1. Introduction
With an ever-improving ability to fabricate miniature functional components, the field
of nanophotonics is in a phase of explosive growth. Not only are novel phenomena being
routinely observed in wavelength- and sub-wavelength-scale components, but these
concepts are being rapidly translated into exotic devices for applications ranging from
solar energy and information technology, to biology [
1
M. L. Brongersma and P. G. Kik, eds., Surface Plasmon Nanophotonics, vol.
131 of Springer series in optical sciences
(Springer, 2007).
,
2
R. Zia, J. A. Schuller, and M. L. Brongersma, “Plasmonics: The next chip-scale
technology,” Materials Today
9, 20–27 (2006). [CrossRef]
,
3
R. A. Pala, J. S. White, E. S. Barnard, J. Liu, and M. L. Brongersma, “Design of plasmonic thin-film solar cells with
broadband absorption enhancements,” Adv. Mater.
21, 1–6 (2009). [CrossRef]
]. At the heart of this development is the ability of nanostructures to
confine, guide, and scatter light in ways that are not achievable with bulk materials.
It follows that understanding and improving the performance of these nanostructures
requires a detailed knowledge of the electromagnetic modes they support.
The allowed electromagnetic modes in microphotonic structures are determined by solving
Maxwell’s equations for the given geometry. The last step in this procedure is the
application of boundary conditions at the interfaces, yielding the dispersion equations
which must be solved to obtain the allowed modes. These dispersion equations are in
general transcendental and except in a few simple cases, their
solutions cannot be expressed in terms of elementary mathematical functions.
When the solutions of the dispersion equations are known to be real, they are usually
determined using a graphical search algorithm such as the bisection method [
4
W. H. Press, S. A. Teukolsky, W. J. Vetterling, and B. P. Flannery, Numerical recipes in C++, The art of scientific
computing (Cambridge University Press,
2002), 2nd ed.
]. However, in lossy material systems or low
index-contrast asymmetric waveguides, the dispersion equations have complex solutions
[
5
A. W. Snyder and J. Love, Optical waveguide theory (Science
Paperbacks, 1983).
]. This calls for a search in two dimensions
(i.e., in the complex plane) and severely limits the effectiveness of graphical
algorithms.
Material loss and waveguide leakage are even more commonly encountered in plasmonic
waveguides [
1
M. L. Brongersma and P. G. Kik, eds., Surface Plasmon Nanophotonics, vol.
131 of Springer series in optical sciences
(Springer, 2007).
]. Due to the growing technological
and scientific clout of plasmonic systems [
6
J. Takahara, S. Yamagishi, H. Taki, A. Morimoto, and T. Kobayashi, “Guiding of a one-dimensional guiding of a
one-dimensional optical beam with nanometer diameter,”
Opt. Lett.
22, 475–477
(1997). [CrossRef] [PubMed]
,
7
J.-C. Weeber, Y. Lacroute, and A. Dereux, “Optical near-field distributions of surface plasmon
waveguide modes,” Phys. Rev. B
68, 115401 (2003). [CrossRef]
,
8
R. Zia, A. Chandran, and M. L. Brongersma, “Dielectric waveguide model for guided surface
polaritons,” Opt. Lett.
30, 1473–1475
(2005). [CrossRef] [PubMed]
,
9
R. Zia, J. A. Schuller, and M. L. Brongersma, “Near-field characterization of guided polariton
propagation and cutoff in surface plasmon waveguides,”
Phys. Rev. B
74, 165415 (2006). [CrossRef]
,
10
R. Zia, M. D. Selker, and M. L. Brongersma, “Leaky and bound modes of surface plasmon
waveguides,” Phys. Rev. B
71, 165431 (2005). [CrossRef]
,
11
G. Veronis and S. Fan, “Bends and splitters in metal-dielectric-metal
subwavelength plasmonic waveguides,” Appl. Phys.
Lett.
87, 131102 (2005). [CrossRef]
,
12
G. Veronis and S. Fan, “Guided subwavelength plasmonic mode supported by a
slot in a thin metal film,” Opt. Lett.
