Cryptographic transistor for true random number generator with low power consumption.

We design a cryptographic transistor (cryptoristor)-based true random number generator (tRNG) with low power consumption and small footprint. This is the first attempt to use irregular and unpredictable operation-induced randomness of a cryptoristor as an entropy source. To extract discrete random numbers with a binary code from the cryptoristor, we developed a noise-coupling analog-to-digital converter. This converter not only converts analog signals to digital random bits but also improves the randomness of the entropy source with low power consumption. The randomness of the cryptoristor is attributed to the random carrier multiplications with the creation and stochastic carrier escape as destruction, which occurs iteratively as long as the input current is fed. The cryptoristor-based tRNG passed 15 randomness test suites of NIST Special Publication 800-22. It is robust to iterative operational stresses and to ambient temperature changes, making it an attractive option for hardware-based security solutions in the Internet of Things due to its low power consumption and small size.

A 10 nV/rt Hz noise level 32-channel neural impedance sensing ASIC for local activation imaging on nerve section.

A 10 nV/rt Hz noise level 32-channel neural impedance sensing ASIC is presented for the application of local activation imaging in nerve section. It is increasingly known that the monitoring and control of nerve signals can improve physical and mental health. Major nerves, such as the vagus nerve and the sciatic nerve, consist of a bundle of fascicles. Therefore, to accurately control a particular application without any side effects, we need to know exactly which fascicle was activated. The only way to find locally activated fascicle is to use electrical impedance tomography (EIT). The ASIC to be introduced is designed for neural EIT applications. A neural impedance sensing ASIC was implemented using CMOS 180-nm process technology. The integrated input referred noise was calculated to be 0.46 µVrms (noise floor 10.3 nVrms/rt Hz) in the measured noise spectrum. At an input of 80 mV, the squared correlation coefficient for linear regression was 0.99998. The amplification gain uniformity of 32 channels was in the range of + 0.23% and - 0.29%. Using the resistor phantom, the simplest model of nerve, it was verified that a single readout channel could detect a signal-to- noise ratio of 75.6 dB or more. Through the reservoir phantom, real-time EIT images were reconstructed at a rate of 8.3 frames per second. The developed ASIC has been applied to in vivo experiments with rat sciatic nerves, and signal processing is currently underway to obtain activated nerve cross-sectional images. The developed ASIC was also applied to in-vivo experiments with rat sciatic nerves, and signal processing is currently underway to obtain locally activated nerve cross-sectional images.