In summary, PAS gives microscopic information about vacancy defects in semiconductors in the concentration range 10 15 -10 19 cm -3 . The positron lifetime is the fingerprint of the open volume associated with a defect, and it can be used to identify mono- and divacancies and larger vacancy clusters. Doppler broadening of the annihilation radiation, on the other hand, can be used to identify the nature of the atoms surrounding the vacancy. Consequently, vacancies on different sub lattices of a compound semiconductor can be distinguished, and impurities associated with the vacancies can be identified. The charge state of a vacancy defect can be determined by the temperature dependence of the positron -trapping coefficient, and positron localization into Rydberg states around negative centers yields information about ionic acceptors that have no open volume. Importantly, as shown in this chapter, the methods based on positron annihilation are not restricted by the nature or physical dimensions of the semiconductor. Defects can be studied in narrow- and wide-bandgap semiconductor materials in samples of any conductivity. Bulk crystals as well as thin films can be subjected to the experiments and defects identified.

Positron annihilation is a well-established technique for producing spectra which can be analyzed for extracting physically meaningful parameters that characterize material defects and vacancies on an atomic scale. Mathematically, this is based on fitting a parameter-dependent model to the experimental data. Traditionally, this fit involves local nonlinear optimization routines that depend on a reasonable initial guess for the searched parameters. Therefore, very different sets of parameters may yield indistinguishably good fits for a given experimental spectrum but, give rise to ambiguities in data analysis. In order to alleviate them, a computer program has been developed for analyzing positron lifetime spectra by incorporating a global nonlinear optimization routine based on Simulated annealing (SA) into the Markov chain Monte-Carlo Bayesian inference algorithm (MCMC-BI) so that it provides a robust fitting tool and yields information on the reliability of the results. It is tested against experimental spectra, comparing the results with those from the well-established commercial programs.