Spectral analysis of line edge and line-width roughness with long-range correlation

Abstract

Large-scale integrations (LSIs) are facing an ever-growing problem of device variability. One of the origins that cause the variability is line-width roughness (LWR) caused by line edge roughness (LER). Accurate characterization of the LWR plays an essential role in controlling the LWR. To do this, we report a methodology, named the “assembly method,” that enables to analyze LWR statistics beyond the conventional correlation length limit, basing on the previous “patchwork” method and recent discrete power spectral density (PSD) method. The methodology virtually assembles a long line by gathering line segments that are randomly scattered on a single line or equally processed different lines. The virtual lines are repeatedly assembled by randomly changing the combination of the segments and the order of the gathered segments while permitting overlaps of the segments between the assembled lines. Squared Fourier transforms of their widths are averaged over the assembled lines to obtain the PSD. By these steps, the statistical noise, which is inherent to experimental PSDs, is markedly reduced. Furthermore, to extract LWR statistics by comparing experimental and theoretical PSDs, we derived an analytic formula of the assembled-line PSD. In the derivation, the randomness of the segment collections played a key role. The PSDs calculated using the formula almost completely fitted experimental PSDs that were obtained by the assembly method. The parameters used in the best-fitted calculation revealed that the photoresist LWR of this study contained a component that had a correlation length of 2780 nm in addition to the previously reported LWR of 35 nm. The LWR variance of the component accounted for approximately 10% of the total variance. The formula also enabled us to evaluate the accuracy of experimentally obtained averages of widths. We find two distinct features in the PSDs by the assembly method. One is the oscillatory structure that shows up in the case when the correlation length is larger than half the length of the segments. A trace of this structure was actually observed in the experimental PSDs of this study. The other is the spikes that are periodically observed as a function of wave number. The spikes originate from a nonstochastic width variation that exists in all the segments in common. Their intensity is proportional to the number of gathered segments in the assembled lines. Because the spikes are excluded from the analysis, the LWR parameters determined by the assembly method are not affected by the nonstochastic variation, unlike the conventional methods. By all these results, we confirm that the assembly method of this study extends the upper limit of analyzable correlation lengths by a factor of approximately 20 and enhances the accuracy as well. This feature also has a practical significance that the widely observed LWR with a correlation length of approximately 35 nm can be analyzed by the assembly method using a conventional critical-dimension scanning-electron-microscope, without resorting to a specially designed one. Accordingly, the method will be a key tool for investigating LER and LWR in developing and manufacturing LSIs. It will also help analyze other stochastic processes in many research and development settings.