Design method for in-process testing of nanostructured surfaces based on scattered light measurements and machine learning

Nanotechnology is a relatively newer field of science that is finding enormous scope in physical and chemical technology, biology and medicine. Nanostructured surfaces enable local reshaping and guiding of light as well as modified optical, mechanical and chemical properties. Therefore, micromachining and nano-structuring techniques, which typically include processes derived from semiconductor IC fabrication technology, are gaining popularity. Both micromachining and nano-structuring include process steps such as lithography, deposition and etching techniques of various kinds.

By using micromachining and nano-structuring processes it is possible to produce surfaces with nano-texture or nanostructures. In micro-scale the functionalized components are usually cantilevers, membranes etc. which also have some nano-surface requirements. In nano-scale the surface of the structure is in general specifically engineered to have components such as nano-rods, nano-pillars etc. to serve different purposes. There are both micro-scale and nano-scale applications of these devices in variety of fields and their scope is always expanding. Nevertheless, their application can be generalized as sensing and actuation in various micro-/nano-electromechanical systems (MEMS/NEMS). The commercially available examples include optical switches, micro-mirrors, lab-on-a-chip devices etc.

The research in this project focuses on any kind of nano-surfaces and their inspection, regardless of the application. Nano-scale components have specific surface characteristics and need careful quality assessment in order to have desired functionality, and it has motivated researchers to come up with innovative concepts to automate the inspection process. The inspection process has to be non-destructive/non-invasive and having characteristics to integrate it in the production phase of nano-surfaces. This is a qualitative inspection to identify defective nano-surfaces, where the definition of defect is subjective to the surface characteristics. The proposed idea is to use scatterometry to have scattered light field and by using AI techniques it is possible to distinguish the scattered light patterns for defective nano-surface from the defect-free ones.

Funding authority:DFG - German Research Foundation
Funding ID:497286574