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Technology scaling has been the most obvious choice of designers and chip manufacturing companies to improve the performance of analog and digital circuits. With the ever shrinking technological node, process variations can no longer be ignored and play a significant role in determining the performance of nanoscaled devices. By choosing a worst case design methodology, circuit designers have been very munificent with the design parameters chosen, often manifesting in pessimistic designs with significant area overheads. Significant work has been done in estimating the impact of intra-die process variations on circuit performance, pertinently, noise margin and standby leakage power, for fixed transistor channel dimensions. However, for an optimal, high yield, SRAM cell design, it is absolutely imperative to analyze the impact of process variations at every design point, especially, since the distribution of process variations is a statistically varying parameter and has an inverse correlation with the area of the MOS transistor. Furthermore, the first order analytical models used for optimization of SRAM memories are not as accurate and the impact of voltage and its inclusion as an input, along with other design parameters, is often ignored. In this thesis, the performance parameters of a nano-scaled 6-T SRAM cell are modeled as an accurate, yield aware, empirical polynomial predictor, in the presence of intra-die process variations. The estimated empirical models are used in a constrained non-linear, robust optimization framework to design an SRAM cell, for a 45 nm CMOS technology, having optimal performance, according to bounds specified for the circuit performance parameters, with the objective of minimizing on-chip area. This statistically aware technique provides a more realistic design methodology to study the trade off between performance parameters of the SRAM. Furthermore, a dual optimization approach is followed by considering SRAM power supply and wordline voltages as additional input parameters, to simultaneously tune the design parameters, ensuring a high yield and considerable area reduction. In addition, the cell level optimization framework is extended to the system level optimization of caches, under both cell level and system level performance constraints.

Process variability is the pivotal factor impacting the design of high yield integrated circuits and systems in deep sub-micron CMOS technologies. The electrical and physical properties of transistors and interconnects, the building blocks of integrated circuits, are prone to significant variations that directly impact the performance and power consumption of the fabricated devices, severely impacting the manufacturing yield. However, the large number of the transistors on a single chip adds even more challenges for the analysis of the variation effects, a critical task in diagnosing the cause of failure and designing for yield. Reliable and efficient statistical analysis methodologies in various design phases are key to predict the yield before entering such an expensive fabrication process. In this thesis, the impacts of process variations are examined at three different levels: device, circuit, and micro-architecture. The variation models are provided for each level of abstraction, and new methodologies are proposed for efficient statistical analysis and design under variation. At the circuit level, the variability analysis of three crucial sub-blocks of today's system-on-chips, namely, digital circuits, memory cells, and analog blocks, are targeted. The accurate and efficient yield analysis of circuits is recognized as an extremely challenging task within the electronic design automation community. The large scale of the digital circuits, the extremely high yield requirement for memory cells, and the time-consuming analog circuit simulation are major concerns in the development of any statistical analysis technique. In this thesis, several sampling-based methods have been proposed for these three types of circuits to significantly improve the run-time of the traditional Monte Carlo method, without compromising accuracy. The proposed sampling-based yield analysis methods benefit from the very appealing feature of the MC method, that is, the capability to consider any complex circuit model. However, through the use and engineering of advanced variance reduction and sampling methods, ultra-fast yield estimation solutions are provided for different types of VLSI circuits. Such methods include control variate, importance sampling, correlation-controlled Latin Hypercube Sampling, and Quasi Monte Carlo. At the device level, a methodology is proposed which introduces a variation-aware design perspective for designing MOS devices in aggressively scaled geometries. The method introduces a yield measure at the device level which targets the saturation and leakage currents of an MOS transistor. A statistical method is developed to optimize the advanced doping profiles and geometry features of a device for achieving a maximum device-level yield. Finally, a statistical thermal analysis framework is proposed. It accounts for the process and thermal variations simultaneously, at the micro-architectural level. The analyzer is developed, based on the fact that the process variations lead to uncertain leakage power sources, so that the thermal profile, itself, would have a probabilistic nature. Therefore, by a co-process-thermal-leakage analysis, a more reliable full-chip statistical leakage power yield is calculated.

Frequency Modulated Continuous Wave (FMCW) radar has wide application areas in both civil and military use. The range resolution is a critical concept for these FMCW radars as for the other radar types. There are theoretical restrictions in the range resolution. In addition, the non-ideal properties of the modules used in the systems negatively affects the range resolution. The transmitter leakage, non-linear frequency sweep, FM to AM distortion and measurement errors are some of the critical non-ideal properties. The problems arising from these non-ideal properties further restrict the range resolution of FMCW radars. Another important concept for the range resolution that can be obtained from FMCW radars is the signal processing method. This thesis deals with the non-ideal properties of the system modules and techniques to reduce their effects on the range resolution. Furthermore, the signal processing methods used for FMCW radar signals and the possible improvement techniques for these methods are discussed. Moreover, a simple signal processing unit called zero crossing counter which can be used for short range FMCW radars is implemented and range resolution performance of this zero crossing counter is investigated by carrying out measurements on a prototype FMCW radar at 2200MHz.

