Si plate radius influence on the photoacoustic signal processed by neural networks

The effect of the sample radius on the total photoacoustic signal processed by neural networks trained with undistorted and distorted signals is carefully analyzed for modulation frequencies from 20 Hz to 20 kHz. This is done for signals generated for a 400-mu m-thick Si n-type plate, whose radius varies from 2 to 7 mm. It is found that the networks trained with both undistorted or distorted signals yield the best predictions for sample radii between 2 and 3 mm, which is close to the used microphone aperture radius of 1.5 mm. The network trained only with undistorted signals gives the best results for sample radii comparable to the microphone dimensions. The obtained results of neural networks in the prediction of Si-plate radius indicate the experimental necessity to use samples with radii slightly over to a microphone aperture.

Automated summary

The effect of sample radius on the prediction accuracy of neural networks trained with undistorted and distorted signals is analyzed in this study. It is found that the networks trained with both undistorted and distorted signals perform best for sample radii between 2 and 3 mm, close to the microphone aperture radius of 1.5 mm. For sample radii comparable to the microphone dimensions, the network trained only with undistorted signals gives the best results. These findings highlight the importance of using slightly larger samples than the microphone aperture in experiments.