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This paper demonstrates a dual-stacked current recycling linear power supply circuit for biomedical implants. Implantable neuro-stimulators often require high-voltage power supplies for electrode actuation purposes while most of the electronic implant sub-systems require low-voltage supplies which are often generated by linear regulators with poor power efficiency. The proposed current recycling technique and circuit allow linear regulators to be stacked, dividing the high-voltage power supply into two low-voltage supply domains; current can be recycled between these two power supplies and power efficiency in the low-voltage powered circuits can be close to doubled. The power supply circuit is implemented in 0.35 \mum high-voltage CMOS process and occupies an active silicon area of 0.45 mm^2, achieving maximum power saving factor and current efficiency of 48.6% and 97.2%, respectively, with quiescent current of only 45 \muA. [ABSTRACT FROM PUBLISHER]

We present a circuit technique to extract true random numbers from carrier capture and emission in oxide traps in the emerging redox-based resistive memory (ReRAM). This phenomenon that appears as small changes in current magnitude passing through the device is known as random telegraph noise (RTN) and is increasingly becoming a source of reliability issues in nanometer-scale devices. We demonstrate a circuit that exploits TRN suitable for a true random number generator (TRNG) in security applications, where the system is secure from different adversarial attacks, including side-channel monitoring and machine learning analysis. We experimentally characterize RTN in ReRAMs and extract its dependency to temperature, voltage, and area. We introduce an RTN harvesting circuit to mitigate sensitivities to temperature fluctuations, injected supply noise, and power signal monitoring. We reduced bias and imbalance in data due to high-speed sampling via von Neumann whitening. The circuit is compared to conventional non-differential readout approach. Our approach shows a 7.26 times improvement in autocorrelation and significant resilience against the injected supply noise. We also demonstrate the TRNG’s quality and robustness using statistical tests and machine learning attacks. The output of the generator satisfies statistical tests for randomness and is immune to modeling attacks based on the machine learning methods. [ABSTRACT FROM AUTHOR]