Browse Prior Art Database

A Method for Defect Quantification of Directed Self-Assembly Processes using SEM

IP.com Disclosure Number: IPCOM000240450D
Publication Date: 2015-Jan-30
Document File: 4 page(s) / 136K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is an automated image-processing algorithm that locates discrepancies by comparing a Scanning Electron Microscope (SEM) image to a given reference. It then outputs the statistics.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 52% of the total text.

Page 01 of 4

A Method for Defect Quantification of Directed Self -Assembly Processes using SEM

Directed Self-Assembly (DSA) is a promising technique that utilizes block copolymers (BCPs) and lithography to create high-resolution patterns (<30nm-pitch) for semiconductor patterning uses.

Studying the area/count of DSA-specific defects (e.g. dislocation, jogs, etc.) in a line/space array can help optimize the process conditions and materials, and therefore further improve the yield.

Modern defect inspection tools do not have the capability to accurately report the defect area due to resolution limits. The existing tools can only report a defect count for smaller defects. Accurately quantifying the defect area can enable the learning of critical processing parameters and further help the optimization of the DSA process.

The novel solution is an image-processing algorithm. The automated algorithm locates the discrepancy by comparing a Scanning Electron Microscope (SEM) image to a given reference, and then outputs the statistics. This algorithm is designed to be used on the SEM images acquired by existing metrology tools, such as CD-SEM, which has high resolution but no functionality to analyze defects.

Figure 1: Demonstrating the identification of non-grating area (defect) in a given SEM image and obtain the area/count of defects

The application of this method to non-grating structures is straightforward. The sensitivity of this method can be further fine-tuned based on the imaging conditions, pixel size, grating dimension, and the target feature size.

Figure 2: Flo...