With aggressive technology scaling, within-die random variations are becoming the most dominant source of process variations. Gate-level statistical static timing is becoming a widely accepted approach as an alternative to static timing analysis. However, statistical timing approaches lack good models for handling timing variations due to within-die random variations. Before performing statistical timing analysis on a design or System On Chip (SoC), the cells in the library are pre-characterized for delay as well as constraints due to these random variations. This is referred to as statistical characterization of the cells. The major contribution of this dissertation is the development of novel techniques for statistical characterization and optimization of cells. The methods couple the knowledge of circuits along with the significant factor analysis methods to compute the sensitivities, to perform statistical timing and to perform sensitivity-aware cell optimizations. The first contribution of this dissertation is a statistical delay characterization method developed for computing delay sensitivities of standard cells considering both global and mismatch process variations. In addition to the cells being characterized for delay, the sequential cells are characterized for timing constraints like setup and hold time constraints. The second contribution of this dissertation addresses the problem of constraint sensitivity characterization in sequential cells. Block-based statistical timing approaches lack accurate consideration of the impact of slew variations on both delay and arrival time variations. Specifically, the delay variations due to within-die random variables (mismatch variables) result in a slew-based correlation during timing propagation. Handling within-die random variations more accurately during statistical timing propagation is the topic of the third contribution of this dissertation. Clock networks are more prone to these within-die random variations and can result in significant clock-skew variations. In the fourth contribution, a timing margining methodology is presented that accurately accounts for the clock skew variations in a timing sign-off flow. Typically, the standard cells are designed very early in the design cycle and long before the process reaches production maturity. Any subtle improvements to reduce variability in standard cells can improve parametric yield significantly. Statistical characterization of cells for timing provides a key baseline for understanding the circuit behavior due to different sources of variation. The sensitivity information can also help increase yield by reducing the variability during the circuit design itself. The final contribution in the dissertation addresses this by defining key cell and device criticality metrics. A sensitivity-aware standard cell layout optimization is demonstrated using the proposed criticality metrics. / text

The Energy Management Strategy (EMS) has a huge effect on the performance of any hybrid vehicle because it determines the operating point of almost every component associated with the powertrain. This means that its optimisation is an incredibly complex task which must consider a number of objectives including the fuel consumption, drive-ability, component degradation and straight-line performance. The EMS is of particular importance for Fuel Cell Hybrid Electric Vehicles (FCHEVs), not only to minimise the fuel consumption, but also to reduce the electrical stress on the fuel cell and maximise its useful lifetime. This is because the durability and cost of the fuel cell stack is one of the major obstacles preventing FCHEVs from being competitive with conventional vehicles. In this work, a novel EMS is developed, specifcally for Fuel Cell Hybrid Electric Vehicles (FCHEVs), which considers not only the fuel consumption, but also the degradation of the fuel cell in order to optimise the overall running cost of the vehicle. This work is believed to be the first of its kind to quantify effect of decisions made by the EMS on the fuel cell degradation, inclusive of multiple causes of voltage degradation. The performance of this new strategy is compared in simulation to a recent strategy from the literature designed solely to optimise the fuel consumption. It is found that the inclusion of the degradation metrics results in a 20% increase in fuel cell lifetime for only a 3.7% increase in the fuel consumption, meaning that the overall running cost is reduced by 9%. In addition to direct implementation on board a vehicle, this technique for optimising the degradation alongside the fuel consumption also allows alternative vehicle designs to be compared in an unbiased way. In order to demonstrate this, the novel optimisation technique is subsequently used to compare alternative system designs in order to identify the optimal economic sizing of the fuel cell and battery pack. It is found that the overall running cost can be minimised by using the smallest possible fuel cell stack that will satisfy the average power requirement of the duty cycle, and by using an oversized battery pack to maximise the fuel cell effciency and minimise the transient loading on the stack. This research was undertaken at Loughborough University as part of the Doctoral Training Centre (DTC) in Hydrogen, Fuel Cells and Their Applications in collaboration with the University of Birmingham and Nottingham University and with sponsorship from HORIBA-MIRA (Nuneaton, UK). A Microcab H4 test vehicle has been made available for use in testing for this research which was previously used for approximately 2 years at the University of Birmingham. The Microcab H4 is a small campus based vehicle designed for passenger transport and mail delivery at low speeds as seen on a university campus. It has a top speed of approximately 30mph, and is fitted with a 1.2kW fuel cell and a 2kWh battery pack.