Roughness determines many functional properties of surfaces, such as adhesion, friction, and (thermal and electrical) contact conductance. Recent analytical models and simulations enable quantitative prediction of these properties from knowledge of the power spectral density (PSD) of the surface topography. The utility of the PSD is that it contains statistical information that is unbiased by the particular scan size and pixel resolution chosen by the researcher. In this article, we first review the mathematical definition of the PSD, including the one- and two-dimensional cases, and common variations of each. We then discuss strategies for reconstructing an accurate PSD of a surface using topography measurements at different size scales. Finally, we discuss detecting and mitigating artifacts at the smallest scales, and computing upper/lower bounds on functional properties obtained from models. We accompany our discussion with virtual measurements on computer-generated surfaces. This discussion summarizes how to analyze topography measurements to reconstruct a reliable PSD. Analytical models demonstrate the potential for tuning functional properties by rationally tailoring surface topography - however, this potential can only be achieved through the accurate, quantitative reconstruction of the power spectral density of real-world surfaces.

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Quantitative characterization of surface topography using spectral analysis

Roughness determines many functional properties of surfaces, such as adhesion, friction, and (thermal and electrical) contact conductance. Recent analytical models and simulations enable quantitative prediction of these properties from knowledge of the power spectral density (PSD) of the surface topography. The utility of the PSD is that it contains statistical information that is unbiased by the particular scan size and pixel resolution chosen by the researcher. In this article, we first review the mathematical definition of the PSD, including the one- and two-dimensional cases, and common variations of each. We then discuss strategies for reconstructing an accurate PSD of a surface using topography measurements at different size scales. Finally, we discuss detecting and mitigating artifacts at the smallest scales, and computing upper/lower bounds on functional properties obtained from models. We accompany our discussion with virtual measurements on computer-generated surfaces. This discussion summarizes how to analyze topography measurements to reconstruct a reliable PSD. Analytical models demonstrate the potential for tuning functional properties by rationally tailoring surface topography—however, this potential can only be achieved through the accurate, quantitative reconstruction of the PSDs of real-world surfaces.

/pdf/quantitative-characterization-of-surface-topography-using-45kh8tzr33.pdf

Various statistical quantities (such as average, peak-to-valley, and root-mean-square roughness) have been applied to characterize surface topography. However, they provide only vertical information. Because spectral analysis provides both lateral and longitudinal information, it is a more informative measurement than all these commonly used statistical quantities. Unfortunately, a standard method to calculate power spectral density (PSD) is not available. For example, the dimensions of PSD are often denoted as either (length)3 or (length)4. This may lead to confusion when utilizing spectral analysis to study surface morphology. In this paper, we will first compare the definitions of PSD commonly used by various authors. Using silicon surface roughness measurements as examples, we will demonstrate the advantages of spectral methods on atomic force microscopic (AFM) image analysis. In this context, we study the effects of typical AFM imaging distortions such as image bow, drift, tip-shape effects, and acou...

Analyzing atomic force microscopy images using spectral methods

Oxide-semiconductor-based thin-film transistors (TFTs), particularly the amorphous ones, are becoming an important emerging technology. Since oxide semiconductors easily form polycrystalline phases, usually more complicated oxide mixtures are needed for growing amorphous phases. In this letter, we report that by simply reducing the thickness, ZnO can be intentionally grown into the amorphous phase. Furthermore, both top-gate and bottom-gate amorphous ZnO TFTs of micrometer scales were effectively implemented using fully lithographic and etching processes. Rather high field-effect mobilities of 25 and 4cm2∕Vs and on∕off current ratios of >107 and >106 were achieved for top-gate and bottom-gate configurations, respectively.

Amorphous ZnO transparent thin-film transistors fabricated by fully lithographic and etching processes

Latex dispersions having a well-controlled, bimodal particle size distribution are gaining attention because they potentially enable control of the dispersion rheology, the film formation characteristics, and the final film properties. Here we study the film formation of dispersions with a bimodal particle size distribution (large:small size ratio of ca. 6:1) and with varying concentrations of the two particle sizes. We also compare the film formation of blends containing only deformable (i.e., “soft”) particles with blends containing both soft and nondeformable (i.e., “hard”) particles. We use ellipsometry as a noninvasive tool for studying film morphology as film formation proceeds. We interpret our ellipsometry data using a physical model of the morphology based on atomic force microscopy observation. Electron microscopy of film cross sections provides information about the bulk morphology. We measure void content and surface roughness in blend films as a function of the concentration of large particle...

Film Formation of Latex Blends with Bimodal Particle Size Distributions: Consideration of Particle Deformability and Continuity of the Dispersed Phase

The correlation between inversion layer mobility of MOSFET's and surface micro-roughness of the channel has been studied using split CV measurements and AFM analysis. The mobility at high normal field decreases with increasing the surface roughness over a wide range of roughness from 0.3 nm to 4.3 nm (RMS). The trend is the same even for very thin gate oxides down to 3 nm. Careful AFM measurements are used to show that the gate oxide thickness doesn't affect the surface roughness, supporting the independence of mobility on the gate oxide thickness.

Correlation between inversion layer mobility and surface roughness measured by AFM

Measurements of Si surface roughness by atomic force microscopy and ellipsometry have been performed over a wide range of conditions. Advanced methods of data analysis have been applied to both techniques leading to a quantitative comparison of root‐mean‐square (rms) roughness to the ellipsometric paramter Δ. Differences in Δ are observed for surfaces with the same rms roughness, but different roughness spectral densities, as expected from theory.

T. Yamanaka

Papers

Correlation between inversion layer mobility and surface roughness measured by AFM

Comparison of Si surface roughness measured by atomic force microscopy and ellipsometry