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All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors

Author

Listed:
  • Akhil Dodda

    (Penn State University)

  • Nicholas Trainor

    (Penn State University
    Penn State University)

  • Joan. M. Redwing

    (Penn State University
    Penn State University
    Penn State University)

  • Saptarshi Das

    (Penn State University
    Penn State University
    Penn State University
    Penn State University)

Abstract

In the emerging era of the internet of things (IoT), ubiquitous sensors continuously collect, consume, store, and communicate a huge volume of information which is becoming increasingly vulnerable to theft and misuse. Modern software cryptosystems require extensive computational infrastructure for implementing ciphering algorithms, making them difficult to be adopted by IoT edge sensors that operate with limited hardware resources and at low energy budgets. Here we propose and experimentally demonstrate an “all-in-one” 8 × 8 array of robust, low-power, and bio-inspired crypto engines monolithically integrated with IoT edge sensors based on two-dimensional (2D) memtransistors. Each engine comprises five 2D memtransistors to accomplish sensing and encoding functionalities. The ciphered information is shown to be secure from an eavesdropper with finite resources and access to deep neural networks. Our hardware platform consists of a total of 320 fully integrated monolayer MoS2-based memtransistors and consumes energy in the range of hundreds of picojoules and offers near-sensor security.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31148-z
    DOI: 10.1038/s41467-022-31148-z
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    References listed on IDEAS

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    2. Chong Li & Xinxin Liao & Zhi-Ke Peng & Guang Meng & Qingbo He, 2023. "Highly sensitive and broadband meta-mechanoreceptor via mechanical frequency-division multiplexing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. 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.
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