Fast high-order variation-aware IC interconnect analysis

Interconnects constitute a dominant source of circuit delay for modern chip designs. The variations of critical dimensions in modern VLSI technologies lead to variability in interconnect performance that must be fully accounted for in timing verification. However, handling a multitude of inter-die/intra-die variations and assessing their impacts on circuit performance can dramatically complicate the timing analysis. In this thesis, three practical interconnect delay and slew analysis methods are presented to facilitate efficient evaluation of wire performance variability. The first method is described in detail in Chapter III. It harnesses a collection of computationally efficient procedures and closed-form formulas. By doing so, process variations are directly mapped into the variability of the output delay and slew. This method can provide the closed-form formulas of the output delay and slew at any sink node of the interconnect nets fully parameterized, in-process variations. The second method is based on adjoint sensitivity analysis and driving point model. It constructs the driving point model of the driver which drives the interconnect net by using the adjoint sensitivity analysis method. Then the driving point model can be propagated through the interconnect network by using the first method to obtain the closedform formulas of the output delay and slew. The third method is the generalized second-order adjoint sensitivity analysis. We give the mathematical derivation of this method in Chapter V. The theoretical value of this method is it can not only handle this particular variational interconnect delay and slew analysis, but it also provides an avenue for automatical linear network analysis and optimization. The proposed methods not only provide statistical performance evaluations of the interconnect network under analysis but also produce delay and slew expressions parameterized in the underlying process variations in a quadratic parametric form. Experimental results show that superior accuracy can be achieved by our proposed methods

High-performance and Low-power Clock Network Synthesis in the Presence of Variation.

Semiconductor technology scaling requires continuous evolution of all aspects of physical design of integrated circuits. Among the major design steps, clock-network synthesis has been greatly affected by technology scaling, rendering existing methodologies inadequate. Clock routing was previously sufficient for smaller ICs, but design difficulty and structural complexity have greatly increased as interconnect delay and clock frequency increased in the 1990s. Since a clock network directly influences IC performance and often consumes a substantial portion of total power, both academia and industry developed synthesis methodologies to achieve low skew, low power and robustness from PVT variations. Nevertheless, clock network synthesis under tight constraints is currently the least automated step in physical design and requires significant manual intervention, undermining turn-around-time. The need for multi-objective optimization over a large parameter space and the increasing impact of process variation make clock network synthesis particularly challenging. Our work identifies new objectives, constraints and concerns in the clock-network synthesis for systems-on-chips and microprocessors. To address them, we generate novel clock-network structures and propose changes in traditional physical-design flows. We develop new modeling techniques and algorithms for clock power optimization subject to tight skew constraints in the presence of process variations. In particular, we offer SPICE-accurate optimizations of clock networks, coordinated to reduce nominal skew below 5 ps, satisfy slew constraints and trade-off skew, insertion delay and power, while tolerating variations. To broaden the scope of clock-network-synthesis optimizations, we propose new techniques and a methodology to reduce dynamic power consumption by 6.8%-11.6% for large IC designs with macro blocks by integrating clock network synthesis within global placement. We also present a novel non-tree topology that is 2.3x more power-efficient than mesh structures. We fuse several clock trees to create large-scale redundancy in a clock network to bridge the gap between tree-like and mesh-like topologies. Integrated optimization techniques for high-quality clock networks described in this dissertation strong empirical results in experiments with recent industry-released benchmarks in the presence of process variation. Our software implementations were recognized with the first-place awards at the ISPD 2009 and ISPD 2010 Clock-Network Synthesis Contests organized by IBM Research and Intel Research.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89711/1/ejdjsy_1.pd

A Fast Symbolic Computation Approach to Statistical Analysis of Mesh Networks with Multiple Sources *

Abstract-Mesh circuits typically consist of many resistive links and many sources. Accurate analysis of massive mesh networks is demanding in the current integrated circuit design practice, yet their computation confronts numerous challenges. When variation is considered, mesh analysis becomes a much harder task. This paper proposes a symbolic computation technique that can be applied to the moment-based analysis of mesh networks with multiple sources. The variation issues are easily taken care of by a structured computation mechanism, which can naturally facilitate sensitivity based analysis. Applications are addressed by applying the computation technique to a set of mesh circuits with varying sizes

Crosstalk Noise Analysis for Nano-Meter VLSI Circuits.

