Optical theremin based true Random Number Generation (TRNG) system

Sharma, Rahul; Ullagaddimath, Ramya; Roy, Amit Baran; Halder, Apratim; Hegde, Veena

doi:10.1109/icacci.2015.7275670

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…The weaknesses of the thermal noise-based designs are its sensitivity to noise-based attacks, compromising the randomness of the output, as well as issues with the design complexity required for post-processing and tuning of the system 10,[23][24][25][26] . Many other entropy sources have been used for TRNGs, such as the timing of time dependent dielectric breakdown (TDDB) 27 , Photonics [28][29][30][31] , and current variation or switching time of emerging technologies such as ReRAM [32][33][34] . TDDB-based designs suffer from high complexity of circuit design and processing circuits which limit their use in low-area, low-power applications.…”

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confidence: 99%

Random-telegraph-noise-enabled true random number generator for hardware security

Brown

Zhang

Zhou

et al. 2020

Sci Rep

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The future security of Internet of Things is a key concern in the cyber-security field. One of the key issues is the ability to generate random numbers with strict power and area constrains. “True Random Number Generators” have been presented as a potential solution to this problem but improvements in output bit rate, power consumption, and design complexity must be made. In this work we present a novel and experimentally verified “True Random Number Generator” that uses exclusively conventional CMOS technology as well as offering key improvements over previous designs in complexity, output bitrate, and power consumption. It uses the inherent randomness of telegraph noise in the channel current of a single CMOS transistor as an entropy source. For the first time multi-level and abnormal telegraph noise can be utilised, which greatly reduces device selectivity and offers much greater bitrates. The design is verified using a breadboard and FPGA proof of concept circuit and passes all 15 of the NIST randomness tests without any need for post-processing of the generated bitstream. The design also shows resilience against machine learning attacks performed by the LSTM neural network.

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