Low-K CMP process control by using interpolation method
For ILD CMP process, accurate polish time control is required to obtain expected film thickness. Comparisons between conventional SiO2 CMP process and low-k CMP process were studied and realized polishing time for low-k material can not be determined by only removal rate due to the existence of incubation time at first stage. Interpolation method was evaluated to determine polishing time for low-k CMP process. Correction of this incubation time was performed by using interpolation method and promising results were obtained.
Multiple objective APC application for an oxide CMP process in a high volume production environment
Presents three independent run-to-run APC controllers for the chemical-mechanical polishing (CMP). The controllers are applied to oxide polish processes in AMD's Fab30 to improve (a) the lot-to-lot variation, (b) to reduce the wafer-to-wafer variation and (c) to increase the wafer uniformity of the post-polish oxide thickness. Since different products and layers are processed on the same tools a method was introduced to compensate device and layer dependencies. The control algorithms were extended to a bi-layered polish process. Significant improvement was achieved for the individual controllers applied in the high volume production environment of Fab30 under the condition of a permanently changing product mix.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.