Vladimir Zolotov

Statistical timing analysis using bounds and selective enumeration

The growing impact of within-die process variation has created the need for statistical timing analysis, where gate delays are modeled as random variables. Statistical timing analysis has traditionally suffered from exponential run time complexity with circuit size, due to arrival time dependencies created by reconverging paths in the circuit. In this paper, we propose a new approach to statistical timing analysis which uses statistical bounds and selective enumeration to refine these bounds. First, we provide a formal definition of the statistical delay of a circuit and derive a statistical timing analysis method from this definition. Since this method for finding the exact statistical delay has exponential run time complexity with circuit size, we also propose a new method for computing statistical bounds which has linear run time complexity. We prove the correctness of the proposed bounds. Since we provide both a lower and upper bound on the true statistical delay, we can determine the quality of the bounds. If the computed bounds are not sufficiently close to each other, we propose a heuristic to iteratively improve the bounds using selective enumeration of the sample space with additional run time. The proposed methods were implemented and tested on benchmark circuits. The results demonstrate that the proposed bounds have only a small error, which can be further reduced using selective enumeration with modest additional run time.

Robust Extraction of Spatial Correlation

The increased variability of process parameters makes it important yet challenging to extract the statistical characteristics and spatial correlation of process variation. Recent progress in statistical static-timing analysis also makes the extraction important for modern chip designs. Existing approaches extract either only a deterministic component of spatial variation or these approaches do not consider the actual difficulties in computing a valid spatial-correlation function, ignoring the fact that not every function and matrix can be used to describe the spatial correlation. Applying mathematical theories from random fields and convex analysis, we develop: 1) a robust technique to extract a valid spatial-correlation function by solving a constrained nonlinear optimization problem and 2) a robust technique to extract a valid spatial-correlation matrix by employing a modified alternative-projection algorithm. Our novel techniques guarantee to extract a valid spatial-correlation function and matrix from measurement data, even if those measurements are affected by unavoidable random noises. Experiment results, obtained from data generated by a Monte Carlo model, confirm the accuracy and robustness of our techniques and show that we are able to recover the correlation function and matrix with very high accuracy even in the presence of significant random noises

Statistical delay computation considering spatial correlations

Process variation has become a significant concern for static timing analysis. In this paper, we present a new method for path-based statistical timing analysis. We first propose a method for modeling inter- and intra-die device length variations. Based on this model, we then present an efficient method for computing the total path delay probability distribution using a combination of device length enumeration for inter-die variation and an analytical approach for intra-die variation. We also propose a simple and effective model of spatial correlation of intra-die device length variation. The analysis is then extended to include spatial correlation. We test the proposed methods on paths from an industrial high-performance microprocessor and present comparisons with traditional path analysis which does not distinguish between inter- and intra-die variations. The characteristics of the device length distributions were obtained from measured data of 8 test chips with a total of 17688 device length measurements. Spatial correlation data was also obtained from these measurements. We demonstrate the accuracy of the proposed approach by comparing our results with Monte-Carlo simulation.

Robust extraction of spatial correlation

The increased variability of process parameters makes it important yet challenging to extract the statistical characteristics and spatial correlation of process variation. Recent progress in statistical static-timing analysis also makes the extraction important for modern chip designs. Existing approaches extract either only a deterministic component of spatial variation or these approaches do not consider the actual difficulties in computing a valid spatial-correlation function, ignoring the fact that not every function and matrix can be used to describe the spatial correlation. Applying mathematical theories from random fields and convex analysis, we develop: 1) a robust technique to extract a valid spatial-correlation function by solving a constrained nonlinear optimization problem and 2) a robust technique to extract a valid spatial-correlation matrix by employing a modified alternative-projection algorithm. Our novel techniques guarantee to extract a valid spatial-correlation function and matrix from measurement data, even if those measurements are affected by unavoidable random noises. Experiment results, obtained from data generated by a Monte Carlo model, confirm the accuracy and robustness of our techniques and show that we are able to recover the correlation function and matrix with very high accuracy even in the presence of significant random noises

Gate sizing using incremental parameterized statistical timing analysis

As technology scales into the sub-90 nm domain, manufacturing variations become an increasingly significant portion of circuit delay. As a result, delays must be modeled as statistical distributions during both analysis and optimization. This paper uses incremental, parametric statistical static timing analysis (SSTA) to perform gate sizing with a required yield target. Both correlated and uncorrelated process parameters are considered by using a first-order linear delay model with fitted process sensitivities. The fitted sensitivities are verified to be accurate with circuit simulations. Statistical information in the form of criticality probabilities are used to actively guide the optimization process which reduces run-time and improves area and performance. The gate sizing results show a significant improvement in worst slack at 99.86% yield over deterministic optimization.

