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Stochastic resonance in MoS2 photodetector

Author

Listed:
  • 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
    Pennsylvania State University
    Pennsylvania State University)

  • Saptarshi Das

    (Pennsylvania State University
    Pennsylvania State University
    Pennsylvania State University)

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.

Suggested Citation

  • Akhil Dodda & Aaryan Oberoi & Amritanand Sebastian & Tanushree H. Choudhury & Joan M. Redwing & Saptarshi Das, 2020. "Stochastic resonance in MoS2 photodetector," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18195-0
    DOI: 10.1038/s41467-020-18195-0
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    Cited by:

    1. Qiao, Zijian & He, Yuanbiao & Liao, Changrong & Zhu, Ronghua, 2023. "Noise-boosted weak signal detection in fractional nonlinear systems enhanced by increasing potential-well width and its application to mechanical fault diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. Amritanand Sebastian & Rahul Pendurthi & Azimkhan Kozhakhmetov & Nicholas Trainor & Joshua A. Robinson & Joan M. Redwing & Saptarshi Das, 2022. "Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Yikai Zheng & Harikrishnan Ravichandran & Thomas F. Schranghamer & Nicholas Trainor & Joan M. Redwing & Saptarshi Das, 2022. "Hardware implementation of Bayesian network based on two-dimensional memtransistors," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Subir Ghosh & Andrew Pannone & Dipanjan Sen & Akshay Wali & Harikrishnan Ravichandran & Saptarshi Das, 2023. "An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Akhil Dodda & Nicholas Trainor & Joan. M. Redwing & Saptarshi Das, 2022. "All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    6. Muhtasim Ul Karim Sadaf & Najam U Sakib & Andrew Pannone & Harikrishnan Ravichandran & Saptarshi Das, 2023. "A bio-inspired visuotactile neuron for multisensory integration," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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