An effective modeling framework for the analysis of interconnects subject to line-edge roughness

This letter proposes a complete and efficient simulation framework to assess the effects of line-edge roughness appearing in on-chip lines. The modeling approach consists of three steps. First, a stochastic macromodel is created for the per-unit-length RLGC parameters of the line. Secondly, random conductor edge profiles are generated using randomized splines. These are combined with the stochastic macromodel to readily provide place-dependent RLGC parameters. Finally, the resulting nonuniform transmission line is analyzed by means of a fast and accurate perturbation technique. To validate the proposed approach, a statistical analysis of the response of a coupled inverted embedded microstrip line is carried out for different roughness parameters

SIGNAL PROCESSING TECHNIQUES AND APPLICATIONS

As the technologies scaling down, more transistors can be fabricated into the same area, which enables the integration of many components into the same substrate, referred to as system-on-chip (SoC). The components on SoC are connected by on-chip global interconnects. It has been shown in the recent International Technology Roadmap of Semiconductors (ITRS) that when scaling down, gate delay decreases, but global interconnect delay increases due to crosstalk. The interconnect delay has become a bottleneck of the overall system performance. Many techniques have been proposed to address crosstalk, such as shielding, buffer insertion, and crosstalk avoidance codes (CACs). The CAC is a promising technique due to its good crosstalk reduction, less power consumption and lower area. In this dissertation, I will present analytical delay models for on-chip interconnects with improved accuracy. This enables us to have a more accurate control of delays for transition patterns and lead to a more efficient CAC, whose worst-case delay is 30-40% smaller than the best of previously proposed CACs. As the clock frequency approaches multi-gigahertz, the parasitic inductance of on-chip interconnects has become significant and its detrimental effects, including increased delay, voltage overshoots and undershoots, and increased crosstalk noise, cannot be ignored. We introduce new CACs to address both capacitive and inductive couplings simultaneously.Quantum computers are more powerful in solving some NP problems than the classical computers. However, quantum computers suffer greatly from unwanted interactions with environment. Quantum error correction codes (QECCs) are needed to protect quantum information against noise and decoherence. Given their good error-correcting performance, it is desirable to adapt existing iterative decoding algorithms of LDPC codes to obtain LDPC-based QECCs. Several QECCs based on nonbinary LDPC codes have been proposed with a much better error-correcting performance than existing quantum codes over a qubit channel. In this dissertation, I will present stabilizer codes based on nonbinary QC-LDPC codes for qubit channels. The results will confirm the observation that QECCs based on nonbinary LDPC codes appear to achieve better performance than QECCs based on binary LDPC codes.As the technologies scaling down further to nanoscale, CMOS devices suffer greatly from the quantum mechanical effects. Some emerging nano devices, such as resonant tunneling diodes (RTDs), quantum cellular automata (QCA), and single electron transistors (SETs), have no such issues and are promising candidates to replace the traditional CMOS devices. Threshold gate, which can implement complex Boolean functions within a single gate, can be easily realized with these devices. Several applications dealing with real-valued signals have already been realized using nanotechnology based threshold gates. Unfortunately, the applications using finite fields, such as error correcting coding and cryptography, have not been realized using nanotechnology. The main obstacle is that they require a great number of exclusive-ORs (XORs), which cannot be realized in a single threshold gate. Besides, the fan-in of a threshold gate in RTD nanotechnology needs to be bounded for both reliability and performance purpose. In this dissertation, I will present a majority-class threshold architecture of XORs with bounded fan-in, and compare it with a Boolean-class architecture. I will show an application of the proposed XORs for the finite field multiplications. The analysis results will show that the majority class outperforms the Boolean class architectures in terms of hardware complexity and latency. I will also introduce a sort-and-search algorithm, which can be used for implementations of any symmetric functions. Since XOR is a special symmetric function, it can be implemented via the sort-and-search algorithm. To leverage the power of multi-input threshold functions, I generalize the previously proposed sort-and-search algorithm from a fan-in of two to arbitrary fan-ins, and propose an architecture of multi-input XORs with bounded fan-ins

Interconnect capacitance extraction under geometric uncertainties

Interconnects are an important constituent of any large scale integrated circuit, and accurate interconnect analysis is essential not only for post-layout verification but also for synthesis. For instance, extraction of interconnect capacitance is needed for the prediction of interconnect-induced delay, crosstalk, and other signal distortion related effects that are used to guide IC routing and floor planning. The continuous progress of semiconductor technology is leading ICs to the era of 45 nm technology and beyond. However, this progress has been associated with increasing variability during the manufacturing processes. This variability leads to stochastic variations in geometric and material parameters and has a significant impact on interconnect capacitance. It is therefore important to be able to quantify the effect of such process induced variations on interconnect capacitance. In this thesis, we have worked on a methodology towards modeling of interconnect capacitance in the presence of geometric uncertainties. More specifically, a methodology is proposed for the finite element solution of Laplace's equation for the calculation of the per-unit-length capacitance matrix of a multi-conductor interconnect structure embedded in a multi-layered insulating substrate and in the presence of statistical variation in conductor and substrate geometry. The proposed method is founded on the idea of defining a single, mean geometry, which is subsequently used with a single finite element discretization, to extract the statistics of the interconnect capacitance in an expedient fashion. We demonstrate the accuracy and efficiency of our method through its application to the extraction of capacitances in some representative geometries for IC interconnects

Optimization Schemes for Variability-Driven VLSI Design Automation

Today's IC design is facing several challenges due to increasing circuit complexity and decreasing feature size, as it pushes to extend Moore's law into nano-scale dimensions. Apart from the challenges that arise directly as a result of feature scaling (e.g., increasing leakage power, reliability issues), imperfections in the manufacturing process have recently turned into a major design hurdle, due to the variations they cause in the device and interconnect parameters from their target values. From an IC design automation perspective, a shift in paradigm, from deterministic to probabilistic, is needed to handle the unpredictable nature of these fabrication variations. In such a probabilistic paradigm, the varying circuit parameters such as leakage power or delay should be accurately modeled, and their correlations due to common sources of variations or physical location on the chip should be well captured. Moreover, variability-driven (probabilistic) design automation needs to efficiently generate a high quality solution. A particular challenge in variability-driven design automation is to define optimality measures among candidate solutions, which allow for inferior solutions to be removed from the solution space thus reducing the run-time complexity. In this dissertation, the superiority probability is introduced as such an optimality measure, and two methods are proposed to compute this probability: an accurate Conditional Monte Carlo simulation method, and an efficient moment-matching approximation method. The effectiveness of using the superiority probability is shown in the context of two important design automation applications: 1) the buffer insertion problem, 2) the dual-Vth leakage optimization problem. Another important task in variability-driven design automation is to develop optimization techniques that can provably generate the optimal solution in an efficient way. In this dissertation, the application of the gate sizing problem is explored to optimally reduce the loss due to fabrication variations in the presence of a timing constraint. The presented formulation, in contrast with the existing variability-driven approaches which are all based on heuristics, is provably optimal. Moreover, unlike existing approaches, it is independent of any assumption on the source and nature of variations

A design flow for performance planning : new paradigms for iteration free synthesis

In conventional design, higher levels of synthesis produce a netlist, from which layout synthesis builds a mask specification for manufacturing. Timing anal ysis is built into a feedback loop to detect timing violations which are then used to update specifications to synthesis. Such iteration is undesirable, and for very high performance designs, infeasible. The problem is likely to become much worse with future generations of technology. To achieve a non-iterative design flow, early synthesis stages should use wire planning to distribute delays over the functional elements and interconnect, and layout synthesis should use its degrees of freedom to realize those delays