Evidence map›Paper›PMID 36854673›Full record

ArticleNature communications2023

Spectral sensitivity near exceptional points as a resource for hardware encryption.

Minye Yang, Liang Zhu, Qi Zhong, Ramy El-Ganainy, Pai-Yen Chen

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Minye Yang *Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, 60607, USA.
Liang Zhu *Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, 60607, USA.
Qi Zhong *Department of Physics, Michigan Technological University, Houghton, MI, 49931, USA.
Ramy El-GanainyDepartment of Physics, Michigan Technological University, Houghton, MI, 49931, USA. ganainy@mtu.edu.ORCID http://orcid.org/0000-0002-6915-672X
Pai-Yen ChenDepartment of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, 60607, USA. pychen@uic.edu.

Funding

National Science Foundation (NSF) ECCS-1914420United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research) FA9550-21-1-0202
6 · The paper itself

Abstract

The spectral sensitivity near exceptional points (EPs) has been recently explored as an avenue for building sensors with enhanced sensitivity. However, to date, it is not clear whether this class of sensors does indeed outperform traditional sensors in terms of signal-to-noise ratio. In this work, we investigate the spectral sensitivity associated with EPs under a different lens and propose to utilize it as a resource for hardware security. In particular, we introduce a physically unclonable function (PUF) based on analogue electronic circuits that benefit from the drastic eigenvalues bifurcation near a divergent exceptional point to enhance the stochastic entropy caused by inherent parameter fluctuations in electronic components. This in turn results in a perfect entropy source for the generation of encryption keys encoded in analog electrical signals. This lightweight and robust analog-PUF structure may lead to a variety of unforeseen securities and anti-counterfeiting applications in radio-frequency fingerprinting and wireless communications.

Identifiers

PMID36854673
PMCPMC9974995

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.