30, 3359–3361
(2005). [CrossRef]
,
13
S. I. Bozhevolnyi, V. S. Volkov, E. Devaux, J.-Y. Laluet, and T. W. Ebbesen, “Channel plasmon subwavelength waveguide components
including interferometers and ring resonators,”
Nature
440, 508–511
(2006). [CrossRef] [PubMed]
]various techniques for solving plasmon
waveguide dispersion equations are in current use. Examples include the reflection-pole
method (RPM) [
14
E. Anemogiannis, E. N. Glytsis, and T. K. Gaylord, “Determination of guided and leaky modes in lossless
and lossy planar multilayer optical waveguides: reflection pole method and
wavevector density method,” J. Lightwave Technol.
17, 929–941
(1999). [CrossRef]
,
15
R. Zia, M. D. Selker, P. B. Catrysse, and M. L. Brongersma, “Geometries and materials for subwavelength surface
plasmon modes,” J. Opt. Soc. Am. A
21, 2442–2446
(2004). [CrossRef]
], Newton’s method [
4
W. H. Press, S. A. Teukolsky, W. J. Vetterling, and B. P. Flannery, Numerical recipes in C++, The art of scientific
computing (Cambridge University Press,
2002), 2nd ed.
], and the
argument-principle method [
16
S. E. Kocabaş, G. Veronis, D. A. B. Miller, and S. Fan, “Modal analysis and coupling in metal-insulator-metal
waveguides,” Phys. Rev. B
79, 035120 (2009). [CrossRef]
]. Practical
implementation of these methods requires careful programming customized for solving the
problem at hand. In the methods that rely on curve-fitting, increasing the accuracy
beyond a few decimals is often a challenging task, often requiring one to iterate the
procedure manually based on previous results. Additionally, the root-finding algorithms
of several commercial mathematical software suites (e.g., Mathematica™) can be very
sensitive to initial guesses provided by the user; estimation of this initial guess is
especially difficult when the solution is complex.
In this paper, we present an easy-to-implement iterative procedure for solving complex
transcendental dispersion equations that is relatively insensitive to initial guess. Our
method applies to rectangular multilayer dielectric and plasmonic waveguides which may
have either material or leakage loss. We first use a simple numerical example to
illustrate the use of this technique and its convergence behavior. We then successively
apply the procedure to find modes of dielectric slab waveguides, photonic wire
waveguides, and plasmonic waveguides.
Fig. 1. Graphs of the left- (red) and the right- (blue) hand sides of
Eq. (4). The red dot indicates the
approximate location of the solution near
x⋍0.5.
Papers in the past have discussed the use of iterative techniques for solving
transcendental equations in general [
17
J. P. McKelvey, “Simple iterative procedures for solving
transcendental equations with the electronic slide rule,”
Am. J. Phys.
43, 331–334
(1975). [CrossRef]
]. Our aim
in this paper is to demonstrate how this method can be applied to automate the design
and analysis of waveguide structures of significant contemporary interest.
2. Iterative technique: A simple example
Although the general philosophy of iterative methods is well known, we will use a simple
example to introduce our terminology, illustrate the procedure use, and build an
intuitive understanding of convergence issues. Suppose we need to solve the
equation:
where
F(
x) is a combination of elementary mathematical
functions. We reformulate this as an iterative problem by converting
Eq. (1) to:
where f is obtained by manipulating the parent function
F. Next, we start with an initial guess x
1 and obtain a sequence according to:
If the sequence defined by
Eq. (3)
converges, then the limiting value is the solution to
Eq. (2). We will illustrate the method by a simple example. Consider
the transcendental equation
Figure 1 shows the graphs of the left-hand side
(LHS) and right-hand side (RHS) of
Eq.
