Electron beam induced current (EBIC) and atom probe tomography (APT) were used in this study to determine electrical activities and impurity compositions at extended defects in multicrystalline silicon (mc-Si) samples. The results provide, for the first time, information regarding the chemical species present at defects whose electrical activity has previously been measured. A new APT specimen fabrication process was developed with the ability to select a specific defect for APT analysis. Development of the APT specimen fabrication process proceeded by first selecting and optimising the preferential etching for nano-scale defect delineation. Three etchants were evaluated, namely Secco, Sirtl and Dash, from which the Secco etch was selected. Three parameters were optimised to produce etch pits with geometries that meet the requirements imposed by APT specimen fabrication methods. The optimum parameters were 0.05M potassium dichromate concentration, 20°C etch temperature, and 30sec etch time. In the second stage, marking techniques were developed in order for the defects to be located throughout the APT specimen fabrication process. However, it became apparent that the conventional APT specimen fabrication method could not be used to fabricate APT specimens containing selected defects in a mc-Si sample. This led to the development of a novel APT specimen fabrication approach which allowed APT specimens to be fabricated, reproducibly, containing grain boundaries and isolated dislocations. In order to evaluate accurately iron contamination in mc-Si, four atom probe parameters were optimised to maximise detection sensitivity: the evaporation rate, the laser beam energy, the pulse repetition rate and the specimen temperature. The optimisation process can be divided in to two parts. In the first part, a matrix of pre-sharpened single-crystal silicon specimens was subjected to a variety of experimental parameters. The optimised parameters were determined to be 0.3% evaporation rate, 0.5nJ beam energy, 160kHz repetition rate and 55K specimen temperature. The second part was to determine the iron detection efficiency –the percentage of detected Fe ions that can be correctly identified as Fe– and sensitivity using these parameters to analyse a specially prepared iron calibration specimen. The values were determined to be a detection efficiency of about 35% and sensitivity of 54ppm or 2.70x10<sup>18</sup> atom/cm<sup>3</sup>. The APT specimen fabrication process and the optimised APT analysis parameters were used to analyse four extended defects in mc-Si samples subjected to three different processing conditions, namely gold-contaminated, as-grown and phosphorus diffusion gettering (PDG). The important aspects of the analysis are listed below: • Gold was not detected at the grain boundary and its associated dislocations in the gold-contaminated specimen. The binding enthalpy of gold to such defects is thus less than 0.63eV. • Iron was not detected in any specimen. • Copper was observed at the grain boundary in the as-grown specimen in the form of individual atoms as well as clusters with diameters ranging between 4nm and 9nm. The electrical activity of the grain boundary was about 58%. • Nickel and carbon were detected at the grain boundary in the post-PDG specimen with the former having platelet structures with diameters and thicknesses ranging between 4nm-7nm and 2nm-4nm, respectively. The recombination strength of the defect was about 22%. • Two nickel clusters were found at the isolated dislocation in the post-PDG specimen. The clusters were spherical with an average diameter of 10nm. The distance between the two clusters was 35nm. The recombination strength of the defect was about 4%.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:667017 |
| Date | January 2015 |
| Creators | Lotharukpong, Chalothorn |
| Contributors | Wilshaw, Peter; Grovenor, Chris |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:a803fada-2296-41c3-9d96-864c186957a2 |
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