Piezoresistive cantilevers with an integrated bimorph actuator

Scanning Probe Microscopy (SPM) has become of fundamental importance for research in area of micro and nano-technology. The continuous progress in these fields requires ultra sensitive measurements at high speed. The imaging speed limitation of the conventional Tapping Mode SPM is due to the actuation time constant of piezotube feedback loop that keeps the tapping amplitude constant. In order to avoid this limit a deflection sensor and an actuator have to be integrated into the cantilever. In this work has been demonstrated the possibility of realisation of piezoresistive cantilever with an embedded actuator. Piezoresistive detection provides a good alternative to the usual optical laser beam deflection technique. In frames of this thesis has been investigated and modelled the piezoresistive effect in bulk silicon (3D case) for both n- and p-type silicon. Moving towards ultra-sensitive measurements it is necessary to realize ultra-thin piezoresistors, which are well localized to the surface, where the stress magnitude is maximal. New physical effects such as quantum confinement which arise due to the scaling of the piezoresistor thickness was taken into account in order to model the piezoresistive effect and its modification in case of ultra-thin piezoresistor (2D case). The two-dimension character of the electron gas in n-type piezoresistors lead up to decreasing of the piezoresistive coefficients with increasing the degree of electron localisation. Moreover for p-type piezoresistors the predicted values of the piezoresistive coefficients are higher in case of localised holes. Additionally, to the integration of the piezoresistive sensor, actuator integrated into the cantilever is considered as fundamental for realisation of fast SPM imaging. Actuation of the beam is achieved thermally by relying on differences in the coefficients of thermal expansion between aluminum and silicon. In addition the aluminum layer forms the heating micro-resistor, which is able to accept heating impulses with frequency up to one megahertz. Such direct oscillating thermally driven bimorph actuator was studied also with respect to the bimorph actuator efficiency. Higher eigenmodes of the cantilever are used in order to increase the operating frequencies. As a result the scanning speed has been increased due to the decreasing of the actuation time constant. The fundamental limits to force sensitivity that are imposed by piezoresistive deflection sensing technique have been discussed. For imaging in ambient conditions the force sensitivity is limited by the thermo-mechanical cantilever noise. Additional noise sources, connected with the piezoresistive detection are negligible.

Statistical evaluation of texture analysis from the biocrystallization method

The consumers are becoming more concerned about food quality, especially regarding how, when and where the foods are produced (Haglund et al., 1999; Kahl et al., 2004; Alföldi, et al., 2006). Therefore, during recent years there has been a growing interest in the methods for food quality assessment, especially in the picture-development methods as a complement to traditional chemical analysis of single compounds (Kahl et al., 2006). The biocrystallization as one of the picture-developing method is based on the crystallographic phenomenon that when crystallizing aqueous solutions of dihydrate CuCl2 with adding of organic solutions, originating, e.g., from crop samples, biocrystallograms are generated with reproducible crystal patterns (Kleber & Steinike-Hartung, 1959). Its output is a crystal pattern on glass plates from which different variables (numbers) can be calculated by using image analysis. However, there is a lack of a standardized evaluation method to quantify the morphological features of the biocrystallogram image. Therefore, the main sakes of this research are (1) to optimize an existing statistical model in order to describe all the effects that contribute to the experiment, (2) to investigate the effect of image parameters on the texture analysis of the biocrystallogram images, i.e., region of interest (ROI), color transformation and histogram matching on samples from the project 020E170/F financed by the Federal Ministry of Food, Agriculture and Consumer Protection(BMELV).The samples are wheat and carrots from controlled field and farm trials, (3) to consider the strongest effect of texture parameter with the visual evaluation criteria that have been developed by a group of researcher (University of Kassel, Germany; Louis Bolk Institute (LBI), Netherlands and Biodynamic Research Association Denmark (BRAD), Denmark) in order to clarify how the relation of the texture parameter and visual characteristics on an image is. The refined statistical model was accomplished by using a lme model with repeated measurements via crossed effects, programmed in R (version 2.1.0). The validity of the F and P values is checked against the SAS program. While getting from the ANOVA the same F values, the P values are bigger in R because of the more conservative approach. The refined model is calculating more significant P values. The optimization of the image analysis is dealing with the following parameters: ROI(Region of Interest which is the area around the geometrical center), color transformation (calculation of the 1 dimensional gray level value out of the three dimensional color information of the scanned picture, which is necessary for the texture analysis), histogram matching (normalization of the histogram of the picture to enhance the contrast and to minimize the errors from lighting conditions). The samples were wheat from DOC trial with 4 field replicates for the years 2003 and 2005, “market samples”(organic and conventional neighbors with the same variety) for 2004 and 2005, carrot where the samples were obtained from the University of Kassel (2 varieties, 2 nitrogen treatments) for the years 2004, 2005, 2006 and “market samples” of carrot for the years 2004 and 2005. The criterion for the optimization was repeatability of the differentiation of the samples over the different harvest(years). For different samples different ROIs were found, which reflect the different pictures. The best color transformation that shows efficiently differentiation is relied on gray scale, i.e., equal color transformation. The second dimension of the color transformation only appeared in some years for the effect of color wavelength(hue) for carrot treated with different nitrate fertilizer levels. The best histogram matching is the Gaussian distribution. The approach was to find a connection between the variables from textural image analysis with the different visual criteria. The relation between the texture parameters and visual evaluation criteria was limited to the carrot samples, especially, as it could be well differentiated by the texture analysis. It was possible to connect groups of variables of the texture analysis with groups of criteria from the visual evaluation. These selected variables were able to differentiate the samples but not able to classify the samples according to the treatment. Contrarily, in case of visual criteria which describe the picture as a whole there is a classification in 80% of the sample cases possible. Herewith, it clearly can find the limits of the single variable approach of the image analysis (texture analysis).