We propose a programmable low-complexity current mode circuit to implement a controllable chaotic Sawtooth map, to take into account and correct the effects of the fabrication process variability, mismatches and temperature variations. The proposed solution, simulated using Cadence Virtuoso and a 0.35μm mixed-signal CMOS technology provided by AMS, is presented and discussed with theoretical arguments

We propose a novel class of Digital Nonlinear Oscillators (DNOs) supporting complex dynamics, including chaos, suitable for the definition of high-performance and low-complexity entropy sources in Programmable Logic Devices (PLDs). We derive our proposal from the analysis of simplified models, investigated as non-autonomous nonlinear dynamical systems under different excitation conditions. The study lead the authors to the design of a fully digital entropy source consuming only two slices of a Xilinx FPGA, including post-processing, sufficient to define a class of TRNGs capable to pass the NIST standard tests for randomness in any worst case experimentally tested by the authors (6 chips, 96 generators). The solution has been compared with others published in the literature, confirming the validity of the proposal.

We discuss the low-complexity integrated circuit design of a true random bit generator with parametric self-tuning capabilities. The design targets a mixedsignal complementary metal-oxide-semiconductor (CMOS) current-mode circuit implementing the Sawtooth chaotic map. Starting from the analysis of the proposed circuit topologies, we investigate how the dynamical system behavior is affected by nonlinear distortions, discussing theoretical simplified models and resorting to qualified numerical simulations. The circuit solutions proposed in this paper and validated with experiments represent an improvement of the designs published in literature, proposing technical solutions to solve stability issues and introducing parametric adjustment capabilities while maintaining a strong link with the studied theoretical models.

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