Scaling of device dimensions into the nanometer process technology has led to a considerable reduction in the gate delays. However, interconnect delays have not scaled in proportion to gate delays, and global-interconnect delays account for a major portion of the total circuit delay. Also, due to process-technology scaling, the spacing between adjacent interconnect wires keeps shrinking, which leads to an increase in the amount of coupling capacitance between interconnect wires. Hence, coupling noise has become an important issue which must be modeled while performing timing verification for VLSI chips. As delay noise strongly depends on the skew between aggressor-victim input transitions, it is not possible to a priori identify the victim-input transition that results in the worst-case delay noise. This thesis presents an analytical result that would obviate the need to search for the worst-case victim-input transition and simplify the aggressor-victim alignment problem significantly. We also propose a heuristic approach to compute the worst-case aggressor alignment that maximizes the victim receiver-output arrival time with current-source driver models. We develop algorithms to compute the set of top-k aggressors in the circuit, which could be fixed to reduce the delay noise of the circuit. Process variations cause variability in the aggressor-victim alignment which leads to variability in the delay noise. This variability is modeled by deriving closed-form expressions of the mean, the standard deviation and the correlations of the delay-noise distribution. We also propose an approach to estimate the confidence bounds on the path delay-noise distribution. Finally, we show that the interconnect corners obtained without incorporating the effects of coupling noise could lead to significant errors, and propose an approach to compute the interconnect corners considering the impact of coupling noise.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64663/1/gravkis_1.pd

Architectural level delay and leakage power modelling of manufacturing process variation

PhD ThesisThe effect of manufacturing process variations has become a major issue regarding the estimation of circuit delay and power dissipation, and will gain more importance in the future as device scaling continues in order to satisfy market place demands for circuits with greater performance and functionality per unit area. Statistical modelling and analysis approaches have been widely used to reflect the effects of a variety of variational process parameters on system performance factor which will be described as probability density functions (PDFs). At present most of the investigations into statistical models has been limited to small circuits such as a logic gate. However, the massive size of present day electronic systems precludes the use of design techniques which consider a system to comprise these basic gates, as this level of design is very inefficient and error prone. This thesis proposes a methodology to bring the effects of process variation from transistor level up to architectural level in terms of circuit delay and leakage power dissipation. Using a first order canonical model and statistical analysis approach, a statistical cell library has been built which comprises not only the basic gate cell models, but also more complex functional blocks such as registers, FIFOs, counters, ALUs etc. Furthermore, other sensitive factors to the overall system performance, such as input signal slope, output load capacitance, different signal switching cases and transition types are also taken into account for each cell in the library, which makes it adaptive to an incremental circuit design. The proposed methodology enables an efficient analysis of process variation effects on system performance with significantly reduced computation time compared to the Monte Carlo simulation approach. As a demonstration vehicle for this technique, the delay and leakage power distributions of a 2-stage asynchronous micropipeline circuit has been simulated using this cell library. The experimental results show that the proposed method can predict the delay and leakage power distribution with less than 5% error and at least 50,000 times faster computation time compare to 5000-sample SPICE based Monte Carlo simulation. The methodology presented here for modelling process variability plays a significant role in Design for Manufacturability (DFM) by quantifying the direct impact of process variations on system performance. The advantages of being able to undertake this analysis at a high level of abstraction and thus early in the design cycle are two fold. First, if the predicted effects of process variation render the circuit performance to be outwith specification, design modifications can be readily incorporated to rectify the situation. Second, knowing what the acceptable limits of process variation are to maintain design performance within its specification, informed choices can be made regarding the implementation technology and manufacturer selected to fabricate the design

