Large-grain polycrystalline silicon films with low intragranular defect density by low-temperature solid-phase crystallization without underlying oxide
The solid-phase crystallization of an amorphous silicon film to polycrystalline silicon by a low-temperature (⩽600 °C) furnace anneal has been investigated in a suspended cantilever structure without underlying silicon oxide by transmission electron microscopy and Raman spectroscopy. The grain size of polysilicon increases up to ∼3.0 μm and the density of intragranular defects decreases one order of magnitude in the samples without underlying oxide, compared with those with underlying oxide. The main reasons for the high quality of the suspended structures are thought to be due to the lower stress in the films during crystallization and a reduced grain nucleation rate.
Characterization of Extreme Si Thinning Process for Wafer-to-Wafer Stacking
Wafer-to-wafer 3D integration has a potential to minimize the Si thickness, which enables us to connect multiple wafers with significantly scaled through-Si vias. In order to achieve this type of 3D structure, backside thinning is a key step. Conventional mechanical grinding is known as the best way to remove bulk Si in terms of cost of ownership (CoO). However, mechanical damage such as induced dislocations needs to be removed after extreme thinning to avoid a serious impact on the device performance. CMP shows the best performance in terms of roughness with a significantly flat surface with only atomic step roughness. Furthermore, the existing mono-vacancies are as low as for a bulk Si substrate. However the total thickness variation (TTV) worsens as more Si is removed. The dry etch process enables a faster etch rate than CMP and wet etching. Furthermore, the mono-vacancy/damage layer after dry etching is equivalent to that achieved when combined with CMP. The combination of CMP and dry etch enables us to achieve extreme thinning of active device wafers (<;5 μm) with minimal roughness, no damage layer (mono-vacancy) and no edge delamination.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.