Related Experiment Video
Updated: May 5, 2026

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
Published on: February 12, 2017
Modelling Key Performance Indicators (KPIs) in the Optimisation of Nanoimprint Lithography (NIL) Processes
Andrzej Pacana1, Karolina Czerwińska1
1Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland.
None:
Nanoimprint lithography (NIL) plays an increasingly important role in modern nanomanufacturing processes, but its effective application in production conditions requires precise tools for evaluating and optimising technological processes. The aim of the study was to develop and model key performance indicators (KPIs) supporting the optimisation of the quality, stability and efficiency of nanoimprint lithography processes. As part of the selection of indicators, a literature review, surveys and in-depth interviews with industry experts were conducted, which enabled the identification of indicators relevant from a technological practice perspective. The proposed KPI classification was directly linked to the stages of the NIL process, creating a basis for operational performance control and process capability analysis. A novel aspect is the proposal of an integrated KPI model that combines the classification of indicators based on the stages of the NIL process with their direct link to technological parameters and measurable quality effects. These indicators have been linked to critical process parameters for different NIL variants, including Thermal NIL, UV-NIL, Roll-to-Roll NIL and Step-and-Repeat NIL, using a process analysis and multi-criteria optimisation approach. Research indicates that the use of an integrated KPI model improves the accuracy of nanostructure mapping, reduced defect density, and increased process efficiency without compromising technological stability. The proposed approach is a universal tool supporting NIL process control, its scaling to industrial applications, and integration with statistical process control and data-driven optimisation methods.
More Related Videos
09:45Large-area Scanning Probe Nanolithography Facilitated by Automated Alignment and Its Application to Substrate Fabrication for Cell Culture Studies
Published on: June 12, 2018
10:49Nanomoulding of Functional Materials, a Versatile Complementary Pattern Replication Method to Nanoimprinting
Published on: January 23, 2013