Process variability is the pivotal factor impacting the design of high yield integrated circuits and systems in deep sub-micron CMOS technologies. The electrical and physical properties of transistors and interconnects, the building blocks of integrated circuits, are prone to significant variations that directly impact the performance and power consumption of the fabricated devices, severely impacting the manufacturing yield. However, the large number of the transistors on a single chip adds even more challenges for the analysis of the variation effects, a critical task in diagnosing the cause of failure and designing for yield. Reliable and efficient statistical analysis methodologies in various design phases are key to predict the yield before entering such an expensive fabrication process. In this thesis, the impacts of process variations are examined at three different levels: device, circuit, and micro-architecture. The variation models are provided for each level of abstraction, and new methodologies are proposed for efficient statistical analysis and design under variation. At the circuit level, the variability analysis of three crucial sub-blocks of today's system-on-chips, namely, digital circuits, memory cells, and analog blocks, are targeted. The accurate and efficient yield analysis of circuits is recognized as an extremely challenging task within the electronic design automation community. The large scale of the digital circuits, the extremely high yield requirement for memory cells, and the time-consuming analog circuit simulation are major concerns in the development of any statistical analysis technique. In this thesis, several sampling-based methods have been proposed for these three types of circuits to significantly improve the run-time of the traditional Monte Carlo method, without compromising accuracy. The proposed sampling-based yield analysis methods benefit from the very appealing feature of the MC method, that is, the capability to consider any complex circuit model. However, through the use and engineering of advanced variance reduction and sampling methods, ultra-fast yield estimation solutions are provided for different types of VLSI circuits. Such methods include control variate, importance sampling, correlation-controlled Latin Hypercube Sampling, and Quasi Monte Carlo. At the device level, a methodology is proposed which introduces a variation-aware design perspective for designing MOS devices in aggressively scaled geometries. The method introduces a yield measure at the device level which targets the saturation and leakage currents of an MOS transistor. A statistical method is developed to optimize the advanced doping profiles and geometry features of a device for achieving a maximum device-level yield. Finally, a statistical thermal analysis framework is proposed. It accounts for the process and thermal variations simultaneously, at the micro-architectural level. The analyzer is developed, based on the fact that the process variations lead to uncertain leakage power sources, so that the thermal profile, itself, would have a probabilistic nature. Therefore, by a co-process-thermal-leakage analysis, a more reliable full-chip statistical leakage power yield is calculated.

Non-invasive side-channel attacks (SCA) are powerful attacks which can be used to obtain the secret key in a cryptographic circuit in feasible time without the need for expensive measurement equipment. Power analysis attacks (PAA) are a type of SCA that exploit the correlation between the leaked power consumption information and processed/stored data. Differential power analysis (DPA) and leakage power analysis (LPA) attacks are two types of PAA that exploit different characteristics of the side-channel leakage profile. DPA attacks exploit the correlation between the input data and dynamic power consumption of cryptographic circuits. Alternatively, LPA attacks utilize the correlation between the input data and leakage power dissipation of cryptographic circuits. There is a growing trend to integrate voltage regulators fully on-chip in modern integrated circuits (ICs) to reduce the power noise, improve transient response time, and increase power efficiency. Therefore, when on-chip voltage regulation is utilized as a countermeasure against power analysis attacks, the overhead is low. However, a one-to-one relationship exists between the input power and load power when a conventional on-chip voltage regulator is utilized. In order to break the one-to-one relationship between the input power and load power, two methodologies can be considered: (a) selecting multi-phase on-chip voltage regulator and using pseudo-random number generator (PRNG) to scramble the activation or deactivation pattern of the multi-phase voltage regulator in the input power profile, (b) enabling random voltage/scaling on conventional on-chip voltage regulators to insert uncertainties to the load power profile. In this dissertation, on-chip voltage regulators are utilized as lightweight countermeasures against power analysis attacks. Converter-reshuffling (CoRe) technique is proposed as a countermeasure against DPA attacks by using a PRNG to scramble the input power profile. The time-delayed CoRe technique is designed to eliminate machine learning-based DPA attacks through inserting a certain time delay. The charge-withheld CoRe technique is proposed to enhance the entropy of the input power profile against DPA attacks with two PRNGs. The security-adaptive (SA) voltage converter is designed to sense LPA attacks and activate countermeasure with low overhead. Additionally, three conventional on-chip voltage regulators: low-dropout (LDO) regulator, buck converter, and switched-capacitor converter are combined with three different kinds of voltage/frequency scaling techniques: random dynamic voltage and frequency scaling (RDVFS), random dynamic voltage scaling (RDVS), and aggressive voltage and frequency scaling (AVFS), respectively, against both DPA and LPA attacks.

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