Multiple regression analysis of postdevelop unbiased line width roughness and etch resistance for high-accuracy estimations of postetch pattern roughness

As device scaling continues, development of photoresists with low pattern roughness and high sensitivity has become challenging. One obstacle that delays the material development process is the discrepancy between postdevelop and postetch pattern roughness, where a photoresist with high postdevelop patterning performance shows poor postetch pattern roughness. Herein, we demonstrate that pattern roughness after a nontrim etch process can be accurately estimated by a multiple regression analysis of a power spectral density (PSD) variable of postdevelop roughness and an etch resistance parameter of resins. The nontrim etch process here refers to an etch condition which leads to increased pattern roughness. Unbiased line width roughness (LWR) shows the highest correlation with postetch LWR among postdevelop roughness PSD variables. An etch resistance parameter also correlates well with postetch roughness. A multiple regression analysis reveals that the contributions of postdevelop unbiased LWR and etch resistance to the postetch LWR are 59% and 41%, respectively. Based on the calculated contributions, postetch LWR is estimated with a high accuracy (R2 > 0.93). This estimation method allows for an efficient material screening at a lithography level without assessing postetch patterning performance, thus the process of material development could be accelerated.