Defect analysis of semiconductor thin films for photovoltaic applications using photo-luminescence and photo-conductivity

The present thesis can be divided into three areas:1) the fabrication of a low temperature photo-luminescence and photoconductivity measuring unit 2) photo-luminescence in the chalcopyrite CulnSez and CulnS2 system for defect and composition analysis and 3) photo-luminescence and photo-conductivity of In:JS3. This thesis shows that photo-luminescence is one of most essential semiconductor characterization tool for a scientific group working on photovoltaics. Tools which can be robust, non-destructive, requiring minimal sample preparation for analysis and most informative of the device applications are sought after by industries and this thesis is towards establishing photo-luminescence as "THE" tool for semiconductor characterization. The possible application of photo-luminescence as a tool for compositional and quality analysis of semiconductor thin films has been worked upon by this thesis. Photo-conductivity complement photo-luminescence and together they provide all the information required for the fabrication of an opto-electronic device.

Regression models for bivariate survival data

Multivariate lifetime data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated lifetime when an individual is followed for the occurrence of two or more types of events, or when distinct individuals have dependent event times. In most studies there are covariates such as treatments, group indicators, individual characteristics, or environmental conditions, whose relationship to lifetime is of interest. This leads to a consideration of regression models.The well known Cox proportional hazards model and its variations, using the marginal hazard functions employed for the analysis of multivariate survival data in literature are not sufficient to explain the complete dependence structure of pair of lifetimes on the covariate vector. Motivated by this, in Chapter 2, we introduced a bivariate proportional hazards model using vector hazard function of Johnson and Kotz (1975), in which the covariates under study have different effect on two components of the vector hazard function. The proposed model is useful in real life situations to study the dependence structure of pair of lifetimes on the covariate vector . The well known partial likelihood approach is used for the estimation of parameter vectors. We then introduced a bivariate proportional hazards model for gap times of recurrent events in Chapter 3. The model incorporates both marginal and joint dependence of the distribution of gap times on the covariate vector . In many fields of application, mean residual life function is considered superior concept than the hazard function. Motivated by this, in Chapter 4, we considered a new semi-parametric model, bivariate proportional mean residual life time model, to assess the relationship between mean residual life and covariates for gap time of recurrent events. The counting process approach is used for the inference procedures of the gap time of recurrent events. In many survival studies, the distribution of lifetime may depend on the distribution of censoring time. In Chapter 5, we introduced a proportional hazards model for duration times and developed inference procedures under dependent (informative) censoring. In Chapter 6, we introduced a bivariate proportional hazards model for competing risks data under right censoring. The asymptotic properties of the estimators of the parameters of different models developed in previous chapters, were studied. The proposed models were applied to various real life situations.