(4), which intersect around
x⋍0.5. To find the root more
accurately by the iterative method, we recast the equation in a form similar to
Eq. (2) by choosing
f
(
x)=1-sin
x, setting up an iteration scheme
following
Eq. (3):
Figure 2(b) plots the first fifty iterations of
Eq. (5) and indicates that the
sequence converges to
x=0.5109734293885691. We compare the 16-decimal
agreement between this value and the solution
x=0.5109734293885691
computed by Mathematica
™’s FindRoot function that implements an optimized
version of the Newton’s method [
4
W. H. Press, S. A. Teukolsky, W. J. Vetterling, and B. P. Flannery, Numerical recipes in C++, The art of scientific
computing (Cambridge University Press,
2002), 2nd ed.
] commonly used
for solving transcendental equations.
Figure
2(c) shows the LHS and RHS of the iteration scheme in
Eq. (5) and
Fig. 2(a) shows how the procedure converges to the intersection point of the
two curves, starting from the initial guess. Making an initial guess corresponds to
choosing a point on the curve
y=
x shown by the red
curve in
Fig. 2(a). This point is indicated in
Fig. 2(a) by the red dot. The vertical lines
in the spiral represent computation of
f (
x) for the
chosen
x and the horizontal lines represent re-substitution of
x by
f (
x) for the next
iteration.
Fig. 2. Solution of the example transcendental equation via the iterative method. The
graphs (a), (d), and (g) show the left- (magenta) and the right- (gray) hand sides
of
Eq. (5),
(6), and
(7). The red dot indicates the position of the initial guess.
Convergence/divergence behavior of the real [(b), (e), (h)] and imaginary [(c),
(f), (i)] parts of the iterates.
Before we proceed to apply the iterative technique to practical waveguide problems, it
is useful to gain an intuitive understanding of how the above scheme converges to the
solution. This is important since, for any given
F(
x),
there are usually multiple ways to choose
f (
x) which
differ in their convergence behavior. For example, an equally-legitimate way of setting
up an iteration scheme for
Eq. (4) would
be to choose
f
(
x)=sin
-1(1-
x) and obtain the sequence
{
xn
} according to:
The behavior of successive iterates is shown in
Fig.
2(d–f). Regardless of how close the initial guess is to the actual solution,
the iterative scheme diverges (spirals away) from the intersection point. Although the
real part of the solution appears to converge after about 25 iterations, the overall
complex solution does not converge, as seen from the undamped oscillations in the
imaginary part of the solution. Thus, this iteration scheme, although derived from the
same parent equation, does not converge. This behavior is typical for transcendental
equations involving trigonometric functions and is therefore encountered for waveguide
problems, as we will show in section 4.2.
On the other hand, convergence of
Eq. (4)
can be improved considerably by choosing an alternative iterative form as follows.
Squaring both sides of
Eq. (4)
yields:
from which we can obtain yet another sequence of {xn
} of iterations according to:
Fig. 3. Criterion for convergence of the iterative solution. The magenta line in both
figures is the curve y=x and the blue lines are
two different cases of y=f (x).
The solution (a) diverges for |f′(x)|≥1 and (d)
converges for |f′(x)|<1. (b), (c), (e), and
(f) show how the convergence/divergence is reflected in the behavior of the real
and the imaginary parts of the successive iterates.
The preceding examples illustrate the crucial importance of choosing appropriate
iterative forms. Arriving at such a form necessitates an understanding of the
convergence of the iterative scheme.
Figure 3
pictorially shows the criterion for convergence of the iterative scheme in
Eq. (2). Whether the iteration spirals
toward the intersection point (the solution) or away from it is governed by the relative
slopes of the intersecting curves. If
f (
x) is steeper
than x (i.e., if |
f′(
x)|>1), the successive
computations and re-substitutions spiral away from the intersection and the solution
diverges. On the other hand, if
f (
x) rises gently
compared to
x (i.e., if
|
f′(
x)|<1), then the scheme spirals toward the
intersection and the solution converges. For convergent functions, the rate of
convergence is governed by the magnitude of |
f′(
x)|.
