Modeling Leakage in Sub-Micron CMOS Technologies

As CMOS technology scaling continues, subthreshold leakage current increases dramatically. A significant percentage of the total chip power is due to leakage, also known as static power. Accurately estimating static power in early stages of design is an important step for developing power efficient products. Leakage current is an important segment of total supply current (IDDQ), which is used as a means to identify defective chips. ID^^ value is determined by the sum of leakage currents of those transistors that can leak. Setting ID^^ value too high or low will result in excessive shipment of defective chips or yield loss because of rejecting good parts, respectively. The goal of this work is to investigate and model leakage mechanisms in submicron CMOS technology using SPICE circuit simulators. The main focus of this research will be subthreshold and reverse-bias p-n junction band-to-band leakage mechanisms and the effect of transistor parameters on them

Methodology for Standby Leakage Power Reduction in Nanometer-Scale CMOS Circuits

In nanometer-scale CMOS technology, leakage power has become a major component of the total power dissipation due to the downscaling of threshold voltage and gate oxide thickness. The leakage power consumption has received even more attention by increasing demand for mobile devices. Since mobile devices spend a majority of their time in a standby mode, the leakage power savings in standby state is critical to extend battery lifetime. For this reason, low power has become a major factor in designing CMOS circuits. In this dissertation, we propose a novel transistor reordering methodology for leakage reduction. Unlike previous technique, the proposed method provides exact reordering rules for minimum leakage formation by considering all leakage components. Thus, this method formulates an optimized structure for leakage reduction even in complex CMOS logic gate, and can be used in combination with other leakage reduction techniques to achieve further improvement. We also propose a new standby leakage reduction methodology, leakage-aware body biasing, to overcome the shortcomings of a conventional Reverse Body Biasing (RBB) technique. The RBB technique has been used to reduce subthreshold leakage current. Therefore, this technique works well under subthreshold dominant region even though it has intrinsic structural drawbacks. However, such drawbacks cannot be overlooked anymore since gate leakage has become comparable to subthreshold leakage in nanometer-scale region. In addition, BTBT leakage also increases with technology scaling due to the higher doping concentration applied in each process technology. In these circumstances, the objective of leakage minimization is not a single leakage source but the overall leakage sources. The proposed leakage-aware body biasing technique, unlike conventional RBB technique, considers all major leakage sources to minimize the negative effects of existing body biasing approach. This can be achieved by intelligently applying body bias to appropriate CMOS network based on its status (on-/off-state) with the aid of a pin/transistor reordering technique

Leakage Power Modeling and Reduction Techniques for Field Programmable Gate Arrays

FPGAs have become quite popular for implementing digital circuits and systems because of reduced costs and fast design cycles. This has led to increased complexity of FPGAs, and with technology scaling, many new challenges have come up for the FPGA industry, leakage power being one of the key challenges. The current generation FPGAs are being implemented in 90nm technology, therefore, managing leakage power in deep-submicron FPGAs has become critical for the FPGA industry to remain competitive in the semiconductor market and to enter the mobile applications domain. In this work an analytical state dependent leakage power model for FPGAs is developed, followed by dual-Vt based designs of the FPGA architecture for reducing leakage power. The leakage power model computes subthreshold and gate leakage in FPGAs, since these are the two dominant components of total leakage power in the scaled nanometer technologies. The leakage power model takes into account the dependency of gate and subthreshold leakage on the state of the circuit inputs. The leakage power model has two main components, one which computes the probability of a state for a particular FPGA circuit element, and the other which computes the leakage of the FPGA circuit element for a given input using analytical equations. This FPGA power model is particularly important for rapidly analyzing various FPGA architectures across different technology nodes. Dual-Vt based designs of the FPGA architecture are proposed, developed, and evaluated, for reducing the leakage power using a CAD framework. The logic and the routing resources of the FPGA are considered for dual-Vt assignment. The number of the logic elements that can be assigned high-Vt in the ideal case by using a dual-Vt assignment algorithm in the CAD framework is estimated. Based upon this estimate two kinds of architectures are developed and evaluated, homogeneous and heterogeneous architectures. Results indicate that leakage power savings of up to 50% can be obtained from these architectures. The analytical state dependent leakage power model developed has been used for estimating the leakage power savings from the dual-Vt FPGA architectures. The CAD framework that has been developed can also be used for developing and evaluating different dual-Vt FPGA architectures, other than the ones proposed in this work

Robust Optimization of Nanometer SRAM Designs

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

Variability-aware low-power techniques for nanoscale mixed-signal circuits.

