Concept:
The conductivity of the sample \({\rm{\sigma }} = {\rm{q}}\left( {{\rm{n}}{{\rm{\mu }}_{\rm{n}}} + {\rm{p}}{{\rm{\mu }}_{\rm{p}}}} \right)\) if, n ≫ p, we can approximate \({\rm{\sigma }} \approx {\rm{qn}}{{\rm{\mu }}_{\rm{n}}}\)
Application:
The conductivity of the sample \({\rm{\sigma }} = {\rm{q}}\left( {{\rm{n}}{{\rm{\mu }}_{\rm{n}}} + {\rm{p}}{{\rm{\mu }}_{\rm{p}}}} \right)\) since, n ≫ p, we can approximate \({\rm{\sigma }} \approx {\rm{qn}}{{\rm{\mu }}_{\rm{n}}}\)
Using \({\rm{n}} = {{\rm{N}}_{\rm{D}}} = {10^{16}}{\rm{c}}{{\rm{m}}^{ - 3}}\), we have \({\rm{\sigma }} = 1.6 \times {10^{ - 19}} \times {10^{16}} \times 1200\)
\(\Rightarrow {\rm{\sigma }} = 1.92\frac{{{\rm{mho}}}}{{{\rm{cm}}}}\)
Now, resistivity
\({\rm{\rho }} = \frac{1}{{\rm{\sigma }}} = \frac{1}{{1.92}} = 0.52{\rm{\;\Omega cm}}\)