Deep Learning-Based Security Verification for a Random Number Generator Using White Chaos

In this paper, a deep learning (DL)-based predictive analysis is proposed to analyze the security of a non-deterministic random number generator (NRNG) using white chaos. In particular, the temporal pattern attention (TPA)-based DL model is employed to learn and analyze the data from both stages of the NRNG: the output data of a chaotic external-cavity semiconductor laser (ECL) and the final output data of the NRNG. For the ECL stage, the results show that the model successfully detects inherent correlations caused by the time-delay signature. After optical heterodyning of two chaotic ECLs and minimal post-processing are introduced, the model detects no patterns among corresponding data. It demonstrates that the NRNG has the strong resistance against the predictive model. Prior to these works, the powerful predictive capability of the model is investigated and demonstrated by applying it to a random number generator (RNG) using linear congruential algorithm. Our research shows that the DL-based predictive model is expected to provide an efficient supplement for evaluating the security and quality of RNGs

Breaking performance limit for waveguide sensor: a way of both enhancing sensitivity and quality factor

In the present study, a dual-dielectric planar waveguides （PWGs） coupling sensor was developed to break the waveguide performance limit. Its designs consisted of a sensor planar waveguide （PWG1） for the realization of multimode sensing and a reference planar waveguide (PWG2) for the realization of Fano and EIT-like resonance. The sensitivity and quality factor （Q） of the device was shown to achieve values of up to 7.8×104 nm/RIU and 4843.75, respectively, allowing its applicability for bulk refractive index （RI） sensing. This yielded an impressive figure of merit （FOM） of 2.2×105 RIU-1, which was the highest reported FOM for an RI sensor. Furthermore, a biosensor designed in this work displayed high-resolution capability and good linearity, along with a limit of detection （LOD） down to 0.14 mg/dL for label-free glucose measurement. This suggests the vast potential for environmental protection, drinking water safety, and biomedical discoveries