A sequence of radioactive decays can be used to implant alpha-emitting sources in substrates for thickness measurements of films grown on them. Starting with 228Th, both direct recoil implantation of 224Ra and a two-stage recoil implantation of 212Pb were performed. The thickness of germanium layers grown on gallium arsenide was determined by measuring the energy loss of the alpha particles traversing them. The results were found to be consistent with those obtained by other methods. The special advantages of the present procedure are discussed.

Posterior fossa tumors (PFT) are the most common tumors in children. Differentiation between the various PFT types is critical, as different tumors have diverse treatment approaches. This study proposes the use of fused architecture comprising two neural networks, a pre-trained ResNet-50 Convolutional Neural Network (CNN) and a tabular based network for the classification of PFT. The study included data for 158 MRI scans of 22 healthy controls and 136 pediatric patients with newly diagnosed PFT (63 Pilocytic Astrocytoma, 57 Medulloblastoma and 16 Ependymoma). The input data for classification were from magnetic resonance imaging: post contrast T1-weighted, fluid attenuated inversion recovery and diffusion Trace images, and tabular data: subject's age. Evaluation of the model was performed in a stratified 5-fold cross-validation manner, based on accuracy, precision, recall and F1 score metrics. Model explanation was performed in terms of visual explanation of the CNN by Gradient-weighted Class Activation Mapping (Grad-CAM) and by testing the contribution to the classification results of the different imaging input data sets and the proposed fused architectures relative to CNN only and tabular only architectures. The best classification results were obtained with the fused CNN + tabular data architecture, and based on diffusion Trace images, achieving mean cross-validation accuracy of 0.88±0.04 for the validation and 0.87±0.02 for the test dataset. Overall, the proposed architecture achieved improvement in accuracy and F1 score compared to CNN method for this dataset.

A method for determining both the thickness and the average stoichiometry of thin films is presented. The method is based on implanting radioactive -sources in the substrate prior to layer growth and measuring the energy loss of the -particles as they traverse the layer. Information about the stoichiometry is obtained through the comparison of the energy loss of -particles of different initial energies. Experimental examples for the utilization of this method are presented, in which Sb was grown on Si substrates, GaAs, InAs and AlAs on GaAs and YBCO on YSZ. The experimental precision which can be expected using the method is discussed, together with specific scenarios in which it could be advantageously applied.

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