A Charge-Recycling Scheme and Ultra Low Voltage Self-Startup Charge Pump for Highly Energy Efficient Mixed Signal Systems-On-A-Chip

The advent of battery operated sensor-based electronic systems has provided a pressing need to design energy-efficient, ultra-low power integrated circuits as a means to improve the battery lifetime. This dissertation describes a scheme to lower the power requirement of a digital circuit through the use of charge-recycling and dynamic supply-voltage scaling techniques. The novel charge-recycling scheme proposed in this research demonstrates the feasibility of operating digital circuits using the charge scavenged from the leakage and dynamic load currents inherent to digital design. The proposed scheme efficiently gathers the “ground-bound” charge into storage capacitor banks. This reclaimed charge is then subsequently recycled to power the source digital circuit. The charge-recycling methodology has been implemented on a 12-bit Gray-code counter operating at frequencies of less than 50 MHz. The circuit has been designed in a 90-nm process and measurement results reveal more than 41% reduction in the average energy consumption of the counter. The total energy savings including the power consumed for the generation of control signals aggregates to an average of 23%. The proposed methodology can be applied to an existing digital path without any design change to the circuit but with only small loss to the performance. Potential applications of this scheme are described, specifically in wide-temperature dynamic power reduction and as a source for energy harvesters. The second part of this dissertation deals with the design and development of a self-starting, ultra-low voltage, switched-capacitor (SC) DC-DC converter that is essential to an energy harvesting system. The proposed charge-pump based SC-converter operates from 125-mV input and thus enables battery-less operation in ultra-low voltage energy harvesters. The charge pump does not require any external components or expensive post-fabrication processing to enable low-voltage operation. This design has been implemented in a 130-nm CMOS process. While the proposed charge pump provides significant efficiency enhancement in energy harvesters, it can also be incorporated within charge recycling systems to facilitate adaptable charge-recycling levels. In total, this dissertation provides key components needed for highly energy-efficient mixed signal systems-on-a-chip

Deep brain drug-delivery control using vagus nerve communications

Vagus nerve stimulation (VNS) uses electrical impulses applied at the neck in order to mitigate the effects of, for example, epileptic seizures. We propose using VNS to provide data pulses to communicate with a drug-delivery system embedded near the brainstem. We model the generation of a vagus nerve compound action potential (CAP), calculating the signal attenuation and the resulting transmission range. The metabolic cost of CAP transmission in terms of the use of adenosine triphosphate (ATP) is also calculated. The channel capacity for on-off keying (OOK) is computed from the CAP characteristics, the neural refractory period and the level of background neural noise. The resulting low bit-rate, unidirectional asynchronous transmission system is analysed for the use of different methods of forward error correction (FEC) to improve bit-error rate (BER). We show a proposed data packet structure that could deliver instructions to an embedded drug-delivery system with multiple addressable drug reservoirs. We also analyse the scope for powering the drug-delivery system with energy harvested from cerebrospinal glucose

Energy harvesting from human and machine motion for wireless electronic devices

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Low power digital baseband core for wireless Micro-Neural-Interface using CMOS sub/near-threshold circuit

This thesis presents the work on designing and implementing a low power digital baseband core with custom-tailored protocol for wirelessly powered Micro-Neural-Interface (MNI) System-on-Chip (SoC) to be implanted within the skull to record cortical neural activities. The core, on the tag end of distributed sensors, is designed to control the operation of individual MNI and communicate and control MNI devices implanted across the brain using received downlink commands from external base station and store/dump targeted neural data uplink in an energy efficient manner. The application specific protocol defines three modes (Time Stamp Mode, Streaming Mode and Snippet Mode) to extract neural signals with on-chip signal conditioning and discrimination. In Time Stamp Mode, Streaming Mode and Snippet Mode, the core executes basic on-chip spike discrimination and compression, real-time monitoring and segment capturing of neural signals so single spike timing as well as inter-spike timing can be retrieved with high temporal and spatial resolution. To implement the core control logic using sub/near-threshold logic, a novel digital design methodology is proposed which considers INWE (Inverse-Narrow-Width-Effect), RSCE (Reverse-Short-Channel-Effect) and variation comprehensively to size the transistor width and length accordingly to achieve close-to-optimum digital circuits. Ultra-low-power cell library containing 67 cells including physical cells and decoupling capacitor cells using the optimum fingers is designed, laid-out, characterized, and abstracted. A robust on-chip sense-amp-less SRAM memory (8X32 size) for storing neural data is implemented using 8T topology and LVT fingers. The design is validated with silicon tapeout and measurement shows the digital baseband core works at 400mV and 1.28 MHz system clock with an average power consumption of 2.2 μW, resulting in highest reported communication power efficiency of 290Kbps/μW to date