Paper
27 December 2002 Compensation of long-range process effects on photomasks by design data correction
Author Affiliations +
Abstract
CD requirements for advanced photomasks are getting very demanding for the 100 nm-node and below; the ITRS roadmap requires CD uniformities below 10 nm for the most critical layers. To reach this goal, statistical as well as systematic CD contributions must be minimized. Here, we focus on the reduction of systematic CD variations across the masks that may be caused by process effects, e.g. dry etch loading. We address this topic by compensating such effects via design data correction analogous to proximity correction. Dry etch loading is modeled by gaussian convolution of pattern densities. Data correction is done geometrically by edge shifting. As the effect amplitude has an order of magnitude of 10 nm this can only be done on e-beam writers with small address grids to reduce big CD steps in the design data. We present modeling and correction results for special mask patterns with very strong pattern density variations showing that the compensation method is able to reduce CD uniformity by 50-70% depending on pattern details. The data correction itself is done with a new module developed especially to compensate long-range effects and fits nicely into the common data flow environment.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jens Schneider, Martin Bloecker, Gerd Ballhorn, Nikola Belic, Hans Eisenmann, and Danny Keogan "Compensation of long-range process effects on photomasks by design data correction", Proc. SPIE 4889, 22nd Annual BACUS Symposium on Photomask Technology, (27 December 2002); https://doi.org/10.1117/12.467572
Lens.org Logo
CITATIONS
Cited by 10 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Critical dimension metrology

Etching

Data corrections

Photomasks

Data modeling

Dry etching

Convolution

Back to Top