The RHS of
Eq. (8) is much flatter
(|
f′(
x)|≪1) near the solution compared to the RHS
of
Eq. (5), which results in the latter’s
considerably slower convergence. While the preceding justification is not a rigorous
proof, it provides a basis for choosing the manipulations required to obtain convergent
forms of the practically-useful equations we consider in the forthcoming sections [
18
J. Dugundji and A. Granas, Fixed Point Theory
(Springer-Verlag, 2003).
].
3. Dispersion equation of a general asymmetric three-layer structure
The design of many integrated photonics systems of practical importance—including both
novel plasmonic waveguide architectures as well as conventional dielectric
waveguides—can often be reduced to solving for the effective mode index and
electromagnetic field distributions in a three-layer planar structure. As such, we focus
our analysis on a generalized three-layer slab waveguide, as shown in
Fig. 4(a). The central layer is called the core and
has a complex relative permittivity
εf
. The bottom and top cladding layers are called the substrate (with
permittivity
εs
) and cover (with permittivity
εc
), respectively.
Fig. 4. Geometries and modes of three-layer infinite slab waveguide structures. Parts
(e–i) plot the typical magnetic field profiles.
Depending on which materials comprise the three layers, it is possible to classify the
most commonly-encountered waveguides into three basic types. The dielectric waveguides,
shown in
Fig. 4(b), have all three layers made
of dielectrics, with
εf
>
εs
,
εc
. The structures in
Figs. 4(c) and (d)
are two basic configurations of plasmonic waveguides. The first, shown in
Fig. 4(c), consists of an dielectric sandwiched
between two (possibly different) metals and is commonly known as a
metal-dielectric-metal (MDM) waveguide. The second structure, shown in
Fig. 4(d), consists of a metal layer between two
(possibly different) dielectrics; it is commonly known as an dielectric-metal-dielectric
(DMD) waveguide.
Figures 4(e–i) schematically show the possible
modes of a general three-layer slab waveguide. These modes are distinguished according
to several criteria: mode number (number of zero-crossings in the field), polarization
(transverse electric or transverse magnetic), field symmetry (even or odd), and field
profile in the core (sinusoidal or hyperbolic). Of the possible field profiles shown in
Fig. 4, the mode shown in (e), having
sinusoidal core-field variation with no zero-crossings, is exclusive to dielectric
waveguides and is known as the fundamental mode. Modes (h) and (i), having hyperbolic
core-field variation, are exclusive to plasmonic waveguides and are known as the
gap-plasmon modes. Modes (f) and (g), having sinusoidal core-field variation with ≥1
zero-crossings, are common to both dielectric and plasmonic waveguides.
This rich variety of modes arises out of the solutions of the dispersion equation
written for an asymmetric three-layer slab waveguide. In principle, a single dispersion
equation can completely describe all the modes of both dielectric and plasmonic
waveguides. However, because the nominally-assumed core-field distributions for the two
types are different (sinusoidal for dielectric waveguides and hyperbolic for plasmonic
waveguides), we choose to write two separate equations for the two types [
19
C. R. Pollock, Fundamentals of Optoelectronics
(McGraw-Hill Professional Publishing,
2003).
,
20
J. J. Burke, G. I. Stegeman, and T. Tamir, “Surface-polariton-like waves guided by thin, lossy
metal films,” Phys. Rev. B
33, 5186–5201
(1986). [CrossRef]
]:
For convenience, we have collected the symbol definitions and their expressions in
Table 1. Using these definitions,
Eqs. (9) and
(10) can be cast explicitly as transcendental equations in a single
complex variable k or κ. Furthermore, it is evident that
Eq. (9) transforms to
Eq.
(10) for purely imaginary values of
k (i.e.,
k →
iκ,
κ ∊ ℝ). Having solved for
k or
κ, the mode’s complex effective index
n
eff may be calculated using relations in
Table 1.
