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

  • 1 Citations

Abstract

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
Volume462
DOIs
StatePublished - 2016 Nov 15

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Nanostructures
Silicon
Security of data

Keywords

  • Artifact metrics
  • Eigenanalysis
  • Information security
  • Measurement stability
  • Nanoscale morphology
  • Resist collapse

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Statistics and Probability

Cite this

Eigenanalysis of morphological diversity in silicon random nanostructures formed via resist collapse. / Naruse, Makoto; Hoga, Morihisa; Ohyagi, Yasuyuki; Nishio, Shumpei; Tate, Naoya; Yoshida, Naoki; Matsumoto, Tsutomu.

In: Physica A: Statistical Mechanics and its Applications, Vol. 462, 15.11.2016, p. 883-888.

Research output: Contribution to journalArticle

Naruse, Makoto; Hoga, Morihisa; Ohyagi, Yasuyuki; Nishio, Shumpei; Tate, Naoya; Yoshida, Naoki; Matsumoto, Tsutomu / Eigenanalysis of morphological diversity in silicon random nanostructures formed via resist collapse.

In: Physica A: Statistical Mechanics and its Applications, Vol. 462, 15.11.2016, p. 883-888.

Research output: Contribution to journalArticle

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AU - Tate,Naoya

AU - Yoshida,Naoki

AU - Matsumoto,Tsutomu

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N2 - 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.

AB - 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.

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