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which again agree with experiment [127]. 6.2 EXCITONS IN HETEROSTRUCTURES In the same way as in bulk, excitons can be formed by the bonding of free electronfree hole pairs or through resonant excitation. Whereas in bulk, the total energy of the exciton is simply the energy of the free electron-free hole pair (i.e. the band gap) plus the exciton binding energy Exo, in a heterostructure there are additional components due to the electron and hole confinement energies, i.e.

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SOM training is based on a competitive learning strategy. Assume I-dimensional input vectors zp, where the subscript p denotes a single training pattern. The first step of the training process is to define a map structure, usually a two-dimensional grid (refer to Figure 4.3). The map is usually square, but can be of any rectangular shape. The number of elements (neurons) in the map is less than the number of training patterns. Ideally, the number of neurons should be less than or equal to the number of independent training patterns. With each neuron on the map is associated an I-dimensional weight vector which forms the centroid of one cluster. Larger cluster groupings are formed by grouping

k,s := 0,0 ;

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The total exciton energy is clearly a function of structure because of the structural dependency of the confinement energies. In addition, it must be expected that the Coulombic potential energy, i.e. Ex , also depends upon the structure. This latter

{ Invariant:

by the following: where P is the usual quantum mechanical linear momentum operator:

O O O O O O/

O^k^iV A s = <Z N-k }

By using this form for the kinetic energy operator T, the Schrodinger equation then becomes:

Bound function:

where the function V(x, y, z] represents the potential energy of the system as a function of the spatial coordinates. Restricting this to the one-dimensional potential of interest here, then the Schrodinger equation for a particle of mass m in a potential well aligned along the z-axis (as in Fig. 2.1) would be:

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Figure 4.3: Self-organizing map together "similar" neighboring neurons. Initialization of the codebook vectors can occur in various ways: Assign random values to each weight w^j = (w k j 1 ,w k j 2 , , w K J I ] , with K the number of rows and J the number of columns of the map. The initial values are bounded by the range of the corresponding input parameter. While random initialization of weight vectors is simple to implement, this form of initialization introduces large variance components into the map which increases training time. Assign to the codebook vectors randomly selected input patterns. That is,

do k<N * k,s := k+l,s+a[k] { 5 = (Zi:0^ Let us check that this meets all the requirements for constructing a loop. The requirement (13.9) enables us to check that the termination condition has been implemented correctly. In this case, instantiating done to k ^ N and inv to the invariant, we get

Outside of the well, V(z) = oo, and hence the only possible solution is U(z) = 0, which in turn implies that all values of the energy E are allowed. Within the potential well, the Schrodinger equation simplifies to:

A 5=

which implies that the solution for U is a linear combination of the functions f ( z ) which when differentiated twice give f(z). Hence try the solution:

with p~ 7(1, P). This approach may lead to premature convergence, unless weights are perturbed with small random values. Find the principal components of the input space, and initialize the codebook vectors to reflect these principal components.

1 88

Substituting into equation (2.6) then gives:

Consideration of the boundary conditions will yield the, as yet unknown, constant k. With this aim, consider again the kinetic energy term for this system, i.e.

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