Comparative stochastic process variation bands for N7, N5, and N3 at EUV

Stochastics effects are the ultimate limiter of optical lithography technology and are a major concern for next-generation technology nodes in EUV lithography. Following up on work published last year, we compare the performance of organic chemically-amplified and condensed metal-oxide resists exposed at different sizing doses using a proxy 2D SRAM layout. For each combination of material, technology node, and lithographic approach, we perform 550,000 physics based Monte-Carlo simulations of the SRAM cell. We look at many performance data, including stochastic process variation bands at fixed, nominal conditions assuming no variation in process parameters vs. the stochastic process variation bands obtained by inclusion of process parameters. Perturbations are applied to exposure dose, focus, chief-ray azimuthal angle, mask CD, stack thicknesses, and PEB temperature. We study stochastic responses for three technology nodes: • An SRAM cell for 7 nm technology node, with Numerical Aperture = 0.33 and patterned with organic chemically amplified resist • An SRAM cell for 5 nm technology node, with Numerical Aperture = 0.33 and patterned with: o Organic chemically amplified resist o Fast photospeed organic chemically amplified resist o Metal-oxide resist • An SRAM cell for 3 nm technology node, patterned with organic chemically amplified resist and: o Numerical Aperture = 0.33 in single exposure o Numerical Aperture = 0.33 with double exposure o Numerical Aperture = 0.55 with anamorphic pupil For each case, we optimize mask bias, source illumination and process conditions across focus to maximize the optical contrast. We did not apply optical proximity correction to the mask. The purpose of the work is to evaluate the stochastic behavior of different features as a function of material strategy, technology node, and lithographic approach.

[1]  E. Hendrickx,et al.  Stochastic effects in EUV lithography , 2018, Advanced Lithography.

[2]  Alexander M. Millkey The Black Swan: The Impact of the Highly Improbable , 2009 .

[3]  Andreas Frommhold,et al.  Dynamic absorption coefficients of CAR and non-CAR resists at EUV , 2016, SPIE Advanced Lithography.

[4]  Geert Vandenberghe,et al.  Metal oxide EUV photoresist performance for N7 relevant patterns and processes , 2016, SPIE Advanced Lithography.

[5]  Lei Sun,et al.  Line-edge roughness performance targets for EUV lithography , 2017, Advanced Lithography.

[6]  Marie Krysak,et al.  Lithographic stochastics: beyond 3σ , 2017, Advanced Lithography.

[7]  Geert Vandenberghe,et al.  Characterizing and modeling electrical response to light for metal-based EUV photoresists , 2016, SPIE Advanced Lithography.

[8]  Peter De Bisschop Stochastic effects in EUV lithography: random, local CD variability, and printing failures , 2017 .

[9]  Alessandro Vaglio Pret,et al.  Modeling and simulation of low-energy electron scattering in organic and inorganic EUV photoresists , 2017, Advanced Lithography.

[10]  Alessandro Vaglio Pret,et al.  XAS photoresists electron/quantum yields study with synchrotron light , 2015, Advanced Lithography.

[11]  T. Wallow,et al.  Deconstructing contact hole CD printing variability in EUV lithography , 2014, Advanced Lithography.

[12]  David Blankenship,et al.  Resist pattern prediction at EUV , 2010, Advanced Lithography.

[13]  Ming Mao,et al.  Design and pitch scaling for affordable node transition and EUV insertion scenario , 2017, Advanced Lithography.

[14]  Yunfei Deng,et al.  Pattern prediction in EUV resists , 2009, Lithography Asia.

[15]  Sascha Migura,et al.  EUV lithography scanner for sub-8nm resolution , 2015, Advanced Lithography.

[16]  A. Vaglio Pret,et al.  Impact of stochastic effects on EUV printability limits , 2014, Advanced Lithography.