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Tito Busani

Publications and source records attributed to Tito Busani.

2 recordsLinked to original sources

Post Annealing Crystallization behavior of RF Sputtered Yttrium Iron Garnet thin films on Si/SiO2 patterned substrates

Yttrium Iron Garnet YIG (Y3Fe5O12), is a commonly used material for magnonic devices due to its crystal and chemical structure, which makes the material highly ferromagnetic and enables long-range magnon propagation. Magnonic devices were fabricated by depositing a 390 nm thick thin film of YIG, using low vacuum RF sputtering, on Si substrates with a 240 nm buffer layer of SiO2. Two sets of devices were used to study the effect of the Si/SiO2 interface on the YIG. The first set features patterned hole pairs on the SiO2, which was created using fluorine etching. Patterned samples were used as seed nucleation points to study the crystallization behavior. The second set was a non-patterned Si/SiO2 with YIG deposited uniformly on the top. Post-deposition recrystallization of the YIG film was accomplished in a horizontal furnace under O2 atmosphere, between 750 degrees C and 850 degrees C. By patterning devices with a SiO2 buffer layer, depositing YIG via RF sputtering, and subsequently crystallizing the films in a furnace, we establish a fabrication route toward devices that can be suspended. Although further optimization of stoichiometry is required, achieving precise compositional control would enable the realization of fully suspended and released YIG devices that can be transferred onto alternative substrates.

cond-mat.mtrl-sci

Probabilistic analysis of solar cell optical performance using Gaussian processes

This work investigates application of different machine learning based prediction methodologies to estimate the performance of silicon based textured cells. Concept of confidence bound regions is introduced and advantages of this concept are discussed in detail. Results show that reflection profiles and depth dependent optical generation profiles can be accurately estimated using Gaussian processes with exact knowledge of uncertainty in the prediction values.It is also shown that cell design parameters can be estimated for a desired performance metric.

cs.LG