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

Statistical timing analysis via modern optimization lens

We formulate statistical static timing analysis (SSTA) as a mixed-integer program and as a geometric program, utilizing histogram approximations of the random variables involved. The geometric-programming approach scales linearly with the number of gates and quadratically with the number of bins in the histogram. This translates, for example, to solving the SSTA for a circuit of 400 gates with 30 bins per each histogram approximation of a random variable in 440 seconds.Comment: 23 pages, 7 figure

Statistical static timing analysis of nonzero clock skew circuit

As microprocessor and ASIC manufacturers continue to push the limits of transistor sizing into the sub-100nm regime, variations in the manufacturing process lead to increased uncertainty about the exact geometry and performance of the resulting devices. Traditional corner-based Static Timing Analysis (STA) assumes worst-case values for process parameters such as transistor channel length and threshold voltage when verifying integrated circuit timing performance. This has become unrea-sonably pessimistic and causes over-design that degrades full-chip performance, wastes engineering effort, and erodes profits while providing negligible yield improvement. Recently, Statistical Static Timing Analysis (SSTA) methods, which model process variations statistically as probability distribution functions (PDFs) rather than deterministically, have emerged to more accurately portray integrated circuit performance. This analysis has been thoroughly performed on traditional zero clock skew circuits where the synchronizing clock signal is assumed to arrive in phase with respect to each register. However, designers will often schedule the clock skew to different registers in order to decrease the minimum clock period of the entire circuit. Clock skew scheduling (CSS) imparts very different timing constraints that are based, in part, on the topology of the circuit. In this thesis, SSTA is applied to nonzero clock skew circuits in order to determine the accuracy improvement relative to their zero skew counterparts, and also to assess how the results of skew scheduling might be impacted with more accurate statistical modeling. For 99.7% timing yield (3 variation), SSTA is observed to improve the accuracy, and therefore increase the timing margin, of nonzero clock skew circuits by up to 2.5x, and on average by 1.3x, the amount seen by zero skew circuits.M.S., Computer Engineering -- Drexel University, 200

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

Exploiting Adaptive Techniques to Improve Processor Energy Efficiency

Rapid device-miniaturization keeps on inducing challenges in building energy efficient microprocessors. As the size of the transistors continuously decreasing, more uncertainties emerge in their operations. On the other hand, integrating more and more transistors on a single chip accentuates the need to lower its supply-voltage. This dissertation investigates one of the primary device uncertainties - timing error, in microprocessor performance bottleneck in NTC era. Then it proposes various innovative techniques to exploit these opportunities to maintain processor energy efficiency, in the context of emerging challenges. Evaluated with the cross-layer methodology, the proposed approaches achieve substantial improvements in processor energy efficiency, compared to other start-of-art techniques

Revamping Timing Error Resilience to Tackle Choke Points at NTC

The growing market of portable devices and smart wearables has contributed to innovation and development of systems with longer battery-life. While Near Threshold Computing (NTC) systems address the need for longer battery-life, they have certain limitations. NTC systems are prone to be significantly affected by variations in the fabrication process, commonly called process variation (PV). This dissertation explores an intriguing effect of PV, called choke points. Choke points are especially important due to their multifarious influence on the functional correctness of an NTC system. This work shows why novel research is required in this direction and proposes two techniques to resolve the problems created by choke points, while maintaining the reduced power needs

Revamping Timing Error Resilience to Tackle Choke Points at NTC

The growing market of portable devices and smart wearables has contributed to innovation and development of systems with longer battery-life. While Near Threshold Computing (NTC) systems address the need for longer battery-life, they have certain limitations. NTC systems are prone to be significantly affected by variations in the fabrication process, commonly called process variation (PV). This dissertation explores an intriguing effect of PV, called choke points. Choke points are especially important due to their multifarious influence on the functional correctness of an NTC system. This work shows why novel research is required in this direction and proposes two techniques to resolve the problems created by choke points, while maintaining the reduced power needs

Efficient algorithms for fundamental statistical timing analysis problems in delay test applications of VLSI circuits

Tremendous advances in semiconductor process technology are creating new challenges for the delay test of today’s digital VLSI circuits. The complexity of state-of-the-art manufacturing processes does not only lead to greater process variability, it also makes today's integrated circuits more prone to defects such as resistive shorts and opens. As a consequence, some of the manufactured circuits do not meet the timing requirements set by the design specification. These circuits must be identified by delay testing and sorted out to ensure the quality of shipped products. Due to the increasing process variability, key transistor and interconnect parameters must be modelled as random variables. These random variables capture the uncertainty caused by process variability, but also the impact of modelling errors and variations in the operating conditions of the circuits, such as the temperature or the supply voltage. The important consequence for delay testing is that a particular delay test detects a delay fault of fixed size in only a subset of all manufactured circuits, which inevitably leads to the shipment of defective products. Despite the fact that this problem is well understood, today's delay test generation methods are unable to consider the distortion of the delay test results, caused by process variability. To analyse and predict the effectiveness of delay tests in a population of circuits which are functionally identical but have varying timing properties, statistical timing analysis is necessary. Although the large runtime of statistical timing analysis is a well known problem, little progress has been made in the development of efficient statistical timing analysis algorithms for the variability-aware delay test generation and delay fault simulation. This dissertation proposes novel and efficient statistical timing analysis algorithms for the variability-aware delay test generation and delay fault simulation in presence of large delay variations. For the detection of path delay faults, a novel probabilistic sensitization analysis is presented which analyses the impact of process variations on the sensitization of the target paths. Furthermore, an efficient method for approximating the probability of detecting small delay faults is presented. Beyond that, efficient statistical SUM and MAX-operations are proposed, which provide the fundamental basis of block-based statistical timing analysis. The experiment results demonstrate the high efficiency of the proposed algorithms