Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/184761
Title: SOT-MTJ-based non-volatile flip-flop with in-memory randomness for application in grain stream ciphers
Authors: Nisar, Arshid
Zahoor, Furqan
Thakker, Sidhaant Sachin
Das, Kunal Kranti
Maitra, Subhamoy
Kaushik, Brajesh Kumar
Chattopadhyay, Anupam
Keywords: Computer and Information Science
Issue Date: 2025
Source: Nisar, A., Zahoor, F., Thakker, S. S., Das, K. K., Maitra, S., Kaushik, B. K. & Chattopadhyay, A. (2025). SOT-MTJ-based non-volatile flip-flop with in-memory randomness for application in grain stream ciphers. IEEE Access, 13, 34677-34686. https://dx.doi.org/10.1109/ACCESS.2025.3543733
Project: NRF-CRP21-2018-0003
Journal: IEEE Access
Abstract: This paper proposes a method for in-memory true random number generation (TRNG) by leveraging the dual functionality of spin-orbit torque based magnetic tunnel junction (SOT-MTJ) while showcasing its efficacy in hardware-efficient Grain stream ciphers for lightweight cryptographic applications. Depending upon its mode of operation, SOT-MTJ acts as both a memory element and a true random number generator. To demonstrate its practical application, SOT-MTJ based non-volatile flip flop (NVFF) is designed which is further utilized to implement Grain-128 stream cipher, as a case study. The SOT-MTJ based NVFF not only carries out the standard shift operation for cipher implementation but also functions as an in-situ initial vector generator for generating key stream, eliminating the need for an additional TRNG circuit. The results show that the proposed Grain-128 cipher design is 5.6 × and 2.5 × more energy efficient and 5× and 2× faster as compared to STT and SOT-MTJ based designs. Furthermore, in comparison to CMOS based cipher design, the proposed technique shows nearly ∼34× more efficiency in terms of area overhead. The proposed approach holds huge promise for resource-constrained cryptographic applications in edge devices.
URI: https://hdl.handle.net/10356/184761
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2025.3543733
Schools: College of Computing and Data Science 
Rights: © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CCDS Journal Articles

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