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

Makoto Naruse, Morihisa Hoga, Yasuyuki Ohyagi, Shumpei Nishio, Naoya Tate, Naoki Yoshida, Tsutomu Matsumoto

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)883-888
Number of pages6
JournalPhysica A: Statistical Mechanics and its Applications
Publication statusPublished - Nov 15 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Condensed Matter Physics


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