Some common defects in prawn processing: their sources and remedies

The processing of prawns has assumed very great importance in India because processed prawn products have become one of the most important foreign exchange earners in the country.

RESIDUAL DEFECTS FOLLOWING RAPID ANNEALING OF BORON IMPLANTED SILICON WITH AND WITHOUT PREAMORPHISATION BY SILICON IMPLANTATION.

Implants of boron into silicon which has been made amorphous by silicon implantation have a shallower depth profile than the same implants into silicon. This results in higher activation and restricted diffusion of the B implants after annealing, and there are also significant differences in the microstructure after annealing compared with B implants into silicon. Rapid isothermal heating with an electron beam and furnace treatments are used to characterize the defect structure as a function of time and temperature. Defects are seen to influence the diffusion of non-substitutional boron.

TRANSIENT SCANNING ELECTRON BEAM ANNEALING METHODS USED TO STUDY DIFFUSION AND DEFECTS IN IMPLANTED SILICON.

Rapid thermal annealing of arsenic and boron difluoride implants, such as those used for source/drain regions in CMOS, has been carried out using a scanning electron beam annealer, as part of a study of transient diffusion effects. Three types of e-beam anneal have been performed, with peak temperatures in the range 900 -1200 degree C; the normal isothermal e-beam anneals, together with sub-second fast anneals and 'dual-pulse' anneals, in which the sample undergoes an isothermal pre-anneal followed by rapid heating to the required anneal temperature is less than 0. 5s. The diffusion occuring during these anneal cycles has been modelled using SPS-1D, an implant and diffusion modelling program developed by one of the authors. This has been modified to incorporate simulated temperature vs. time cycles for the anneals. Results are presented applying the usual equilibrium clustering model, a transient point-defect enhancement to the diffusivity proposed recently by Fair and a new dynamic clustering model for arsenic. Good agreement with SIMS measurements is obtained using the dynamic clustering model, without recourse to a transient defect model.