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

Yield-driven power-delay-optimal CMOS full-adder design complying with automotive product specifications of PVT variations and NBTI degradations

We present the detailed results of the application of mathematical optimization algorithms to transistor sizing in a full-adder cell design, to obtain the maximum expected fabrication yield. The approach takes into account all the fabrication process parameter variations specified in an industrial PDK, in addition to operating condition range and NBTI aging. The final design solutions present transistor sizing, which depart from intuitive transistor sizing criteria and show dramatic yield improvements, which have been verified by Monte Carlo SPICE analysis

Statistical leakage estimation in 32nm CMOS considering cells correlations

International audienceIn this paper a method to estimate the leakage power consumption of CMOS digital circuits taking into account input states and process variations is proposed. The statistical leakage estimation is based on a pre-characterization of library cells considering correlations (ρ) between cells leakages. A method to create cells leakage correlation matrix is introduced. The maximum relative error achieved in the correlation matrix is 0.4% with respect to the correlations obtained by Monte Carlo simulations. Next the total circuit leakage is calculated from this matrix and cells leakage means and variances. The accuracy and efficiency of the approach is demonstrated on a C3540 (8 bit ALU) ISCAS85 Benchmark circuit

HIGH PERFORMANCE CLOCK DISTRIBUTION FOR HIGH-SPEED VLSI SYSTEMS

Tohoku University堀口 進課

Modeling the impact of process variations in worst-case energy consumption estimation

The advent of autonomous power-limited systems poses a new challenge for system verification. Powerful processors needed to enable autonomous operation, are typically power-hungry, jeopardizing battery duration. Therefore, guaranteeing a given battery duration requires worst-case energy consumption (WCEC) estimation for tasks running on those systems. Unfortunately, processor energy and power can suffer significant variation across different units due to process variation (PV), i.e. variability in the electrical properties of transistors and wires due to imperfect manufacturing, which challenges existing WCEC estimation methods for applications. In this paper, we propose a statistical modeling approach to capture PV impact on applications energy and a methodology to compute their WCEC capturing PV, as required to deploy portable critical devices.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773). MINECO partially supported Jaume Abella under Ramon y Cajal fellowship RYC-2013-14717.Peer ReviewedPostprint (author's final draft

Max Operation in Statistical Static Timing Analysis on the Non-Gaussian Variation Sources for VLSI Circuits

As CMOS technology continues to scale down, process variation introduces significant uncertainty in power and performance to VLSI circuits and significantly affects their reliability. If this uncertainty is not properly handled, it may become the bottleneck of CMOS technology improvement. As a result, deterministic analysis is no longer conservative and may result in either overestimation or underestimation of the circuit delay. As we know that Static-Timing Analysis (STA) is a deterministic way of computing the delay imposed by the circuits design and layout. It is based on a predetermined set of possible events of process variations, also called corners of the circuit. Although it is an excellent tool, current trends in process scaling have imposed significant difficulties to STA. Therefore, there is a need for another tool, which can resolve the aforementioned problems, and Statistical Static Timing Analysis (SSTA) has become the frontier research topic in recent years in combating such variation effects. There are two types of SSTA methods, path-based SSTA and block-based SSTA. The goal of SSTA is to parameterize timing characteristics of the timing graph as a function of the underlying sources of process parameters that are modeled as random variables. By performing SSTA, designers can obtain the timing distribution (yield) and its sensitivity to various process parameters. Such information is of tremendous value for both timing sign-off and design optimization for robustness and high profit margins. The block-based SSTA is the most efficient SSTA method in recent years. In block-based SSTA, there are two major atomic operations max and add. The add operation is simple; however, the max operation is much more complex. There are two main challenges in SSTA. The Topological Correlation that emerges from reconvergent paths, these are the ones that originate from a common node and then converge again at another node (reconvergent node). Such correlation complicates the maximum operation. The second challenge is the Spatial Correlation. It arises due to device proximity on the die and gives rise to the problems of modeling delay and arrival time. This dissertation presents statistical Nonlinear and Nonnormals canonical form of timing delay model considering process variation. This dissertation is focusing on four aspects: (1) Statistical timing modeling and analysis; (2) High level circuit synthesis with system level statistical static timing analysis; (3) Architectural implementations of the atomic operations (max and add); and (4) Design methodology. To perform statistical timing modeling and analysis, we first present an efficient and accurate statistical static timing analysis (SSTA) flow for non-linear cell delay model with non-Gaussian variation sources. To achieve system level SSTA we apply statistical timing analysis to high-level synthesis flow, and develop yield driven synthesis framework so that the impact of process variations is taken into account during high-level synthesis. To accomplish architectural implementation, we present the vector thread architecture for max operator to minimize delay and variation. Finally, we present comparison analysis with ISCAS benchmark circuits suites. In the last part of this dissertation, a SSTA design methodology is presented

Addressing Manufacturing Challenges in NoC-based ULSI Designs

Hernández Luz, C. (2012). Addressing Manufacturing Challenges in NoC-based ULSI Designs [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1669