Eigenanalysis of morphological diversity in silicon random nanostructures formed via resist collapse

Nano-artifact metrics is an information security principle and technology that exploits physically uncontrollable processes occurring at the nanometer-scale to protect against increasing security threats. Versatile morphological patterns formed on the surfaces of planar silicon devices originating from resist collapse are one of the most unique and useful vehicles for nano-artifact metrics. In this study, we demonstrate the eigenanalysis of experimentally fabricated silicon random nanostructures, through which the diversity and the potential capacity of identities are quantitatively characterized. Our eigenspace-based approach provides intuitive physical pictures and quantitative discussions regarding the morphological diversity of nanostructured devices while unifying measurement stability, which is one of the most important concerns regarding security applications. The analysis suggests approximately 10¹¹⁵ possible identities per 0.18-μm² nanostructure area, indicating the usefulness of nanoscale versatile morphology. The presented eigenanalysis approach has the potential to be widely applicable to other materials, devices, and system architectures.