Overlay and edge placement error metrology in the era of stochastics

Abstract

For the most advanced nodes, edge placement errors are typically dominated by stochastics, necessitating a rigorous stochastics approach to modeling and measuring edge placement errors and their contributors. In this work, a new approach to developing an edge placement error (EPE) model useful for lot dispositioning or EPE budgeting is presented. Approach: As an example of the proposed approach, a rigorous EPE model is developed for the case of complementary lithography, where dense lines and spaces are cut with a second patterning step. This model gives rise to the generation of an Overlay Process Window, the range of overlay errors that can be tolerated in the presence of stochastics critical dimension and placement errors of the individual layers. The resulting model uses only measurable quantities and allows the prediction of EPE-based failure rates for the purpose of lot dispositioning. One interesting outcome is that Angstrom-level changes in the 1-sigma stochastics terms produces nanometer-level changes in the overlay process window. This new EPE modeling approach provides a more rigorous and accurate method for lot dispositioning and EPE budgeting than prior approaches.

Modeling edge placement error performance of EUV and DSA multipatterning processes

“…where the exact error function expression has been approximated as an exponential for the case of low failure probability. 1 It is useful to note that this failure probability is only a function of the ratio 𝜎 𝐶𝐷 3 /𝜇 𝐶𝐷 3 .…”

“…One use for the calculated failure rate is to create an overlay process window (OPW). 1 The failure rate for both 𝐶𝐷 3 and 𝐶𝐷 4 are added together and then plotted as a function of 𝑂𝑉𝐿, the x-direction overlay error (Figure 4). By defining a maximum acceptable failure rate (for example, 1X10 -10 , or 0.1 ppb), the range of overlay that keeps the failure rate below this threshold can be determined and is called the OPW.…”

“…In previous work, an approach for rigorously incorporating stochastic variations into overlay and edge placement error (EPE) evaluations and yield predictions was developed. 1 The basic steps in the method are:…”