Efficiency of an organic solar cell is very sensitive to fabrication procedure. One of the most important parameters is active layer morphology which radically influences several cell properties such as generation rate, layer resistance, charge carrier motilities etc. Meantime, in P3HT:fullerene based solar cells, using PCBM would improve the morphology and increase the cost simultaneously. On the other hand, C60 is way less expensive, but its limited solubility in common solvents would influence cell performance. To benefit from its cost and as the formation of C60 aggregates and P3HT crystallinity significantly depend on the solvent which would influence several cell properties, one should find a proper solvent. To make an in-depth investigation of solvent effects, experimental investigations will not suffice and using a precise model to fit the data and extract hidden parameters would help us to have a deep understanding of the cells physical basis. In this work, an optimization algorithm is employed to fit a numerical model simulation results with experiments and the model benefits from a field dependent series resistance. Simulation results indicate that a suitable solvent mainly improves the cell performance by changing 3 basic parameters which are G, μn and μp. Additionally, although parameters such as Eg and DC dielectric constant are very crucial in determining power conversion efficiency, they cannot be effectively improved changing the solvent. It is reported that the cell prepared by Cl-naph:CB performs better than the other cells. Considering our results, it can be attributed to its larger G, μn and μp. It also has the least Rs and the largest Rsh among all other P3HT:C60 based cells (which is caused by its higher mobility-carrier density product). This work gives experimentalists an idea of how they should choose a solvent. The results can also be generalized to find a proper solvent for other active layer materials.
Static thermal analyses for the earlier concept and the enhanced quasi-monolithic integration technology (QMIT) are performed in detail. The effects of several parameters such as the properties of the materials involved and different geometries in all possible structures are described. Simulation results confirm a very low thermal resistance for the enhanced QMIT structure and highlight its superiority to the earlier concept of QMIT structures. This leads to a longer lifetime, a higher reliability and a better performance of the packaging.
A systematic approach is presented to achieve a reliable neural model for microwave active devices with different numbers of training data. The method is implemented for a small-signal bias depended modeling of pHEMT in tow different environments, on a standard test-fixture and in the New Generation Quasi-Monolithic Integration Technology (NGQMIT), with different numbers of training data. The errors for different numbers of training data have been compared to each other and show that by using this method a reliable model is achievable even though the number of training data is considerably small. The method aims at constructing a model, which can satisfy the criteria of minimum training error, maximum smoothness (to avoid the problem of over-fitting), and simplest network structure.
Application of a new thermal nano-probe based on the changes of electrical resistivity of a nanometer-sized filament with temperature has been presented for the thermal imaging of microwave power active devices. The filament is integrated into an atomic force scanning probe piezoresistive type cantilever. The novel thermal probe has a spatial resolution better than 80 nm and a thermal resolution of the order of 10-3 K. The measurements have been successfully performed on a 30 fingers GaAs-MESFET with a maximum power dissipation of 2.5 W. The microwave transistor has been implemented in a circuit in such a way to prevent the undesired microwave oscillations. In this case the power dissipation is equal to the dc power input. The near-field measurements have been compared with three-dimensional finite element simulations. A good agreement between simulations and measurements is achieved.
In this paper, application of scanning probe microscopy (SPM) and nanometer surface profiler of DEKTAK for determination of thermal stress in standard structure of QMIT is described. A three dimension finite element (3DFE) thermal stress simulator, a scanning probe microscopy measurements and nanometer surface profiler accompanied with a Peltier element (PE) have been used to determine the thermal stress distribution in the standard structure of QMIT. In this method by measuring and mapping the surface profile of Si-wafer around the embedded devices using SPM and DEKTAK the induced thermal stress is determined. Effects of different parameters such as baking temperature, power dissipation of the embedded GaAs-FET, geometry and elastic properties of thermal conductive epoxy have been described in details. Remarkable agreement between calculated and measured displacements created by thermal stress was found.
A new imaging method which can obtain the gray levels directly from the output waveform of Pulsed Laser Radar (PLR) is developed. A simple digital signal processing technique and multi layer perceptrons (MLP) type neural network (NN) have been used to obtain the gray level information from the pulse shapes. The method has been implemented in a real PLR to improve contrast and speed of 2D imaging in PLR. To compare the method with the standard method, a picture consists of 16 gray levels (from 0 for black to 1 for white) with both method has been scanned. Because of the ability of NNs in extracting the information from nonlinear and noisy data and preprocessing of the noisy input pulse shapes to the NN, the average and maximum of errors in the gray levels in comparison with standard method more than 88.5% and 72.6% improved, respectively. Because in this method the effect of the noise is decreased, it is possible to make the imaging with the same resolution as in standard method but with a lower averaging in sampling unit and this dramatically increases speed of the measurements.