Modeling and Analysis of Large-Scale On-Chip Interconnects

As IC technologies scale to the nanometer regime, efficient and accurate modeling and analysis of VLSI systems with billions of transistors and interconnects becomes increasingly critical and difficult. VLSI systems impacted by the increasingly high dimensional process-voltage-temperature (PVT) variations demand much more modeling and analysis efforts than ever before, while the analysis of large scale on-chip interconnects that requires solving tens of millions of unknowns imposes great challenges in computer aided design areas. This dissertation presents new methodologies for addressing the above two important challenging issues for large scale on-chip interconnect modeling and analysis: In the past, the standard statistical circuit modeling techniques usually employ principal component analysis (PCA) and its variants to reduce the parameter dimensionality. Although widely adopted, these techniques can be very limited since parameter dimension reduction is achieved by merely considering the statistical distributions of the controlling parameters but neglecting the important correspondence between these parameters and the circuit performances (responses) under modeling. This dissertation presents a variety of performance-oriented parameter dimension reduction methods that can lead to more than one order of magnitude parameter reduction for a variety of VLSI circuit modeling and analysis problems. The sheer size of present day power/ground distribution networks makes their analysis and verification tasks extremely runtime and memory inefficient, and at the same time, limits the extent to which these networks can be optimized. Given today?s commodity graphics processing units (GPUs) that can deliver more than 500 GFlops (Flops: floating point operations per second). computing power and 100GB/s memory bandwidth, which are more than 10X greater than offered by modern day general-purpose quad-core microprocessors, it is very desirable to convert the impressive GPU computing power to usable design automation tools for VLSI verification. In this dissertation, for the first time, we show how to exploit recent massively parallel single-instruction multiple-thread (SIMT) based graphics processing unit (GPU) platforms to tackle power grid analysis with very promising performance. Our GPU based network analyzer is capable of solving tens of millions of power grid nodes in just a few seconds. Additionally, with the above GPU based simulation framework, more challenging three-dimensional full-chip thermal analysis can be solved in a much more efficient way than ever before

Advanced control systems for fast orbit feedback of synchrotron electron beams

Diamond Light Source is the UK’s national synchrotron facility that produces synchrotron radiation for research. At source points of synchrotron radiation, the electron beam stability relative to the beam size is critical for the optimal performance of synchrotrons. The current requirement at Diamond is that variations in the beam position should not exceed 10% of the beam size for frequencies up to 140Hz. This is guaranteed by the fast orbit feedback that actuates hundreds of corrector magnets at a sampling rate of 10kHz to reduce beam vibrations down to sub-micron levels. For the next-generation upgrade, Diamond-II, the beam stability requirements will be raised to 3% up to 1kHz. Consequently, the sampling rate will be increased to 100kHz and an additional array of fast correctors will be introduced, which precludes the use of the existing controller. This thesis develops two different control approaches to accommodate the additional array of fast correctors at Diamond-II: internal model control based on the generalised singular value decomposition (GSVD) and model predictive control (MPC). In contrast to existing controllers, the proposed approaches treat the control problem as a whole and consider both arrays simultaneously. To achieve the sampling rate of 100kHz, this thesis proposes to reduce the computational complexity of the controllers in several ways, such as by exploiting symmetries of the magnetic lattice. To validate the controllers for Diamond-II, a real-time control system is implemented on high-performance hardware and integrated in the existing synchrotron. As a first-of-its-kind application to electron beam stabilisation in synchrotrons, this thesis presents real-world results from both MPC and GSVD-based controllers, demonstrating that the proposed approaches meet theoretical expectations with respect to performance and robustness in practice. The results from this thesis, and in particular the novel GSVD-based method, were successfully adopted for the Diamond-II upgrade. This may enable the use of more advanced control systems in similar large-scale and high-speed applications in the future

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Modeling and Design Techniques for 3-D ICs under Process, Voltage, and Temperature Variations

