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

Statistical circuit simulations - from ‘atomistic’ compact models to statistical standard cell characterisation

This thesis describes the development and application of statistical circuit simulation methodologies to analyse digital circuits subject to intrinsic parameter fluctuations. The specific nature of intrinsic parameter fluctuations are discussed, and we explain the crucial importance to the semiconductor industry of developing design tools which accurately account for their effects. Current work in the area is reviewed, and three important factors are made clear: any statistical circuit simulation methodology must be based on physically correct, predictive models of device variability; the statistical compact models describing device operation must be characterised for accurate transient analysis of circuits; analysis must be carried out on realistic circuit components. Improving on previous efforts in the field, we posit a statistical circuit simulation methodology which accounts for all three of these factors. The established 3-D Glasgow atomistic simulator is employed to predict electrical characteristics for devices aimed at digital circuit applications, with gate lengths from 35 nm to 13 nm. Using these electrical characteristics, extraction of BSIM4 compact models is carried out and their accuracy in performing transient analysis using SPICE is validated against well characterised mixed-mode TCAD simulation results for 35 nm devices. Static d.c. simulations are performed to test the methodology, and a useful analytic model to predict hard logic fault limitations on CMOS supply voltage scaling is derived as part of this work. Using our toolset, the effect of statistical variability introduced by random discrete dopants on the dynamic behaviour of inverters is studied in detail. As devices scaled, dynamic noise margin variation of an inverter is increased and higher output load or input slew rate improves the noise margins and its variation. Intrinsic delay variation based on CV/I delay metric is also compared using ION and IEFF definitions where the best estimate is obtained when considering ION and input transition time variations. Critical delay distribution of a path is also investigated where it is shown non-Gaussian. Finally, the impact of the cell input slew rate definition on the accuracy of the inverter cell timing characterisation in NLDM format is investigated

Cross-Layer Resiliency Modeling and Optimization: A Device to Circuit Approach

The never ending demand for higher performance and lower power consumption pushes the VLSI industry to further scale the technology down. However, further downscaling of technology at nano-scale leads to major challenges. Reduced reliability is one of them, arising from multiple sources e.g. runtime variations, process variation, and transient errors. The objective of this thesis is to tackle unreliability with a cross layer approach from device up to circuit level

Statistical compact model strategies for nano CMOS transistors subject of atomic scale variability

One of the major limiting factors of the CMOS device, circuit and system simulation in sub 100nm regimes is the statistical variability introduced by the discreteness of charge and granularity of matter. The statistical variability cannot be eliminated by tuning the layout or by tightening fabrication process control. Since the compact models are the key bridge between technology and design, it is necessary to transfer reliably the MOSFET statistical variability information into compact models to facilitate variability aware design practice. The aim of this project is the development of a statistical extraction methodology essential to capture statistical variability with optimum set of parameters particularly in industry standard compact model BSIM. This task is accomplished by using a detailed study on the sensitivity analysis of the transistor current in respect to key parameters in compact model in combination with error analysis of the fitted Id-Vg characteristics. The key point in the developed direct statistical compact model strategy is that the impacts of statistical variability can be captured in device characteristics by tuning a limited number of parameters and keeping the values for remaining major set equal to their default values obtained from the “uniform” MOSFET compact model extraction. However, the statistical compact model extraction strategies will accurately represent the distribution and correlation of the electrical MOSFET figures of merit. Statistical compact model parameters are generated using statistical parameter generation techniques such as uncorrelated parameter distributions, principal component analysis and nonlinear power method. The accuracy of these methods is evaluated in comparison with the results obtained from ‘atomistic’ simulations. The impact of the correlations in the compact model parameters has been analyzed along with the corresponding transistor figures of merit. The accuracy of the circuit simulations with different statistical compact model libraries has been studied. Moreover, the impact of the MOSFET width/length on the statistical trend of the optimum set of statistical compact model parameters and electrical figures of merit has been analyzed with two methods to capture geometry dependencies in proposed statistical models

Robustness Analysis of Controllable-Polarity Silicon Nanowire Devices and Circuits

