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A Compact Low-Voltage True Random Number Generator based on Inkjet Printing Technology

  • Printed electronics (PE) is a fast-growing field with promising applications in wearables, smart sensors, and smart cards, since it provides mechanical flexibility, and low-cost, on-demand, and customizable fabrication. To secure the operation of these applications, true random number generators (TRNGs) are required to generate unpredictable bits for cryptographic functions and padding. However,Printed electronics (PE) is a fast-growing field with promising applications in wearables, smart sensors, and smart cards, since it provides mechanical flexibility, and low-cost, on-demand, and customizable fabrication. To secure the operation of these applications, true random number generators (TRNGs) are required to generate unpredictable bits for cryptographic functions and padding. However, since the additive fabrication process of the PE circuits results in high intrinsic variations due to the random dispersion of the printed inks on the substrate, constructing a printed TRNG is challenging. In this article, we exploit the additive customizable fabrication feature of inkjet printing to design a TRNG based on electrolyte-gated field-effect transistors (EGFETs). We also propose a printed resistor tuning flow for the TRNG circuit to mitigate the overall process variation of the TRNG so that the generated bits are mostly based on the random noise in the circuit, providing a true random behavior. The simulation results show that the overall process variation of the TRNGs is mitigated by 110 times, and the generated bitstream of the tuned TRNGs passes the National Institute of Standards and Technology - Statistical Test Suite. For the proof of concept, the proposed TRNG circuit was fabricated and tuned. The characterization results of the tuned TRNGs prove that the TRNGs generate random bitstreams at the supply voltage of down to 0.5 V. Hence, the proposed TRNG design is suitable to secure low-power applications in this domain.show moreshow less

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Metadaten
Author:Ahmet Turan Erozan, Guan Ying Wang, Rajendra Bishnoi, Jasmin Aghassi-HagmannORCiDGND, Mehdi Baradaran Tahoori
Year of Publication:2020
Language:English
Parent Title (English):IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Volume:28
Issue:6
ISSN:1063-8210 (Print)
ISSN:1557-9999 (Online)
First Page:1485
Last Page:1495
Document Type:Article (reviewed)
Institutes:Bibliografie
Release Date:2020/12/16
Licence (German):License LogoEs gilt das UrhG
DOI:https://doi.org/10.1109/TVLSI.2020.2975876