Three-dimensional (3-D) integration is a promising solution to further enhance the density and performance of modern integrated circuits (ICs). In 3-D ICs, multiple dies (tiers or planes) are vertically stacked. These dies can be designed and fabricated separately. In addition, these dies can be fabricated in different technologies. The effect of different sources of variations on 3-D circuits, consequently, differ from 2-D ICs. As technology scales, these variations significantly affect the performance of circuits. Therefore, it is increasingly important to accurately and efficiently model different sources of variations in 3-D ICs. The process, voltage, and temperature variations in 3-D ICs are investigated in this dissertation. Related modeling and design techniques are proposed to design a robust 3-D IC. Process variations in 3-D ICs are first analyzed. The effect of process variations on synchronization and 3-D clock distribution networks, is carefully studied. A novel statistical model is proposed to describe the timing variation in 3-D clock distribution networks caused by process variations. Based on this model, different topologies of 3-D clock distribution networks are compared in terms of skew variation. A set of guidelines is proposed to design 3-D clock distribution networks with low clock uncertainty. Voltage variations are described by power supply noise. Power supply noise in 3-D ICs is investigated considering different characteristics of potential 3-D power grids in this thesis. A new algorithm is developed to fast analyze the steady-state IR-drop in 3-D power grids. The first droop of power supply noise, also called resonant supply noise, is usually the deepest voltage drop in power distribution networks. The effect of resonant supply noise on 3-D clock distribution networks is investigated. The combined effect of process variations and power supply noise is modeled by skitter consisting of both skew and jitter. A novel statistical model of skitter is proposed. Based on this proposed model and simulation results, a set of guidelines has been proposed to mitigate the negative effect of process and voltage variations on 3-D clock distribution networks. Thermal issues in 3-D ICs are considered by carefully modeling thermal through silicon vias (TTSVs) in this dissertation. TTSVs are vertical vias which do not carry signals, dedicated to facilitate the propagation of heat to reduce the temperature of 3-D ICs. Two analytic models are proposed to describe the heat transfer in 3-D circuits related to TTSVs herein, providing proper closed-form expressions for the thermal resistance of the TTSVs. The effect of different physical and geometric parameters of TTSVs on the temperature of 3-D ICs is analyzed. The proposed models can be used to fast and accurately estimate the temperature to avoid the overuse of TTSVs occupying a large portion of area. A set of models and design techniques is proposed in this dissertation to describe and mitigate the deleterious effects of process, voltage, and temperature variations in 3-D ICs. Due to the continuous shrink in the feature size of transistors, the large number of devices within one circuit, and the high operating frequency, the effect of these variations on the performance of 3-D ICs becomes increasingly significant. Accurately and efficiently estimating and controlling these variations are, consequently, critical tasks for the design of 3-D ICs

Layout optimization in ultra deep submicron VLSI design

As fabrication technology keeps advancing, many deep submicron (DSM) effects have become increasingly evident and can no longer be ignored in Very Large Scale Integration (VLSI) design. In this dissertation, we study several deep submicron problems (eg. coupling capacitance, antenna effect and delay variation) and propose optimization techniques to mitigate these DSM effects in the place-and-route stage of VLSI physical design. The place-and-route stage of physical design can be further divided into several steps: (1) Placement, (2) Global routing, (3) Layer assignment, (4) Track assignment, and (5) Detailed routing. Among them, layer/track assignment assigns major trunks of wire segments to specific layers/tracks in order to guide the underlying detailed router. In this dissertation, we have proposed techniques to handle coupling capacitance at the layer/track assignment stage, antenna effect at the layer assignment, and delay variation at the ECO (Engineering Change Order) placement stage, respectively. More specifically, at layer assignment, we have proposed an improved probabilistic model to quickly estimate the amount of coupling capacitance for timing optimization. Antenna effects are also handled at layer assignment through a linear-time tree partitioning algorithm. At the track assignment stage, timing is further optimized using a graph based technique. In addition, we have proposed a novel gate splitting methodology to reduce delay variation in the ECO placement considering spatial correlations. Experimental results on benchmark circuits showed the effectiveness of our approaches