Substantial downscaling of the feature size in current CMOS technology has confronted digital designers with serious challenges including short channel effect and high amount of leakage power. To address these problems, emerging nano-devices, e.g., Silicon NanoWire FET (SiNWFET), is being introduced by the research community. These devices keep on pursuing Mooreâs Law by improving channel electrostatic controllability, thereby reducing the Off âstate leakage current. In addition to these improvements, recent developments introduced devices with enhanced capabilities, such as Controllable-Polarity (CP) SiNWFETs, which make them very interesting for compact logic cell and arithmetic circuits. At advanced technology nodes, the amount of physical controls, during the fabrication process of nanometer devices, cannot be precisely determined because of technology fluctuations. Consequently, the structural parameters of fabricated circuits can be significantly different from their nominal values. Moreover, giving an a-priori conclusion on the variability of advanced technologies for emerging nanoscale devices, is a difficult task and novel estimation methodologies are required. This is a necessity to guarantee the performance and the reliability of future integrated circuits. Statistical analysis of process variation requires a great amount of numerical data for nanoscale devices. This introduces a serious challenge for variability analysis of emerging technologies due to the lack of fast simulation models. One the one hand, the development of accurate compact models entails numerous tests and costly measurements on fabricated devices. On the other hand, Technology Computer Aided Design (TCAD) simulations, that can provide precise information about devices behavior, are too slow to timely generate large enough data set. In this research, a fast methodology for generating data set for variability analysis is introduced. This methodology combines the TCAD simulations with a learning algorithm to alleviate the time complexity of data set generation. Another formidable challenge for variability analysis of the large circuits is growing number of process variation sources. Utilizing parameterized models is becoming a necessity for chip design and verification. However, the high dimensionality of parameter space imposes a serious problem. Unfortunately, the available dimensionality reduction techniques cannot be employed for three main reasons of lack of accuracy, distribution dependency of the data points, and finally incompatibility with device and circuit simulators. We propose a novel technique of parameter selection for modeling process and performance variation. The proposed technique efficiently addresses the aforementioned problems. Appropriate testing, to capture manufacturing defects, plays an important role on the quality of integrated circuits. Compared to conventional CMOS, emerging nano-devices such as CP-SiNWFETs have different fabrication process steps. In this case, current fault models must be extended for defect detection. In this research, we extracted the possible fabrication defects, and then proposed a fault model for this technology. We also provided a couple of test methods for detecting the manufacturing defects in various types of CP-SiNWFET logic gates. Finally, we used the obtained fault model to build fault tolerant arithmetic circuits with a bunch of superior properties compared to their competitors

Solid State Circuits Technologies

The evolution of solid-state circuit technology has a long history within a relatively short period of time. This technology has lead to the modern information society that connects us and tools, a large market, and many types of products and applications. The solid-state circuit technology continuously evolves via breakthroughs and improvements every year. This book is devoted to review and present novel approaches for some of the main issues involved in this exciting and vigorous technology. The book is composed of 22 chapters, written by authors coming from 30 different institutions located in 12 different countries throughout the Americas, Asia and Europe. Thus, reflecting the wide international contribution to the book. The broad range of subjects presented in the book offers a general overview of the main issues in modern solid-state circuit technology. Furthermore, the book offers an in depth analysis on specific subjects for specialists. We believe the book is of great scientific and educational value for many readers. I am profoundly indebted to the support provided by all of those involved in the work. First and foremost I would like to acknowledge and thank the authors who worked hard and generously agreed to share their results and knowledge. Second I would like to express my gratitude to the Intech team that invited me to edit the book and give me their full support and a fruitful experience while working together to combine this book

Statistical modelling of nano CMOS transistors with surface potential compact model PSP

The development of a statistical compact model strategy for nano-scale CMOS transistors is presented in this thesis. Statistical variability which arises from the discreteness of charge and granularity of matter plays an important role in scaling of nano CMOS transistors especially in sub 50nm technology nodes. In order to achieve reasonable performance and yield in contemporary CMOS designs, the statistical variability that affects the circuit/system performance and yield must be accurately represented by the industry standard compact models. As a starting point, predictive 3D simulation of an ensemble of 1000 microscopically different 35nm gate length transistors is carried out to characterize the impact of statistical variability on the device characteristics. PSP, an advanced surface potential compact model that is selected as the next generation industry standard compact model, is targeted in this study. There are two challenges in development of a statistical compact model strategy. The first challenge is related to the selection of a small subset of statistical compact model parameters from the large number of compact model parameters. We propose a strategy to select 7 parameters from PSP to capture the impact of statistical variability on current-voltage characteristics. These 7 parameters are used in statistical parameter extraction with an average RMS error of less than 2.5% crossing the whole operation region of the simulated transistors. Moreover, the accuracy of statistical compact model extraction strategy in reproducing the MOSFET electrical figures of merit is studied in detail. The results of the statistical compact model extraction are used for statistical circuit simulation of a CMOS inverter under different input-output conditions and different number of statistical parameters. The second challenge in the development of statistical compact model strategy is associated with statistical generation of parameters preserving the distribution and correlation of the directly extracted parameters. By using advanced statistical methods such as principal component analysis and nonlinear power method, the accuracy of parameter generation is evaluated and compared to directly extracted parameter sets. Finally, an extension of the PSP statistical compact model strategy to different channel width/length devices is presented. The statistical trends of parameters and figures of merit versus channel width/length are characterized

Intrinsic variability of nanoscale CMOS technology for logic and memory.

The continuous downscaling of CMOS technology, the main engine of development of the semiconductor Industry, is limited by factors that become important for nanoscale device size, which undermine proper device operation completely offset gains from scaling. One of the main problems is device variability: nominally identical devices are different at the microscopic level due to fabrication tolerance and the intrinsic granularity of matter. For this reason, structures, devices and materials for the next technology nodes will be chosen for their robustness to process variability, in agreement with the ITRS (International Technology Roadmap for Semiconductors). Examining the dispersion of various physical and geometrical parameters and the effect these have on device performance becomes necessary. In this thesis, I focus on the study of the dispersion of the threshold voltage due to intrinsic variability in nanoscale CMOS technology for logic and for memory. In order to describe this, it is convenient to have an analytical model that allows, with the assistance of a small number of simulations, to calculate the standard deviation of the threshold voltage due to the various contributions