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 acoustic noise. As a result, we will provide a procedure to obtain accurate and reproducible AFM measurements.

To accurately determine the thickness of ultrathin SiO, films, the influence of roughness on ellipsometric measurements must be examined. Although ellipsometry has been applied to study roughness, quantitative relationship of Si surface roughness measured by ellipsometry and atomic force microscopy is not available. This leads to difficulties in estimating the influence of roughness on oxide thickness measurements. Here we first establish the correlation of roughness measured by these two techniques. Based on such a relationship, we can explain the discrepancy of oxide thickness measured by ellipsometry and surface sensitive techniques.

We evaluated the rate limiting steps in a post sulfuric acid/hydrogen peroxide mix dump rinsing process. The parameters we examined include, the pull-out velocity from the chemical bath, the draining during transport into the rinse tank, immersion into the rinse tank, dumping, draining after a dump cycle and flow rate during fills between dumps. Based on our investigations an optimized dump rinsing process was developed using programmed flow rates and dumping cycles. The quality of the optimized rinsing process was determined by measuring sulfur residue and light point defects on wafer surfaces that had undergone the optimized rinsing process. This optimized process requires 5 minutes and 75 liters of water, replacing a process that initially ran for up to 16 minutes and consumed 600 liters of water. Our optimized rinsing process, resulted in a factor of five saving in water consumption and two in cycle lifetime with no statistical degradation in surface quality.