Table 1. Definitions of various quantities and their expressions in terms of the
material parameters and the perpendicular core wavevector k or κ.
| Symbol | Definition/Expression |
|---|
|
k
0
| 2π/λ0 |
|
p
| 1 (for TE),
εf
/εc
(for TM) |
|
q
| 1 (for TE),
εf/εs
(for TM) |
|
K
c, K
s
|
,
|
|
Q
c, Q
s
|
,
|
|
γ
c, γ
s
|
,
|
|
α
c, α
s
|
,
|
|
ξ
c, ξ
s
|
,
|
|
G
c, G
s
|
,
|
|
S
| (pα
c+qα
s)/2 |
|
A, B
| (pξ
c+qξ
s)/2, (pξ
c − qξ
s)/2 |
|
n
eff
|
,
|
Fig. 5. Categories of three-layer slab waveguides for the purposes of iterative
solution.
We will now begin the description of the actual iterative solution procedure. We saw in
section 1 that the convergence of this technique depends crucially on the iteration
function. As a result, the iterative form for determining modes with sinusoidal
core-fields [
Fig. 4(e–g)] is different from the
one needed for determining modes with hyperbolic core-fields [
Fig. 4(h–i)]. Therefore, we split our description into two broad
categories: dielectric waveguides and plasmonic waveguides. These two categories are
further divided depending upon the degree of confinement (strong or weak) for dielectric
waveguides, and core/cladding type (MDM or DMD) for plasmonic waveguides.
Figure 5 depicts the division of the waveguide
modes that we have made for the purposes of describing our iterative solution process.
In the forthcoming sections, we will obtain and test rapidly-convergent iterative forms
for calculating the mode indices of these four sub-categories of three-layer asymmetric
slab waveguides. These useful forms will be boxed for the convenience of the reader.
5. Modes of plasmonic waveguides
The examples in the preceding sections indicate the relative simplicity of the iterative
method when applied to dielectric waveguides. However, the true advantage of this
technique emerges when dealing with systems having material loss (complex permittivity)
or leakage loss (complex propagation constant). The modal indices are complex in both
these cases and providing graphical root-finders with a complex initial guess becomes
difficult. As such, the self-converging behavior of the iterative algorithm becomes an
invaluable asset.
Propagation loss and waveguide leakage arise routinely in sub-wavelength-scale metallic
waveguides. Intense research efforts are currently underway in the field of metal-based
optics, also known as plasmonics (see, e.g., [
1
M. L. Brongersma and P. G. Kik, eds., Surface Plasmon Nanophotonics, vol.
131 of Springer series in optical sciences
(Springer, 2007).
]
and references contained therein). Extended metal structures support surface
plasmon-polariton (SPP) modes that are electromagnetic waves strongly coupled to
collective electron oscillations in the metal. To exploit the strong light localization
achievable in plasmonic structures, a variety of waveguide configurations have recently
been proposed and demonstrated [
1
M. L. Brongersma and P. G. Kik, eds., Surface Plasmon Nanophotonics, vol.
131 of Springer series in optical sciences
(Springer, 2007).
]. However,
because of its simplicity, the MDM geometry remains a canonical structure for achieving
and studying strong light confinement. As such, a convenient technique for determining
the optical modes of MDM waveguides is highly valuable.
We begin the description of the iterative solution technique for plasmonic waveguides by
referring to the division of slab waveguide types illustrated in
Fig. 4(c) and (d). We first consider the modes of an MDM-type
plasmonic waveguide followed by a treatment of the DMD-type.
5.1. Metal-dielectric-metal waveguides
A MDM waveguide supports gap-plasmon and TM-like waveguide modes. Even and odd
gap-plasmon modes are practically important, as they offer the strongest
sub-wavelength field confinement. However, TM-like modes are also of theoretical
value and are necessary for gaining a complete understanding of reflection and
transmission phenomena in MDM waveguides and antennas [
16
S. E. Kocabaş, G. Veronis, D. A. B. Miller, and S. Fan, “Modal analysis and coupling in metal-insulator-metal
waveguides,” Phys. Rev. B
79, 035120 (2009). [CrossRef]
]. Effective indices for both these types of modes can be
conveniently determined using the iterative technique, as we show in the
following.
