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Semiconductor layers employed in fabricating advanced node devices are becoming thinner and their electrical properties are diverging from those established for highly crystalline standards. Since these properties also change as a function of depth within the film, accurate carrier profiling solutions are required. The Differential Hall Effect (DHE) technique has the unique capability of measuring mobility and carrier concentration (active carriers) through the depth of a semiconductor film. It comprises making successive sheet resistance and sheet Hall coefficient measurements as the thickness of the electrically active layer at a test region is reduced through successive material removal steps. Difference equations are then used to process the data and plot the desired depth profiles. The fundamentals of DHE were established in 1960s. Recently, the adaption of electrochemical processing for the material removal steps, and the integration of all other functionalities in a Differential Hall Effect Metrology (DHEM) tool, has made this technique more practical and accurate and improved its depth resolution to a sub-nm range. In this contribution, we review the development history of this important technique and present data from recent characterization work carried out on Si, Ge and SiGe layers. Full article

In remote sensing images, small objects have too few discriminative features, are easily confused with background information, and are difficult to locate, leading to a degradation in detection accuracy when using general object detection networks for aerial images. To solve the above problems, we propose a remote sensing small object detection network based on the attention mechanism and multi-scale feature fusion, and name it AMMFN. Firstly, a detection head enhancement module (DHEM) was designed to strengthen the characterization of small object features through a combination of multi-scale feature fusion and attention mechanisms. Secondly, an attention mechanism based channel cascade (AMCC) module was designed to reduce the redundant information in the feature layer and protect small objects from information loss during feature fusion. Then, the Normalized Wasserstein Distance (NWD) was introduced and combined with Generalized Intersection over Union (GIoU) as the location regression loss function to improve the optimization weight of the model for small objects and the accuracy of the regression boxes. Finally, an object detection layer was added to improve the object feature extraction ability at different scales. Experimental results from the Unmanned Aerial Vehicles (UAV) dataset VisDrone2021 and the homemade dataset show that the AMMFN improves the ${\mathrm{A}\mathrm{P}}_{\mathrm{s}}$ values by 2.4% and 3.2%, respectively, compared with YOLOv5s, which represents an effective improvement in the detection accuracy of small objects. Full article