D. Maier

A unifying view on some experimental effects in tapping-mode atomic force microscopy

Several experimental effects that occur in tapping-mode atomic force microscopy are examined: apparent hysteresis effects in force probes and frequency sweeps and distortions in imaging. It is found through examinations of the phase space that they can all be reduced to one common cause: the existence of more than one stable state of the tip vibration for certain parameters. It is shown that the experimental effects can be explained considering only the phase space under the assumption that measurement noise exists. Numerical simulations confirm the theoretical and experimental findings.

Investigation of compensation defects in CdTe:Cl samples grown by different techniques

A detailed analysis of the photoinduced current transients of differently grown CdTe:Cl samples was performed in the 100–140 K range in order to investigate the influence of different growth techniques (sublimation, Bridgman method, and traveling heater method) on compensation defects. While studying the experimental results the analysis of the transients turned out to be a crucial point. With the conventional two‐gate technique only one trap with misleading trap parameters could be identified in each sample. Analyzing the transients with the regularization method proposed recently [C. Eiche, D. Maier, M. Schneider, D. Sinerius, J. Weese, K. W. Benz, and J. Honerkamp, J. Phys. Condens. Matter 4, 6131 (1992)], three traps could be identified in each sample. Only one of these traps leads to an activation energy and a cross section approximately the same for the different samples. The other two traps of each sample depend on the growth technique.

High resolution method for the analysis of admittance spectroscopy data

There are several experimental methods which give information about the thermal relaxation times of the deep levels in a semiconductor. Analyzing the temperature dependence of the relaxation times, the activation energy and the cross section of the corresponding deep levels can be determined. An essential problem of such methods is the identification of the relaxation times in the measured signal. In the context of time‐dependent measurements such as photoinduced current transient spectroscopy and deep level transient spectroscopy, Tikhonov regularization was recently proposed as a high resolution method for this purpose. In this contribution it is proposed to apply Tikhonov regularization in order to identify the thermal relaxation times in admittance spectroscopy data. The method is tested and discussed using simulated data. Finally, admittance spectroscopy data of a GaAs diode are analyzed. The results demonstrate that the resolution of an ordinary admittance spectroscopy setup can considerably be impr...

AN IMPROVED ANALYSIS OF ADMITTANCE DATA FOR HIGH RESISTIVITY MATERIALS BY A NONLINEAR REGULARIZATION METHOD

Admittance data of GaAs Schottky diodes are analyzed in order to calculate the activation energy and the cross section of deep levels. An improved model for high resistivity semiconductor material is introduced. In this model the admittance depends nonlinearly on the distribution of the relaxation times of the deep levels and on material parameters like the resistivity, the barrier height, and the band gap of the semiconductor. The reconstruction of a continuous distribution and of the parameters from experimental admittance data is an ill-posed inverse problem which is solved by a nonlinear regularization procedure. The method is used to analyze the deep levels and the semiconducting properties of a semi-insulating GaAs diode. Values for the resistivity, the barrier height, and the band gap could be confirmed and deep levels with activation energy in the range of 0.5–0.72 eV were determined to be electrical active.

Optimization of surface acoustic wave sensor arrays and application to high performance liquid chromatography

Abstract Some issues regarding the optimization of sensor arrays are discussed. The criteria considered here are based on Lorbers figures of merit and nonlinear generalizations thereof. Especially, it is discussed how the averaging over the analyte space should be performed for nonlinear data and what optimization criterion should be used. A genetic algorithm is used as optimization algorithm. These considerations are used for sensor selection and sensor reduction of surface acoustic wave sensor devices applied in preparative process high performance liquid chromatography.

Influence of correlated errors on the estimation of the relaxation time spectrum in dynamic light scattering

An important step in analyzing data from dynamic light scattering is estimating the relaxation time spectrum from the correlation time function. This estimation is frequently done by regularization methods. To obtain good results with this step, the statistical errors of the correlation time function must be taken into account [J. Phys. A 6, 1897 (1973)]. So far error models assuming independent statistical errors have been used in the estimation. We show that results for the relaxation time spectrum are better if correlation between statistical errors is taken into account. There are two possible ways to obtain the error sizes and their correlations. On the one hand, they can be calculated from the correlation time function by use of a model derived by Schätzel. On the other hand, they can be computed directly from the time series of the scattered light. Simulations demonstrate that the best results are obtained with the latter method. This method requires, however, storing the time series of the scattered light during the experiment. Therefore a modified experimental setup is needed. Nevertheless the simulations also show improvement in the resulting relaxation time spectra if the error model of Schätzel is used. This improvement is confirmed when a lattice with a bimodal sphere size distribution is applied to experimental data.

Early transition detection-A dynamic extension to common classification methods

Abstract An extension for classification methods in order to process time-dependent data is introduced. It is based on the detection of transitions from one steady state to another one by examination of the time derivatives of classification vectors. The method is called Early Transition Detection (ETD). It is shown that it can be used in conjunction with a number of common classification methods like SIMCA or Artificial Neural Nets and it is successfully tested on simulated and on real data.

Two improvements of early transition detection

Two improvements of early transition detection (ETD) are presented. One improvement allows for transitions to be detected if they occur on very different timescales. The other extends ETD to be applicable together with unsupervised classification methods. Both improvements are successfully tested on data obtained from sensor array measurements. Copyright

Using properties of random matrices for target factor analysis of sensor array data

Target factor analysis is an important issue in the analysis of sensor array data as it allows one to test whether measurements contain only the substances with which a chemical sensor system was calibrated. In this paper a new approach based on the properties of random matrices is presented. The problem is first transformed to a pseudorank estimation problem by forming a combined calibration – prediction data matrix. Then the largest eigenvalue of the estimated measurement error matrix of this matrix is compared with maximum values obtained from pure random matrices. The test is statistically exact and especially useful for sensor array data. The largest eigenvalue test is compared with Malinowski’s F‐test on simulated data and tested on real data from chemical sensor arrays.