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by example 12.8, ( Z k : 0 ^ k < n : 2 k ) = 2 n -l } arithmetic }

(4.9) (4.10) (4.11)

conditions. In an analogy with the hydrogen atom the first excited state of the donor might be written as follows:

The property thus follows by the principle of strong mathematical induction. D Example 12.12. The recurrence relation

where A2S has been labelled specifically as it cannot be assumed, a priori, that The constant a has been introduced, and is determined by ensuring orthogonality between the ground state and this the first excited state, i.e.

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F.0 = 0 , F.I = 1 , F.(n+2) =F.(n+l) + F.n ,

Aot(t - 1) = oktp(t - 1) - oktp(t - 2)

Studies do exist in the literature of the excited states of donors in heterostructures [114], but to the author's present knowledge the extension utilizing the general form has not yet been attempted. A simple alternative approach which yields reasonable results is presented later in this chapter. 5.11 APPLICATION TO SPIN-FLIP RAMAN SPECTROSCOPY IN DILUTED MAGNETIC SEMICONDUCTORS 5.11.1 Diluted magnetic semiconductors

where n is a natural number, defines the Fibonacci numbers. The first few elements in the sequence are 0 , 1 , 1 , 2 , 3 , 5 , 8 .

Figure 5.20 Zeeman splitting of conduction (CB) and valence (VB) bands of a diluted magnetic semiconductor within an external magnetic field B

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The Fibonacci sequence has a number of remarkable properties which are discussed in depth in many textbooks. Many involve its relationship to the golden ratio, G, where

Principal component analysis (PCA) is a statistical technique used to transform a data space into a smaller space of the most relevant features. The aim is to project the original I-dimensional space onto an I-dimensional linear subspace, where / < /, such that the variance in the data is maximally explained within the smaller I-dimensional space. Features (or inputs) that have little variance are thereby removed. The principal components of a data set are found by calculating the covariance (or correlation) matrix of the data patterns, and by getting the minimal set of orthogonal vectors (the eigenvectors) that span the space of the Covariance matrix. Given the set of orthogonal vectors, any vector in the space can be constructed with a linear combination of the eigenvectors. Oja developed the first principal components learning rule, with the aim of extracting the principal components from the input data [Oja 1982]. Oja's principal

Diluted magnetic semiconductors (DMSs) [115,116] are important because of the strong exchange interaction between the hybridised sp3 d orbitals of the magnetic

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G = ld + v/5) .

ions and charge carriers. This manifests itself most clearly in the giant Zeeman splittings observed in both the conduction and valence bands when the material is placed in an external magnetic field. At low magnetic ion concentrations, the materials generally exhibit 'frustrated' paramagnetism, with the number of spin singlet states being reduced by nearest-neighbour anti-ferromagnetic spin-pairing [117]. At low magnetic fields (< 8 T) these spins remain locked and cannot contribute to the paramagnetism of the material; however experiments carried out under very high magnetic fields have been able to break these spin-doublets. The magnetic ion itself, usually Mn2+, sits substitutionally on a cation site and can generally be incorporated to high concentrations. The most common DMS is Cd1_xMnxTe, while others include Zn1_xMnxS, and more recently Ga1_xMnxAs. Fig. 5.20 shows the Zeeman effect in a DMS material, with the vertical arrows linking the heavy-hole states ( 3/2) and the electron states, thus illustrating the allowed interband transitions under circularly polarized light, i.e. is the a transition, and is the cr+ transition. The magnitudes of the conduction and valence band splittings are given in terms of the variables A and B as follows:

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