R. Krukar et al., REACTIVE ION ETCHING PROFILE AND DEPTH CHARACTERIZATION USING STATISTICAL AND NEURAL-NETWORK ANALYSIS OF LIGHT-SCATTERING DATA, Journal of applied physics, 74(6), 1993, pp. 3698-3706
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.