A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

Abstract

True Random Number Generator (TRNG) is used in many applications, generally for generating random cryptography keys. In this way, the trust of the cryptography system depends on the quality of the random numbers generated. However, the entropy fluctuations produced by external perturbations generate some false positives in the random sequence. These false positives can generate a disastrous scenario, depending on the application. This work presents the results of different tests to demonstrate the robustness and health of the TRNG based on frequency collapse. The TRNG passed all entropy tests provided for NIST SP800-90B and AIS31. The entropy test denotes a 0.9789 minimum entropy normalized and 7.998 Shannon entropy. In addition, the TRNG passes the health tests provided for NIST SP800-90B. The health test shows a number of identical values I v = 0%, I v−1 < 0.004% and a maximum cutoff value M C v = 10 with LM C v = 13 in the repetition count and adaptive proportion tests, respectively. The implementation passed all the statistical tests provided for NIST SP800-22 and AIS20. Besides, the implementation passes the different tests with Process, Voltage, and Temperature (PVT) variations. The TRNG is implemented in a 0.18µm General Purpose (GP) CMOS technology, occupying 25600µm 2 with four entropy sources. Finally, the implementation presents a 7.3 until 9.2-Mb/s of bit rate, 0.56 until 1.88-mW of power consumption, and 77.2 until 204.3-pJ/bit of energy per bit using an entropy source with 16 and 2 delay stages, respectively.

A Peripheral-Free True Random Number Generator Based on Integrated Circuits Enabled by Atomically Thin Two-Dimensional Materials

“…Moreover, the performance of the restart test on the random sequences, for which 1000 bits were collected by restarting the entropy source 1000 times, was also carried out successfully. With a minimum normalized entropy of 0.8780 and an average minimum entropy of 0.8875, our results are at par with earlier studies that utilize frequency collapse in multimodal ring oscillators (RO), , jitter in RO, metastability in latches, and natural decay in radio isotopes as entropy sources. After collecting the bit streams, they were subjected to test protocols under the NIST SP800-22 tests assessing a particular null hypothesis, which assumes that the sequence is random.…”

A 2D Cryptographic Hash Function Incorporating Homomorphic Encryption for Secure Digital Signatures

“…The NIST SP800‐90B tests were performed under the non‐IID track where the minimum entropies of 0.8644 and 0.8718 were achieved for both Key Alice and Key Bob , respectively, which are at par with earlier studies with the same testing parameters. [ 52‐56 ] In addition, the generated keys also pass all of the 15 NIST SP800‐22 tests with a confidence interval of 99%. These results are summarized in Figure S9a,b, (Supporting Information), respectively.…”

A Unified NVRAM and TRNG in Standard CMOS Technology

“…The health test denotes a 21 of Identical Values (I V ) for an entropy source with a binary sequence in the Repetition Count Test (RCT). Also, the Limit of the Maximum Cutoff Value (LM C V ) of 589 with window size (W ) of 1024 bits in the Adaptive Proportion Test (APT) [26]. The charge of the SFP in the NVRAM cell is calibrated to obtain the best condition in the entropy source.…”

“…The secure system typically implements a standalone TRNG, exploiting the different physical phenomena to obtain random numbers. For example, entropy sources based on a chaotic system [20]- [22], charge trapping in FinFet [23], the frequency collapse in multimodal ring oscillator [24]- [26], and metastability in latches [27]- [30]. However, the new approaches unified the TRNG with the memories used in the system, reducing the area overhead.…”