5.1.1. Gap-plasmon modes
We start with our master plasmonic
Eq.
(10) and rearrange it as:
Here,
αc
,
αs
, and
S are related to
κ as specified
in
Table 1. Treating this as a quadratic
equation in
κ and solving yields the convergent iterative form
for the MDM gap-plasmon modes as:
This simple-looking equation can calculate even (+ sign) and odd (- sign)
gap-plasmon mode indices of a wide variety of deep sub-wavelength asymmetric
plasmonic waveguides. We illustrate the use of
Eq. (18) through two examples. Our first structure is a
50-nm-thick gold-silica-silver slab waveguide operating at 1550 nm. The relative
permittivities of gold, silica, and silver are assumed to be -95.92-i10.97,
2.1025, and -143.49-i9.52, respectively. The effective index of the even
gap-plasmon mode, calculated using the + sign in
Eq. (18), is given in
Table
4.
Figure 10 shows plots of the
LHS and RHS of
Eq. (18) and the
convergence of the real and imaginary parts of the effective index.
The thin 50-nm slab considered above does not support an odd (antisymmetric)
gap-plasmon mode. However, increasing the silica thickness to 3
µm
allows the structure to support gap-plasmon modes of both symmetries. We calculate
the effective indices iteratively using
Eq. (18) with + and - signs for odd and even gap-plasmon modes
respectively. Once again, the iterative method agrees with the Newton’s method
(
Table 4).
5.1.2. TM-like waveguide modes
The other class of modes supported by a MDM waveguide are the TM-like waveguide
modes with profiles as shown in
Fig.
4(f) and (g). For these modes,
κ is purely imaginary
and plasmon
Eq. (10) transforms
to dielectric
Eq. (9). The
iterative form for obtaining the indices of these modes is identical to
Eq. (12), with only a few slight
differences as regards its implementation.
Fig. 10. (a) Normalized left- and right-hand sides of
Eq. (18);
f
(
κ) refers to the RHS. Convergence of the real (b)
and the imaginary (c) parts of the effective index for the fundamental
gap-plasmon mode.
Table 4. Effective indices of various modes of MDM waveguides operating at
1550 nm obtained using the iterative method, with a comparison to the
solutions calculated using the Newton’s method as implemented by the
FindRoot function in Mathematica™.

For the case of dielectric waveguides, the mode index
M
assumed integer values starting from 0 for even modes and 1 for odd modes. For
MDM waveguides, this is reversed:
M assumes integer values
starting from 1 for even modes and 0 for odd modes. This is a consequence of
the signs of
p and
q being reversed for MDM
waveguides due to the negative permittivity of the metal “claddings.”
Additionally, because of the large index difference between metals and
dielectrics, the TM-like modes of MDM waveguides are almost always calculated
using the strong-confinement formula in
Eq. (12).
To show how TM-like modes are calculated for MDM waveguides, we choose a 300-nm
thick silica layer sandwiched between semi-infinite gold and silver layers. The
permittivities of each material are the same as assumed in the previous
subsection. The indices of the first five TM-like modes, calculated using the
strong-confinement formula
Eq.
(12), are listed in
Table 4
alongside the corresponding solutions obtained using the Newton’s method.
5.1.3. Symmetric MDM waveguides
Many contemporary high-confinement architectures employ the
symmetricMDMwaveguide as their skeleton structure. Equality of the relative
permittivities of the substrate and cover further simplify
Eq. (18) for the gap-plasmon modes.
We can express the resulting iterative forms as:
5.2. Dielectric-metal-dielectric waveguides
The second type of plasmonic waveguide structure commonly encountered in
practice is an DMD waveguide, as shown in
Fig. 4(d). In theory, metal films of arbitrary thickness in a
homogenous dielectric medium (including air) or in Kretschmann-type coupling
configurations [
21
H. Raether, “Surface plasmons on smooth and rough surfaces and on
gratings,” Springer Tracts Mod. Phys.
111, 1–133 (1988).
