MBE-grown Si and Si(1-x)Ge(x) quantum dots embedded within epitaxial Gd2O3 on Si(111) substrate for floating gate memory device.

Si and Si(1-x)Ge(x) quantum dots embedded within epitaxial Gd2O3 grown by molecular beam epitaxy have been studied for application in floating gate memory devices. The effect of interface traps and the role of quantum dots on the memory properties have been studied using frequency-dependent capacitance-voltage and conductance-voltage measurements. Multilayer quantum dot memory comprising four and five layers of Si quantum dots exhibits a superior memory window to that of single-layer quantum dot memory devices. It has also been observed that single-layer Si(1-x)Ge(x) quantum dots show better memory characteristics than single-layer Si quantum dots.

Some novel classifiers designed using prototypes extracted by a new scheme based on self-organizing feature map.

We propose two new comprehensive schemes for designing prototype-based classifiers. The scheme addresses all major issues (number of prototypes, generation of prototypes, and utilization of the prototypes) involved in the design of a prototype-based classifier. First we use Kohonen's self-organizing feature map (SOFM) algorithm to produce a minimum number (equal to the number of classes) of initial prototypes. Then we use a dynamic prototype generation and tuning algorithm (DYNAGEN) involving merging, splitting, deleting, and retraining of the prototypes to generate an adequate number of useful prototypes. These prototypes are used to design a "1 nearest multiple prototype (1-NMP)" classifier. Though the classifier performs quite well, it cannot reasonably deal with large variation of variance among the data from different classes. To overcome this deficiency we design a "1 most similar prototype (1-MSP)" classifier. We use the prototypes generated by the SOFM-based DYNAGEN algorithm and associate with each of them a zone of influence. A norm (Euclidean)-induced similarity measure is used for this. The prototypes and their zones of influence are fine-tuned by minimizing an error function. Both classifiers are trained and tested using several data sets, and a consistent improvement in performance of the latter over the former has been observed. We also compared our classifiers with some benchmark results available in the literature.