Dependence of the leakage current on the film quality in polycrystalline silicon thin-film transistors Empirical relationship between low-frequency drain current noise and grain-boundary potential barrier height in high-temperature-processed polycrystalline silicon thin-film transistorsThe off-state current in n-and p-channel polycrystalline silicon thin-film transistors ͑polysilicon TFTs͒ is investigated systematically by conduction measurements at various temperatures and low-frequency noise measurements at room temperature. It is demonstrated that the leakage current is controlled by the reverse biased drain junction. The main conduction mechanisms are due to thermal generation at low electric fields and Poole-Frenkel accompanied by thermionic filed emission at high electric fields. The leakage current is correlated with the traps present in the polysilicon bulk and at the gate oxide/polysilicon interface which are estimated from the on-state current activation energy data. Analysis of the leakage current noise spectral density confirms that deep levels with uniform energy distribution in the silicon band gap are the main factors in determining the leakage current. The density of deep levels determined from noise analysis is in agreement with the value obtained from conductance activation energy analysis. The substantially lower leakage current observed in the n-channel polysilicon TFT is explained by the development of positive fixed charges at the interface near the drain junction which suppress the electric field.
Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%).
The structural and electrical properties of excimer laser annealed polycrystalline silicon thin-film transistors (polysilicon TFTs) are investigated in relation to the laser energy density. The devices were fabricated on 50 nm thick polysilicon films prepared by excimer laser crystallization (ELA) of amorphous silicon or by a combined solid phase crystallization (SPC) and ELA process. The structural properties of the polysilicon films have been investigated by transmission electron microscopy analysis. The effective density of states distributions in the polysilicon films and in the oxide traps near the oxide/polysilicon interface have been determined from low frequency noise measurements. The TFT performance parameters are compared with respect to their correlation with the structural properties of the polysilicon films and their electrically active defects, the basic variables being the starting material (amorphous silicon or SPC polysilicon) and the laser energy density.
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