REACTIVE ION ETCHING PROFILE AND DEPTH CHARACTERIZATION USING STATISTICAL AND NEURAL-NETWORK ANALYSIS OF LIGHT-SCATTERING DATA

As device design rules continue to shrink for the manufacturing of int egrated circuits, unprecedented challenges for process inspection appe ar. No longer is optical microscopy adequate for determining if proces s results meet specifications. On the other hand, the alternative-scan ning electron microscopy-is time consuming, destructive, and costly. A nother approach is to measure scattered light intensity as a function of scattering angle, as opposed to imaging, to obtain distinct signatu res for submicron structures. In this work, a set of Si wafers with ph otolithographically defined lines and spaces are reactively ion etched . By varying process conditions, a range of depths and sidewall profil es is generated and then inspected by detecting visible scattered lase r light over 180-degrees. The resultant scattergrams are then analyzed both by using discriminant analysis and by training a neural network to catalog the microstructures according to depth and profile. We find that this approach is a viable alternative to destructive sampling an d off-line inspection by scanning electron microscopy: depth and profi le are correctly classified with better than 95% accuracy.