Evidence map›Paper›PMID 42737157›Full record

ArticlePolymers2026

Nanoscale Roughness in Ultra-Thick Resists by Laser-Scanning Grayscale Direct-Write Lithography and Surface Smoothing.

Giulia Malvicini, Dogukan Güçtemur, Sina Saxer, Jan Erjawetz, Helmut Schift

Abstract read
In one paragraph

Article in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Giulia MalviciniLaboratory for Nano and Quantum Technologies, Paul Scherrer Institute (PSI), Forschungsstrasse 111, 5232 Villigen, Switzerland.ORCID 0009-0000-2169-9867
Dogukan GüçtemurnanoLab, FHNW School of Life Sciences, Hofackerstrasse 30, 4132 Muttenz, Switzerland.
Sina SaxernanoLab, FHNW School of Life Sciences, Hofackerstrasse 30, 4132 Muttenz, Switzerland.ORCID 0000-0002-7101-0623
Jan ErjawetzXRnanotech, Switzerland Innovation Park Innovaare, Parkstrasse 1, 5234 Villigen, Switzerland.
Helmut SchiftLaboratory for Nano and Quantum Technologies, Paul Scherrer Institute (PSI), Forschungsstrasse 111, 5232 Villigen, Switzerland.ORCID 0000-0002-6056-7530

Funding

Swiss Nanoscience Institute 18.01Swiss Nanoscience Institute 1801
6 · The paper itself

Abstract

Surface roughness at the nanometer scale limits the optical performance of reflective components for X-ray and extreme ultraviolet beam shaping. While sub-nanometer roughness can be achieved by polishing planar substrates, it remains challenging for continuous three-dimensional topographies fabricated by grayscale direct-write lithography in polymer resists. In this work, mm-long linear grayscale slopes are introduced as a calibration platform to distinguish between form, waviness, and roughness contributions. Process optimization reduces artifacts such as gray-value discretization and stitching, while replication into PMMA combined with the TASTE process enables a reduction of intrinsic roughness below 2 nm. Laser scanning confocal and atomic force microscopy are used as complementary techniques to assess surface quality across spatial scales. The results provide insight into the origin of roughness in novolak-based resists and its evolution through the fabrication chain, highlighting material limitations and paths toward smooth polymer optics with sub-nanometer roughness.

Indexed as

atomic force microscopy (AFM)direct-write lithographygrayscale lithographylaser scanning confocal microscopy (LSCM)polymer replicationthermally activated selective topography equilibration (TASTE)ultra-thick photoresist

Identifiers

PMID42737157
PMCPMC13567985

What Socratic holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.