14 March 2012 Application of stochastic modeling to resist optimization problems
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Abstract
BACKGROUND: Modifying specific resist properties or isolating a particular resist response can be difficult or impossible in experiments. At EUV, tool time is limited and expensive, complicating access to experimental data. Computer modeling can help to mitigate these difficulties, allowing researchers to reduce or better focus the nature of actual experiments. METHODS: We apply stochastic simulation to the study of chemically-amplified resists at EUV. The model is calibrated to experimental data; the agreement between data and simulation are compared using RLS triangles. Using the calibrated model as a representation of the initial condition, we attempt to improve virtual resist performance by decreasing acid diffusivity rate, increasing quencher loading and by replacing conventional quencher with photo-decomposable base (PDB). The effect of PDB upon the virtual resist is further investigated. RESULTS: Virtual resist performance improved by lowering acid diffusivity, by increasing quencher loading and by replacing conventional quencher with photo-decomposable base (PDB). The net improvements observed are a 17% increase in EL and a 13% reduction in LER compared to the initial condition. PDB may offer a path to reduce resist roughness up to 20%, by allowing higher loading density than conventional quenchers and relaxing the acidic quantum yield required to achieve acceptable roughness. Using the simulator to isolate a specific response, PDB acts to improve the chemical contrast and reduce the chemical noise in the blocked polymer concentration after PEB.
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John J. Biafore, Mark D. Smith, "Application of stochastic modeling to resist optimization problems", Proc. SPIE 8325, Advances in Resist Materials and Processing Technology XXIX, 83250H (14 March 2012); doi: 10.1117/12.916518; https://doi.org/10.1117/12.916518
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