B
                # non sono invertibili --> cambio leggermente i kx... ma dovrei invece
                # trattare separatamente (analiticamente) questi casi
                if cond(A) > 1e10:
                    warning('BAD CONDITIONING: randomization of kx')
                    while cond(A) > 1e10:
                        Kx = Kx * (1 + 1e-9 * S.rand())
                        B = kx[:, S.newaxis] / k * linsolve(E, Kx) - I
                        A = S.diag((kx / k) ** 2) - E

                if S.absolute(K[2] / k) > 1e-10:

                    raise ValueError(
                        'First Order Helmholtz Operator not implemented, yet!')

                elif ky == 0 or S.allclose(S.diag(Ky / ky * k), 1):

                    # lalanne
                    # H_U_reduced = S.dot(Ky, Ky) + A
                    H_U_reduced = (ky / k) ** 2 * I + A
                    # H_S_reduced = S.dot(Ky, Ky) + S.dot(Kx, linsolve(E, S.dot(Kx, E11))) - E11
                    H_S_reduced = (ky / k) ** 2 * I + kx[:, S.newaxis] / k * linsolve(E,
                                                                                      kx[:, S.newaxis] / k * E11) - E11

                    q1, W1 = eig(H_U_reduced)
                    q1 = S.sqrt(q1)
                    q2, W2 = eig(H_S_reduced)
                    q2 = S.sqrt(q2)

                    # boundary conditions

                    # V11 = S.dot(linsolve(A, W1), S.diag(q1))
                    V11 = linsolve(A, W1) * q1[S.newaxis, :]
                    V12 = (ky / k) * S.dot(linsolve(A, Kx), W2)
                    V21 = (ky / k) * S.dot(linsolve(B, Kx), linsolve(E, W1))
                    # V22 = S.dot(linsolve(B, W2), S.diag(q2))
                    V22 = linsolve(B, W2) * q2[S.newaxis, :]

                    # Vss = S.dot(Fc, V11)
                    Vss = fc[:, S.newaxis] * V11
                    # Wss = S.dot(Fc, W1)  + S.dot(Fs, V21)
                    Wss = fc[:, S.newaxis] * W1 + fs[:, S.newaxis] * V21
                    # Vsp = S.dot(Fc, V12) - S.dot(Fs, W2)
                    Vsp = fc[:, S.newaxis] * V12 - fs[:, S.newaxis] * W2
                    # Wsp = S.dot(Fs, V22)
                    Wsp = fs[:, S.newaxis] * V22
                    # Wpp = S.dot(Fc, V22)
                    Wpp = fc[:, S.newaxis] * V22
                    # Vpp = S.dot(Fc, W2)  + S.dot(Fs, V12)
                    Vpp = fc[:, S.newaxis] * W2 + fs[:, S.newaxis] * V12
                    # Wps = S.dot(Fc, V21) - S.dot(Fs, W1)
                    Wps = fc[:, S.newaxis] * V21 - fs[:, S.newaxis] * W1
                    # Vps = S.dot(Fs, V11)
                    Vps = fs[:, S.newaxis] * V11

                    Mc2bar = S.asarray(S.bmat([[Vss, Vsp, Vss, Vsp],
                                               [Wss, Wsp, -Wss, -Wsp],
                                               [Wps, Wpp, -Wps, -Wpp],
                                               [Vps, Vpp, Vps, Vpp]]))

                    x = S.r_[S.exp(-k * q1 * d), S.exp(-k * q2 * d)]

                    # Mc1 = S.dot(Mc2bar, S.diag(S.r_[S.ones_like(x), x]))
                    xx = S.r_[S.ones_like(x), x]
                    Mc1 = Mc2bar * xx[S.newaxis, :]

                    X[:, :, nlayer] = S.diag(x)

                    MTp = linsolve(Mc2bar, MT)
                    MTp1[:, :, nlayer] = MTp[0:2 * nood, :]
                    MTp2 = MTp[2 * nood:, :]

                    MT = S.dot(
                        Mc1, S.r_[
                            I2, S.dot(
                                MTp2, linsolve(
                                    MTp1[
                                        :, :, nlayer], X[
                                        :, :, nlayer]))])

                else:

                    ValueError(
                        'Second Order Helmholtz Operator not implemented, yet!')

            # M = S.asarray(S.bmat([-MR, MT]))
            M = S.c_[-MR, MT]
            b = S.r_[S.sin(psi) * dlt,
                     1j * S.sin(psi) * n1 * S.cos(alpha) * dlt,
                     -1j * S.cos(psi) * n1 * dlt,
                     S.cos(psi) * S.cos(alpha) * dlt]

            x = linsolve(M, b)
            R, T = S.split(x, 2)
            Rs, Rp = S.split(R, 2)
            for ii in range(1, nlayers - 1):
                T = S.dot(linsolve(MTp1[:, :, ii], X[:, :, ii]), T)
            Ts, Tp = S.split(T, 2)

            DE1[:, iwl] = (k1i[2, :] / (k1[2])).real * S.absolute(Rs) ** 2 + \
                          (k1i[2, :] / (k1[2] * n1 ** 2)).real * \
                S.absolute(Rp) ** 2
            DE3[:, iwl] = (k3i[2, :] / (k1[2])).real * S.absolute(Ts) ** 2 + \
                          (k3i[2, :] / (k1[2] * n3 ** 2)).real * \
                S.absolute(Tp) ** 2

        # save the results
        self.DE1 = DE1
        self.DE3 = DE3

        return self