Commercial applicationsSoftware for image analysis of block copolymer line patterns

Miniaturization of device components

Analysis of surface patterns

Semiconductor industry

CONTACTJordi Reverter - jordi.reverter@icn2.cat Head of Knowledge and Technology Transfer

Institut Català de Nanociència i Nanotecnologia (ICN2). Campus de la UAB, Edifici ICN2 08193 Bellaterra / Barcelona (Spain) / Tel.: +34 93 737 26 49 / www.icn2.cat

SOFTWARE FOR IMAGE ANALYSIS OF BLOCK COPOLYMER LINE PATTERNSICN2: Claudia Delgado Simão, Worawut Khunsin, Clivia Sotomayor Torres UCC/Tyndall: Dmitri Tuchapsky, Michael A. Morris, Andreas Amann

To meet the increasing demand for smaller, faster devices, a continued decrease in the feature size of components is required. For the sub-20 nm node, block copolymer (BCP) based technologies present promising cost-effective alternatives to overcome the fabrication challenges of established lithographic techniques. BCPs are a robust plat-form to generate patterns with nanostructures on the 5-100 nm scale and their directed self-assembly has permitted to achieve highly ordered one-dimensional (lines) and two dimensional (hexagonal dots) arrays. Specialized metrology is required for accurate di-mensional and defect analysis of these type of nanostructures.

SOFTWARE CHARACTERISTICS SOFTWARE ADVANTATGES

The software is written in Python language code, an open source software installable in Windows, Linux and Mac based processers. It has a user-friendly interface that permits to analyse any image and retrieve the results in an intuitive and immediate manner. The software receives as input an image of a line pattern from block copolymers directed self-assembly and is able to identify and analyse its lines and defects (branching points, turning points, lone points and end points of the lines).

The output of the software consists in statistical data giving the dimensional metrology of the line arrays (pitch estimation histograms and placement error, line width histograms and LER estimation) and the defect type pareto and estimates the defect density of the samples. This methodology is unique on its kind, being the only tool that permits achieving such information from block copolymer patterns.

► No feature size limitation

► Compatible with soft materials

► Non-destructive method

► Large area analysis

► Suitable for cross sample comparison

► Robust against image contrast and how images were taken

► Versatile for post processing

► Versatile code, can be modified to fit end user needs and preferences

► Specialized in BCP patterns but also analyses lines array prepared by any conventional method

► Full analysis in minutes

► Compatible with semiconductor industry’s metrology requirements