On-chip inductance modeling and analysis

With operating frequencies approaching the gigahertz range, inductance is becoming an increasingly important consideration in the design and analysis of on-chip interconnect. We present an accurate technique for modeling and analyzing the effects of parasitic inductance on power grid noise, signal delay and crosstalk. We propose a detailed circuit model composed of interconnect resistance, inductance and distributed capacitance, device decoupling capacitances, quiescent activity in the grid, pad locations, and pad/package inductance which accurately determines current distribution and, hence, on-chip inductive effects, and proves superior to the traditional simplified loop inductance approach. The model uses partial inductances, computed using an analytical formula for a pair of parallel rectangular conductors spaced in any relative position. We present experimental results, obtained from simulations of industrial circuits, that show the importance of various model components while analyzing on-chip inductance. We also propose a simple sparsification technique to handle large, dense partial inductance matrices.

Circuit optimization using statistical static timing analysis

In this paper, we propose a new sensitivity based, statistical gate sizing method. Since circuit optimization effects the entire shape of the circuit delay distribution, it is difficult to capture the quality of a distribution with a single metric. Hence, we first introduce a new objective function that provides an effective measure for the quality of a delay distribution for both ASIC and high performance designs. We then propose an efficient and exact sensitivity based pruning algorithm based on a newly proposed theory of perturbation bounds. A heuristic approach for sensitivity computation which relies on efficient computation of statistical slack is then introduced. Finally, we show how the pruning and statistical slack based approaches can be combined to obtain nearly identical results compared with the brute-force approach but with an average run-time improvement of up to 89/spl times/. We also compare the optimization results against that of a deterministic optimizer and show an improvement up to 16% in the 99-percentile circuit delay and up to 31% in the standard deviation for the same circuit area.

Computation and refinement of statistical bounds on circuit delay

The growing impact of within-die process variation has created the need for statistical timing analysis, where gate delays are modeled as random variables. Statistical timing analysis has traditionally suffered from exponential run time complexity with circuit size, due to arrival time dependencies created by reconverging paths in the circuit. In this paper, we propose a new approach to statistical timing analysis that is based on statistical bounds of the circuit delay. Since these bounds have linear run time complexity with circuit size, they can be computed efficiently for large circuits. Since both a lower and upper bound on the true statistical delay is available, the quality of the bounds can be determined. If the computed bounds are not sufficiently close to each other, we propose a heuristic to iteratively improve the bounds using selective enumeration of the sample space with additional run time. We demonstrate that the proposed bounds have only a small error and that by carefully selecting a small set of nodes for enumeration, this error can be further improved.

Model and analysis for combined package and on-chip power grid simulation

We present new modeling and simulation techniques to improve the accuracy and efficiency of transient analysis of large power distribution grids. These include an accurate model for the inherent decoupling capacitance of non-switching devices, as well as a statistical switching current model for the switching devices. Moreover, three new simulation techniques are presented for problem size-reduction and speed-up. Results of application of these techniques on three PowerPC/sup TM/ microprocessors are also presented.

Driver modeling and alignment for worst-case delay noise

In this paper, we present a new approach to model the impact of cross-coupling noise on interconnect delay. We introduce a new linear driver model that accurately models the noise pulse induced on a switching signal net due to cross coupling capacitance. The proposed model effectively captures the nonlinear behavior of the victim driver gate during the transition and has an average error below 8% whereas the traditional approach using a Thevenin model incurs an average error of 48%. We also discuss the worst case alignment of the aggressor net transitions with respect to the victim net transition, emphasizing the need to maximize not merely the delay of the interconnect alone but the combined delay of the interconnect and receiver gate. We show that the worst case alignment of an aggressor net transition is a function of the receiver gate output loading, victim transition edge rate, and the noise pulse width and height and hence propose a pre-characterization approach to efficiently predict the worst-case alignment. The proposed methods were implemented in an industrial noise analysis tool called ClariNet. Results on industrial designs are presented to demonstrate the effectiveness of our approach.