A quantitative analysis of linear temperature-dependent coupled thermoelectronic diffusion waves in the generation of the laser-induced IR photothermal radiometric signal from a photoexcited, plasma-wave-dominated, semi-infinite semiconductor Si wafer is presented. The strong coupling between the (usually assumed decoupled) carrier and thermal wave transport equations is accounted for explicitly and the range of violation of the Vasil’ev-Sandomirskii condition is studied. Thermoelectronic coupling is found to degrade the sensitivity of the plasma-originating IR radiometric signal in high-quality process Si substrates at elevated background temperatures and high modulation frequencies, with the exception of highly degenerate plasmas. © 1997 Society of Photo-Optical Instrumentation Engineers.

A quantitative analysis of the temperature dependence (40–300 K) of the photothermal reflectance signal on crystalline and phosphorus implanted silicon wafers is presented. A theoretical model has been adapted for the interpretation of experimental results concerning photothermal measurements as a function of temperature. The quantitative analysis of the temperature dependence of the photothermal reflectance signal on crystalline and implanted silicon wafers at various doses led to a nondestructive evaluation of their optical, electronic, and thermal properties in a wide temperature range.

A complete theoretical analysis of the laser photomodulated thermoreflectance signal from a two‐layer semiconducting wafer is presented. It is shown that the electronic and thermal properties of a thin surface layer may be determined by using the measured induced photothermal signal. Several numerical simulations are performed in order to study the influence of various electronic, optical and thermal parameters of the two‐layer sample on the photomodulated thermoreflectance signal. The influence of the upper layer as well as the influence of the substrate on the signal are also discussed and parameter regimes are identified, where the characterization of the thin overlayer may be possible using this technique.

A two layer model has been developed in order to study the dependence of the photothermal radiometric signal on the optoelectronic and thermophysical properties of a two layer semiconducting sample. In the following theoretical analysis, both thermal and electronic contributions have been used in order to interpret the infrared emission of a semiconducting sample under the influence of a pump modulated laser beam. The potential of the above model towards a quantitative analysis of photothermal radiometric experimental data, for the extraction of thermal and electronic parameters of the semiconducting thin film and substrate, has been examined. Experimental evidence of the quality of this model has also been presented.

Skin cancer is a major public health problem, especially in the western world, having direct negative impact on life expectancy, quality of life, and on the economy in general. However, early detection of skin cancer could significantly reduce the mortality rate. For this purpose, tremendous efforts have been deployed in recent years for developing machine learning algorithms that can help in the early detection of skin cancer. In this paper, we focus on the classification of the three most common types of skin cancer lesions: Basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma (MEL). Two different convolution neural network (CNN) architectures were implemented for this aim: YOLO, version 7 (v7) using transfer learning and an in-house developed CNN algorithm with optimum number of layers and hyper-parameters. The results obtained by implementing the two algorithms with a total number of 2,792 training samples (after performing data augmentation) show better performance compared to some of the recently published works in the literature. Using YOLO, v7, the average accuracy, sensitivity, and specificity are 89.65%, 85%, and 91.90%, respectively. The aforementioned average values using the proposed CNN algorithm are 90.12%, 85.55%, and 92.57%, respectively.