] are examples of
DMD waveguides. In practice, we refer to metallic waveguides as DMD-type
only if the SPP modes on the two metal-dielectric interfaces are coupled.
Because of the rapid decay of the fields (with distance) inside metals, such
a mode-coupling is possible only for thin (
h<100nm)
metal films.
To obtain the iterative form for determining mode indices of DMD waveguides,
we write
Eq. (10) as:
where
A and
B are defined in
Table 1. Considering this as a
quadratic equation in
A and
B leads us to
the desired iterative forms. We start by identifying the lower-index
dielectric as the substrate and using initial guesses of
A
0=
k
0,
B
0=0, and
κ
0=
k
0. We then iterate to obtain five different quantities
successively, using the following equations in the order shown:
Here,
an,bn
are intermediate dummy parameters and
ξ
c,n+1 and
ξ
s,n+1 are related to
κ
n+1 through
Table 1. The indices of the low- and high-energy plasmon modes
are obtained by using the + and the - signs, respectively, in
Eq. (21a). The large asymmetry
in the decay constants in the metal core (
κ) and dielectric
claddings (
ξc,ξs
) makes the determination of the effective indices of DMD modes a
numerically-challenging problem. For the iterative procedure, this
translates into a difficulty in obtaining convergent iteration expressions.
Therefore, unlike the previous cases, the iterative procedure for DMD
waveguides involves multiple steps instead of a single closed-form iteration
function. Admittedly, this multi-step algorithm goes against the “pocket
calculator” philosophy, but nevertheless is convenient and efficient
compared to direct numerical solutions.
We will now illustrate the use of Eq. (21) through specific examples.
Consider the operation of a 50-nm-thick silver film on a silica substrate at
1550 nm. The relative permittivities of silver and silica are the same as
assumed previously; the top cover is air (
εc
=1). We start by specifying
A
0,
B
0, and
κ
0, and obtain their successive values according to Eq. (21). The
convergence of
An,Bn
, and
κn
is shown in
Fig. 11;
Table 5 compares the solution
obtained using the iterative scheme with that computed using the Newton’s
method. In our computations, the Newton’s method was unable to return the
complex mode index unless we gave a very precise
Table 5. Effective indices of various modes of an DMD waveguide operating
at 1550 nm obtained using the iterative method and their comparison
with solutions obtained using the Newton’s method as implemented by
the FindRoot function in Mathematica™.

initial guess for both the real and the imaginary parts. On
the other hand, the iterative method computed the complex mode index
regardless of the initial guess.
Fig. 11. Convergence of the real and imaginary parts of κ,
A, and B in Eq. (21) for the case
of a 50 nm thick silver-silica-silver waveguide operating at 1550
nm.
Our next example is a symmetric structure with a 100-nm silver film
sandwiched between two silica layers. Although 100 nm is at the boundary of
the film behaving like an DMD waveguide versus two separate interfaces, we
choose these dimensions to highlight the robustness of the iterative
technique even for the most extreme cases of root-finding. For this
structure, both high- and low-energy modes exist, whose indices are
conveniently calculated by using the - and + signs, respectively, in Eq.
(21). The indices calculated using the iterative method and the Newton’s
method for this example are included in Table 5 and show good agreement.
Calculating these indices was especially difficult using the Newton’s method
because of their close numerical proximity. The iterative technique, on the
other hand, specifies separate functions (the + and- signs) which are
guaranteed to converge to these different modes, irrespective of their
numerical proximity.
5.2.1. Symmetric DMD waveguides
Symmetric DMD waveguides appear in the form of idealized waveguide
geometries such as metal films in homogenous dielectric media (including
air). Fortunately, for the symmetric case, the iterative scheme consists
of a single equation which can be written as:
These equations do not follow automatically from
Eq. (21) but instead have to
be derived separately by considering the dispersion equation for the
symmetric DMD waveguide. Eq. (22) are preferable to Eq. (21) for
analyzing symmetric DMD structures owing to better convergence behavior
and an obvious ease in programming.