<p>Today, micro- and nano-electronic devices are becoming more complex and advanced as the dimensions are shrinking. It is therefore a very challenging task to develop new device technologies with performance that can be predicted. This thesis focuses on advanced measurement techniques and TCAD simulations in order to characterize and understand the device physics of advanced semiconductor devices. </p><p>TCAD simulations were made on a novel MOSFET device with asymmetric source and drain structures. The results showed that there exists an optimum range of implantation doses where the device has a significantly higher figure-of-merit regarding speed and voltage capability, compared to a symmetric MOSFET. Furthermore, both 2D and 3D simulations were used to develop a resistive model of the substrate noise coupling. </p><p>Of particular interest to this thesis is the random dopant fluctuation (RDF). The result of RDF can be characterized using very advance and reliable measurement techniques. In the thesis an ultra-high precision parametric mismatch measurement system was designed and implemented. The best ever reported performance on short-term repeatability of the measurements was demonstrated. A new bipolar parametric mismatch phenomenon was also revealed using the measurement system.</p><p>A complete simulation platform, called SiSPET (Simulated Statistical Parameter Extraction Tool), was developed and integrated into the framework of a commercial TCAD environment. A special program for randomization of the doping was developed and proven to provide RDF effects in agreement measurement. The SiSPET system was used to investigate how different device models were able to take RDF effects into account. The RDF effects were translated in to parameter fluctuations using the developed extraction routines. It was shown that the basic MOSFET fluctuation model could be improved by including the field dependenent mobility. However, if a precise description of the fluctuations is required an advanced compact-model, such as MOS Model 11 should be used.</p>

Today, micro- and nano-electronic devices are becoming more complex and advanced as the dimensions are shrinking. It is therefore a very challenging task to develop new device technologies with performance that can be predicted. This thesis focuses on advanced measurement techniques and TCAD simulations in order to characterize and understand the device physics of advanced semiconductor devices. TCAD simulations were made on a novel MOSFET device with asymmetric source and drain structures. The results showed that there exists an optimum range of implantation doses where the device has a significantly higher figure-of-merit regarding speed and voltage capability, compared to a symmetric MOSFET. Furthermore, both 2D and 3D simulations were used to develop a resistive model of the substrate noise coupling. Of particular interest to this thesis is the random dopant fluctuation (RDF). The result of RDF can be characterized using very advance and reliable measurement techniques. In the thesis an ultra-high precision parametric mismatch measurement system was designed and implemented. The best ever reported performance on short-term repeatability of the measurements was demonstrated. A new bipolar parametric mismatch phenomenon was also revealed using the measurement system. A complete simulation platform, called SiSPET (Simulated Statistical Parameter Extraction Tool), was developed and integrated into the framework of a commercial TCAD environment. A special program for randomization of the doping was developed and proven to provide RDF effects in agreement measurement. The SiSPET system was used to investigate how different device models were able to take RDF effects into account. The RDF effects were translated in to parameter fluctuations using the developed extraction routines. It was shown that the basic MOSFET fluctuation model could be improved by including the field dependenent mobility. However, if a precise description of the fluctuations is required an advanced compact-model, such as MOS Model 11 should be used.

Transdermal drug delivery systems (TDS) are pharmaceutical devices that are designed to deliver specific drugs to the human body by diffusion through skin. The TDS effectiveness suffers from crystallization in the patch when they are kept in storage for more than two years. It has been reported that there are two types of crystals in the patch: needle and aggregate, and growth of drug crystals in TDS generally occurs only in the middle third of the polymer layer. In our study, fluorescence microscopy, EDS (SEM) and Raman microspectroscopy were used to further characterize the crystals. The results show that the needle crystals most probably contain estradiol and acrylic resin conjugate. The FTIR spectrum of the model sample proved the occurrence of a reaction between estradiol and acrylic resin. Crystal growth in an unstressed matrix of a dissolved crystallizable drug component was simulated using a kinetic Monte Carlo model. Simulation using Potts model with proper boundary condition gives the crystals in the middle of matrix in the higher temperature. Bond fluctuation model is also being implemented to study representative dense TDS polymer matrix. This model can account for the size effect of polymer chain on the crystal growth. The drug release profile from TDS was also studied by simulating the diffusion of drug molecules using Monte Carlo techniques for different initial TDS microstructure. The release rate and profile of TDS depend on the dissolution process of the crystal. At low storage temperature, the grains are evenly distributed throughout the thickness of the TDS patch, thus the release rate and profile is similar to the randomly initiated system. Further work on stress induced crystallization is currently under development. Although the study was specifically done for drug in a polymer medium, the techniques developed in this investigation is in general applicable to any constrained crystallization in a polymer medium.

An in-depth knowledge of the structure formation process and the resulting dependency of the morphology on the reaction mechanism is a key requirement in order to design application-oriented materials. For twin polymerization, the basic idea of the reaction process is established, and important structural properties of the final nanoporous hybrid materials are known. However, the effects of changing the reaction mechanism parameters on the final morphology is still an open issue. In this work, the dependence of the morphology on the reaction mechanism is investigated based on a previously introduced lattice-based Monte Carlo method, the reactive bond fluctuation model. We analyze the effects of the model parameters, such as movability, attraction, or reaction probabilities on structural properties, like the specific surface area, the radial distribution function, the local porosity distribution, or the total fraction of percolating elements. From these examinations, we can identify key factors to adapt structural properties to fulfill desired requirements for possible applications. Hereby, we point out which implications theses parameter changes have on the underlying chemical structure.

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