New circuit design techniques that accommodate lower supply voltages necessary for portable systems need to be integrated into the semiconductor intellectual property (IP) core. Systems that once worked at 3.3 V or 2.5 V now need to work at 1.8 V or lower, without causing any performance degradation. Also, the fluctuation of device characteristics caused by process variation in nanometer technologies is seen as design yield loss. The numerous parasitic effects induced by layouts, especially for high-performance and high-speed circuits, pose a problem for IC design. Lack of exact layout information during circuit sizing leads to long design iterations involving time-consuming runs of complex tools. There is a strong need for low-power, high-performance, parasitic-aware and process-variation-tolerant circuit design. This dissertation proposes methodologies and techniques to achieve variability, power, performance, and parasitic-aware circuit designs. Three approaches are proposed: the single iteration automatic approach, the hybrid Monte Carlo and design of experiments (DOE) approach, and the corner-based approach. Widely used mixed-signal circuits such as analog-to-digital converter (ADC), voltage controlled oscillator (VCO), voltage level converter and active pixel sensor (APS) have been designed at nanoscale complementary metal oxide semiconductor (CMOS) and subjected to the proposed methodologies. The effectiveness of the proposed methodologies has been demonstrated through exhaustive simulations. Apart from these methodologies, the application of dual-oxide and dual-threshold techniques at circuit level in order to minimize power and leakage is also explored

Low power predictable memory and processing architectures

Great demand in power optimized devices shows promising economic potential and draws lots of attention in industry and research area. Due to the continuously shrinking CMOS process, not only dynamic power but also static power has emerged as a big concern in power reduction. Other than power optimization, average-case power estimation is quite significant for power budget allocation but also challenging in terms of time and effort. In this thesis, we will introduce a methodology to support modular quantitative analysis in order to estimate average power of circuits, on the basis of two concepts named Random Bag Preserving and Linear Compositionality. It can shorten simulation time and sustain high accuracy, resulting in increasing the feasibility of power estimation of big systems. For power saving, firstly, we take advantages of the low power characteristic of adiabatic logic and asynchronous logic to achieve ultra-low dynamic and static power. We will propose two memory cells, which could run in adiabatic and non-adiabatic mode. About 90% dynamic power can be saved in adiabatic mode when compared to other up-to-date designs. About 90% leakage power is saved. Secondly, a novel logic, named Asynchronous Charge Sharing Logic (ACSL), will be introduced. The realization of completion detection is simplified considerably. Not just the power reduction improvement, ACSL brings another promising feature in average power estimation called data-independency where this characteristic would make power estimation effortless and be meaningful for modular quantitative average case analysis. Finally, a new asynchronous Arithmetic Logic Unit (ALU) with a ripple carry adder implemented using the logically reversible/bidirectional characteristic exhibiting ultra-low power dissipation with sub-threshold region operating point will be presented. The proposed adder is able to operate multi-functionally

Scaling and intrinsic parameter fluctuations in nanoCMOS devices

The core of this thesis is a thorough investigation of the scaling properties of conventional nano-CMOS MOSFETs, their physical and operational limitations and intrinsic parameter fluctuations. To support this investigation a well calibrated 35 nm physical gate length real MOSFET fabricated by Toshiba was used as a reference transistor. Prior to the start of scaling to shorter channel lengths, the simulators were calibrated against the experimentally measured characteristics of the reference device. Comprehensive numerical simulators were then used for designing the next five generations of transistors that correspond to the technology nodes of the latest International Technology Roadmap for Semiconductors (lTRS). The scaling of field effect transistors is one of the most widely studied concepts in semiconductor technology. The emphases of such studies have varied over the years, being dictated by the dominant issues faced by the microelectronics industry. The research presented in this thesis is focused on the present state of the scaling of conventional MOSFETs and its projections during the next 15 years. The electrical properties of conventional MOSFETs; threshold voltage (VT), subthreshold slope (S) and on-off currents (lon, Ioffi ), which are scaled to channel lengths of 35, 25, 18, 13, and 9 nm have been investigated. In addition, the channel doping profile and the corresponding carrier mobility in each generation of transistors have also been studied and compared. The concern of limited solid solubility of dopants in silicon is also addressed along with the problem of high channel doping concentrations in scaled devices. The other important issue associated with the scaling of conventional MOSFETs are the intrinsic parameter fluctuations (IPF) due to discrete random dopants in the inversion layer and the effects of gate Line Edge Roughness (LER). The variations of the three important MOSFET parameters (loff, VT and Ion), induced by random discrete dopants and LER have been comprehensively studied in the thesis. Finally, one of the promising emerging CMOS transistor architectures, the Ultra Thin Body (UTB) SOl MOSFET, which is expected to replace the conventional MOSFET, has been investigated from the scaling point of view