Stochastic resonance in MoS2 photodetector
Akhil Dodda,
Aaryan Oberoi,
Amritanand Sebastian,
Tanushree H. Choudhury,
Joan M. Redwing and
Saptarshi Das ()
Additional contact information
Akhil Dodda: Pennsylvania State University
Aaryan Oberoi: Pennsylvania State University
Amritanand Sebastian: Pennsylvania State University
Tanushree H. Choudhury: Pennsylvania State University
Joan M. Redwing: Pennsylvania State University
Saptarshi Das: Pennsylvania State University
Nature Communications, 2020, vol. 11, issue 1, 1-11
Abstract:
Abstract In this article, we adopt a radical approach for next generation ultra-low-power sensor design by embracing the evolutionary success of animals with extraordinary sensory information processing capabilities that allow them to survive in extreme and resource constrained environments. Stochastic resonance (SR) is one of those astounding phenomena, where noise, which is considered detrimental for electronic circuits and communication systems, plays a constructive role in the detection of weak signals. Here, we show SR in a photodetector based on monolayer MoS2 for detecting ultra-low-intensity subthreshold optical signals from a distant light emitting diode (LED). We demonstrate that weak periodic LED signals, which are otherwise undetectable, can be detected by a MoS2 photodetector in the presence of a finite and optimum amount of white Gaussian noise at a frugal energy expenditure of few tens of nano-Joules. The concept of SR is generic in nature and can be extended beyond photodetector to any other sensors.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
https://www.nature.com/articles/s41467-020-18195-0 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18195-0
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-020-18195-0
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().