Novel methodology for postexposure bake calibration and optimization basedon electrical linewidth measurement and process metamodeling

By combining electrical linewidth measurements and neural-network (NN) proc ess metamodeling, lithography simulators can be calibrated in an efficient way. In this work we present a novel methodology for characterizing postexp osure bake using a very large experimental data set, so that the calibrated model can be used as a truly predictive tool. The adoption of a special te st reticle mask allowed us to collect more than 700000 critical dimensions CDs from 24 silicon wafers for a matrix of postexposure bake (PEB) time, an d temperature conditions. The Lithographic patterns included isolated, semi dense and dense lines for structures of 0.25, 0.20, 0.175, and 0.15 mu m no minal size replicated across the exposure field and across the wafer. As a result of this particular metrology, each measured CD was associated with b oth topological (position on the wafer and position within the field) and p rocess information (exposure dose, FEB time, and temperature). Database man agement techniques were implemented in order to extract and analyze such a massive data set. Process metamodeling (PMM) was used for the calibration o f a FEB model describing the joint effect of photoacid diffusion and photoa cid loss, coupled with a deprotection reaction. PMM creates a NN model of t he FEB original model (a "model of a model") so that the diffusion coeffici ent, the acid loss, and the deprotection rates can be estimated by inversio n of the NN mapping. The comparisons between experimental and simulated dat a show excellent agreement that is maintained across the entire process spa ce. (C) 1998 American Vacuum Society. [S0734-211X(98)03206-5].