Theory of ferromagnetism in planar heterostructures of (Mn,III)-V semiconductors

A density-functional theory of ferromagnetism in heterostructures of compound semiconductors doped with magnetic impurities is presented. The variable functions in the density-functional theory are the charge and spin densities of the itinerant carriers and the charge and localized spins of the impurities. The theory is applied to study the Curie temperature of planar heterostructures of III-V semiconductors doped with manganese atoms. The mean-field, virtual-crystal and effective-mass approximations are adopted to calculate the electronic structure, including the spin-orbit interaction, and the magnetic susceptibilities, leading to the Curie temperature. By means of these results, we attempt to understand the observed dependence of the Curie temperature of planar δ-doped ferromagnetic structures on variation of their properties. We predict a large increase of the Curie temperature by additional confinement of the holes in a δ-doped layer of Mn by a quantum well.

Ferromagnetism mediated by few electrons in a semimagnetic quantum dot

A (II,Mn)VI diluted magnetic semiconductor quantum dot with an integer number of electrons controlled with a gate voltage is considered. We show that a single electron is able to induce a collective spontaneous magnetization of the Mn spins, overcoming the short range antiferromagnetic interactions, at a temperature order of 1 K, 2 orders of magnitude above the ordering temperature in bulk. The magnetic behavior of the dot depends dramatically on the parity of the number of electrons in the dot.

Coherently photoinduced ferromagnetism in diluted magnetic semiconductors

Ferromagnetism is predicted in undoped diluted magnetic semiconductors illuminated by intense sub-band-gap laser radiation . The mechanism for photoinduced ferromagnetism is coherence between conduction and valence bands induced by the light which leads to an optical exchange interaction. The ferromagnetic critical temperature TC depends both on the properties of the material and on the frequency and intensity of the laser and could be above 1K.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.