Micro Electro Mechanical Systems (MEMS) based accelerometers are one of the most commonly used sensors out there. They are used in devices such as, airbags, smartphones, airplanes, and many more. Although they are very accurate, they degrade with time or get offset due to some damage. To fix this, they must be calibrated again using physical calibration technique, which is an expensive process to conduct. However, these sensors can also be calibrated infield by applying an on-chip electrical stimulus to the sensor. Electrical stimulus-based calibration could bring the cost of testing and calibration significantly down as compared to factory testing. In this thesis, simulations are presented to formulate a statistical prediction model based on an electrical stimulus. Results from two different approaches of electrical calibration have been discussed. A prediction model with a root mean square error of 1% has been presented in this work. Experiments were conducted on commercially available accelerometers to test the techniques used for simulations.

This work analyzes and develops a point-of-load (PoL) synchronous buck converter using enhancement-mode Gallium Nitride (e-GaN), with emphasis on optimizing reverse conduction loss by using a well-known technique of placing an anti-parallel Schottky diode across the synchronous power device. This work develops an improved analytical switching model for the GaN-based converter with the Schottky diode using piecewise linear approximations.

To avoid a shoot-through between the power switches of the buck converter, a small dead-time is inserted between gate drive switching transitions. Despite optimum dead-time management for a power converter, optimum dead-times vary for different load conditions. These variations become considerably large for PoL applications, which demand high output current with low output voltages. At high switching frequencies, these variations translate into losses that contribute significantly to the total loss of the converter. To understand and quantify power loss in a hard-switching buck converter that uses a GaN power device in parallel with a Schottky diode, piecewise transitions are used to develop an analytical switching model that quantifies the contribution of reverse conduction loss of GaN during dead-time.

The effects of parasitic elements on the dynamics of the switching converter are investigated during one switching cycle of the converter. A designed prototype of a buck converter is correlated to the predicted model to determine the accuracy of the model. This comparison is presented using simulations and measurements at 400 kHz and 2 MHz converter switching speeds for load (1A) condition and fixed dead-time values. Furthermore, performance of the buck converter with and without the Schottky diode is also measured and compared to demonstrate and quantify the enhanced performance when using an anti-parallel diode. The developed power converter achieves peak efficiencies of 91.7% and 93.86% for 2 MHz and 400 KHz switching frequencies, respectively, and drives load currents up to 6A for a voltage conversion from 12V input to 3.3V output.

In addition, various industry Schottky diodes have been categorized based on their packaging and electrical characteristics and the developed analytical model provides analytical expressions relating the diode characteristics to power stage performance parameters. The performance of these diodes has been characterized for different buck converter voltage step-down ratios that are typically used in industry applications and different switching frequencies ranging from 400 KHz to 2 MHz.

The manufacturing process for electronic systems involves many players, from chip/board design and fabrication to firmware design and installation.

In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk.

Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities.

It is becoming increasingly necessary to trust but verify the received devices both at production and in the field.

Unauthorized hardware or firmware modifications, known as Trojans,

can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices.

Since Trojans may be triggered in the field at an unknown instance,

it is essential to detect their presence at run-time.

However, it isn't easy to run sophisticated detection algorithms on these devices

due to limited computational power and energy, and in some cases, lack of accessibility.

Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain.

In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory.

When the device enters the test mode, a predefined test application is run on the device

repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes.

The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios.

The efficiency of spacecraft’s solar cells reduces over the course of their operation. Traditionally, they are configured to extract maximum power at the end of their life and not have a system which dynamically extracts the maximum power over their entire life. This work demonstrates the benefit of dynamic re-configuration of spacecraft’s solar arrays to access the full power available from the solar panels throughout their lifetime. This dynamic re-configuration is achieved using enhancement mode GaN devices as the switches due to their low Ron and small footprint.

This work discusses hardware Implementation challenges and a prototype board is designed using components-off-the-shelf (COTS) to study the behavior of photovoltaic (PV) panels with different configurations of switches between 5 PV cells. The measurement results from the board proves the feasibility of the idea, showing the power improvements of having the switch structure. The measurement results are used to simulate a 1kW satellite system and understand practical trade-offs of this idea in actual satellite power systems.

Additionally, this work also presents the implementation of CMOS controller integrated circuit (IC) in 0.18um technology. The CMOS controller IC includes switched-capacitor converters in open loop to provide the floating voltages required to drive the GaN switches. Each CMOS controller IC can drive 10 switches in series and parallel combination. Furthermore, the designed controller IC is expected to operate under 300MRad of total dose radiation, thus enabling the controller modules to be placed on the solar cell wings of the satellites.

With the steady advancement of neural network research, new applications are continuously emerging. As a tool for test time reduction, neural networks provide a reliable method of identifying and applying correlations in datasets to speed data processing. By leveraging the power of a deep neural net, it is possible to record the motion of an accelerometer in response to an electrical stimulus and correlate the response with a trim code to reduce the total test time for such sensors. This reduction can be achieved by replacing traditional trimming methods such as physical shaking or mathematical models with a neural net that is able to process raw sensor data collected with the help of a microcontroller. With enough data, the neural net can process the raw responses in real time to predict the correct trim codes without requiring any additional information. Though not yet a complete replacement, the method shows promise given more extensive datasets and industry-level testing and has the potential to disrupt the current state of testing.

