1 April 2006 Line edge roughness and intrinsic bias for two methacrylate polymer resist systems
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Abstract
Line edge roughness (LER) and intrinsic bias of 193-nm photoresist based on two methacrylate polymers are evaluated over a range of base concentration. Roughness is characterized as a function of the image log slope of the aerial image, the gradient in photoacid concentration, and the gradient in polymer protecting groups. Use of the polymer protection gradient as a characteristic roughness metric accounts for the effects of base concentration. Results demonstrate that a methacrylate terpolymer exhibits an advantage over the copolymer resist by achieving lower roughness at smaller values for the polymer protection gradient, resulting in lower LER for patterning. Intrinsic bias is found to be a function of the concentration of base. Process window analysis demonstrates that a greater depth of focus can be achieved for resists with low intrinsic bias. However, a tradeoff in depth of focus with LER is found. Spectral analysis indicates resists with greater intrinsic bias exhibit greater correlation lengths. Systems with greater intrinsic bias demonstrate lesser roughness for patterned features, with a minimum roughness achieved at maximum intrinsic bias. Kinetics of deprotection are modeled to calculate the chemical contrast of each resist. Resists exhibiting the greatest chemical contrast are identified as materials that generate the least roughness.
©(2006) Society of Photo-Optical Instrumentation Engineers (SPIE)
Adam R. Pawloski, Alden Acheta, Harry J. Levinson, Timothy B. Michaelson, Andrew Jamieson, Yukio Nishimura, and Carlton Grant Willson "Line edge roughness and intrinsic bias for two methacrylate polymer resist systems," Journal of Micro/Nanolithography, MEMS, and MOEMS 5(2), 023001 (1 April 2006). https://doi.org/10.1117/1.2200675
Published: 1 April 2006
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CITATIONS
Cited by 27 scholarly publications.
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KEYWORDS
Line edge roughness

Polymers

Diffusion

Data modeling

Image quality

Optical lithography

Systems modeling

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