Study on defect reduction for high aspect ratio etch process
Increasing data needs much larger memory capacity. One of the Flash Memory Solution is Vertical NAND (V-NAND) Flash Memory. In order to fabricate this device, a high aspect ratio hole must be made in the channel hole through the etching process after multilayer thin film deposition. In order to obtain high aspect ratio etch, process condition should have high RF power, high flow rate of polymer gas, and long process time. This harsh etch condition results in high ion energy and lots of polymer production. It can create defects like fluorocarbon polymer and arcing. In this study, we studied arcing mechanism. Here we defined various margins for arcing-free and optimized the focus ring. And we improved pumping conductance for polymer discharge. This study removed defects and obtained arcing-free during V-NAND etch process.
Study on Wafer De-Chuck for High Aspect Ratio Etch Process
Higher aspect ratio feature etch is required with the increase of the layer count in 3D NAND technology. The arcing frequency in high aspect ratio etch process increases because charge accumulates due to the high power and more polymer deposition in the process. Thus, it is essential for the etching tool configure optimization to avoid arcing occurrence. In this study, we found that the helium detection for De-chuck charge release has high risk of arcing, while the soft-lifted pin charge release detection has wider window in avoiding the arcing happening. To acquire additional margin, the De-chuck parameters are optimized for healthier mass production.
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.