Power management circuits are employed in most electronic integrated systems, including applications for automotive, IoT, and smart wearables. Oftentimes, these power management circuits become a single point of system failure, and since they are present in most modern electronic devices, they become a target for hardware security attacks. Digital circuits are typically more prone to security attacks compared to analog circuits, but malfunctions in digital circuitry can affect the analog performance/parameters of power management circuits. This research studies the effect that these hacks will have on the analog performance of power circuits, specifically linear and switching power regulators/converters. Apart from security attacks, these circuits suffer from performance degradations due to temperature, aging, and load stress. Power management circuits usually consist of regulators or converters that regulate the load’s voltage supply by employing a feedback loop, and the stability of the feedback loop is a critical parameter in the system design. Oftentimes, the passive components employed in these circuits shift in value over varying conditions and may cause instability within the power converter. Therefore, variations in the passive components, as well as malicious hardware security attacks, can degrade regulator performance and affect the system’s stability. The traditional ways of detecting phase margin, which indicates system stability, employ techniques that require the converter to be in open loop, and hence can’t be used while the system is deployed in-the-field under normal operation. Aging of components and security attacks may occur after the power management systems have completed post-production test and have been deployed, and they may not cause catastrophic failure of the system, hence making them difficult to detect. These two issues of component variations and security attacks can be detected during normal operation over the product lifetime, if the frequency response of the power converter can be monitored in-situ and in-field. This work presents a method to monitor the phase margin (stability) of a power converter without affecting its normal mode of operation by injecting a white noise/ pseudo random binary sequence (PRBS). Furthermore, this work investigates the analog performance parameters, including phase margin, that are affected by various digital hacks on the control circuitry associated with power converters. A case study of potential hardware attacks is completed for a linear low-dropout regulator (LDO).

This paper presents work that was done to develop an energy-efficient electoral and frame count system for underwater sea turtle image and video recognition using convolutional neural networks, deep learning framework, and the Python programming language. An underwater sea turtle image recognition program is essential to protect turtles from the threat of bycatch - sea turtles are accidentally caught when fishermen aim for a different type of underwater species. This underwater image recognition system is used to detect the presence of sea turtles, then different kinds of acoustic and light stimuli are used to warn the turtles of approaching danger to reduce bycatch. This image detection system will be placed on a fishing boat to run on a machine at all times (24 hours and 7 days a week). A live video capture from a low-power underwater camera that is attached to the boat will be sent to the image detection system on the machine to analyze the presence of sea turtles in each frame of the video. To lower the computational time and energy of the machine, an energy-efficient electoral and frame count system is implemented on this image detection system.

Flexible hybrid electronics (FHE) is emerging as a promising solution to combine the benefits of printed electronics and silicon technology. FHE has many high-impact potential areas, such as wearable applications, health monitoring, and soft robotics, due to its physical advantages, which include light weight, low cost and the ability conform to different shapes. However, physical deformations that can occur in the field lead to significant testing and validation challenges. For example, designers have to ensure that FHE devices continue to meet specs even when the components experience stress due to bending. Hence, physical deformation, which is hard to emulate, has to be part of the test procedures developed for FHE devices. This paper is the first to analyze stress experience at different parts of FHE devices under different bending conditions. Then develop a novel methodology to maximize the test coverage with minimum number of text vectors with the help of a mixed integer linear programming formulation.

This work covers the design and implementation of a Parallel Doherty RF Power Amplifier in a GaN HEMT process for medium power macro-cell (16W) base station applications. This work improves the key parameters of a Doherty Power Amplifier including the peak and back-off efficiency, operational instantaneous bandwidth and output power by proposing a Parallel Doherty amplifier architecture.

As there is a progression in the wireless communication systems from the first generation to the future 5G systems, there is ever increasing demand for higher data rates which means signals with higher peak-to-average power ratios (PAPR). The present modulation schemes require PAPRs close to 8-10dB. So, there is an urgent need to develop energy efficient power amplifiers that can transmit these high data rate signals.

The Doherty Power Amplifier (DPA) is the most common PA architecture in the cellular infrastructure, as it achieves reasonably high back-off power levels with good efficiency. This work advances the DPA architecture by proposing a Parallel Doherty Power Amplifier to broaden the PAs instantaneous bandwidth, designed with frequency range of operation for 2.45 – 2.70 GHz to support WiMAX applications and future broadband signals.

This dissertation proposes and presents two different passive sigma-delta

modulator zoom Analog to Digital Converter (ADC) architectures. The first ADC is fullydifferential, synthesizable zoom-ADC architecture with a passive loop filter for lowfrequency Built in Self-Test (BIST) applications. The detailed ADC architecture and a step

by step process designing the zoom-ADC along with a synthesis tool that can target various

design specifications are presented. The design flow does not rely on extensive knowledge

of an experienced ADC designer. Two example set of BIST ADCs have been synthesized

with different performance requirements in 65nm CMOS process. The first ADC achieves

90.4dB Signal to Noise Ratio (SNR) in 512µs measurement time and consumes 17µW

power. Another example achieves 78.2dB SNR in 31.25µs measurement time and

consumes 63µW power. The second ADC architecture is a multi-mode, dynamically

zooming passive sigma-delta modulator. The architecture is based on a 5b interpolating

flash ADC as the zooming unit, and a passive discrete time sigma delta modulator as the

fine conversion unit. The proposed ADC provides an Oversampling Ratio (OSR)-

independent, dynamic zooming technique, employing an interpolating zooming front-end.

The modulator covers between 0.1 MHz and 10 MHz signal bandwidth which makes it

suitable for cellular applications including 4G radio systems. By reconfiguring the OSR,

bias current, and component parameters, optimal power consumption can be achieved for

every mode. The ADC is implemented in 0.13 µm CMOS technology and it achieves an

SNDR of 82.2/77.1/74.2/68 dB for 0.1/1.92/5/10MHz bandwidth with 1.3/5.7/9.6/11.9mW

power consumption from a 1.2 V supply.