Photothermal Depth Profiling Of Optical Gradient Materials By Neural Network

Details Photothermal Depth Profiling Of Optical Gradient Materials By Neural Network Photothermal Depth Profiling Of Optical Gradient Materials By Neural Network

Jul 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010pobeo..89..147p&link_type=abstract

Publications of the Astronomical Observatory of Belgrade, vol. 89, pp. 147-150

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Scientific paper

The mathematical model which describes dependence of optically induced temperature variations on modulation frequencies of incident laser beam is derived. By analysis of the model it is shown that measured photothermal frequency response involves information about magnitude and shape of depth variations of the sample absorption coefficient. The neural network for photothermal optical depth profilometry is suggested and developed. The numerical simulations have been carried out for suitably chosen optically gradient samples. It has been demonstrated that the suggested algorithm has high accuracy and low noise sensitivity with a short reconstruction surface time. Also, the algorithm doesn't demand the special computer resources.

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