Combining tSPL and RIE for grayscale nanopattern amplification

© 2024 EPFL

© 2024 EPFL

Congratulations to Berke Erbas, PhD student at LMIS1, for his new publication entitled "Combining thermal scanning probe lithography and dry etching for grayscale nanopattern amplification" in the journal "Microsystems & Nanoengineering".

Grayscale structured surfaces with nanometer-scale features are used in a growing number of applications in optics and fluidics. Thermal scanning probe lithography achieves a lateral resolution below 10 nm and a vertical resolution below 1 nm, but its maximum depth in polymers is limited. Here, we present an innovative combination of nanowriting in thermal resist and plasma dry etching with substrate cooling, which achieves up to 10-fold amplification of polymer nanopatterns into SiO2 without proportionally increasing surface roughness. Sinusoidal nanopatterns in SiO2 with 400 nm pitch and 150 nm depth are fabricated free of shape distortion after dry etching. To exemplify the possible applications of the proposed method, grayscale dielectric nanostructures are used for scalable manufacturing through nanoimprint lithography and for strain nanoengineering of 2D materials. Such a method for aspect ratio amplification and smooth grayscale nanopatterning has the potential to find application in the fabrication of photonic and nanoelectronic devices.

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This work received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Project MEMS4.0, ERC-2016-ADG, grant agreement No. 742685) and the EU’s H2020 framework program for research and innovation under grant agreement n. 101007417, NFFA-Europe Pilot Project. M.B. acknowledges the support of SNSF Eccellenza grant No. PCEGP2_194528, and support from the QuantERA II Programme that has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 101017733. G.F. and M.P. received funding through the European research council H2020 - UE Framework Programme for Research & Innovation (2014-2020); ERC-2017-CoG; InCell; Project number 773091, and the Swiss National Science Foundation through grant 200021_182562.