Statistical strategies to capture correlation between overshooting effect and propagation delay time in nano-CMOS inverters

In this paper, we model statistical correlation between overshooting effect and propagation delay time in nano-CMOS technology considering the influence of intrinsic parameter fluctuations caused by discreteness of charge and granularity of matter. The impact of input slew rate, output capacitive load, and supply voltage on this statistical correlation is comprehensively studied. Moreover, we propose two alternative approaches which are capable of reproducing the statistical correlation as well as mean and standard deviation of both propagation delay time and overshoot voltage. We evaluate the accuracy of these alternative approaches against accurate Monte-Carlo simulations. It is shown that the statistical correlations are almost preserved using these alternative approaches

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

Etude de la variabilité en technologie FDSOI : du transistor aux cellules mémoires SRAM

The scaling of bulk MOSFETs transistors is facing various difficulties at the nanometer era. The variability of the electrical characteristics becomes a major challenge which increases as the device dimensions are scaled down. Fully-Depleted Silicon On Insulator (FDSOI) technology, developed as an alternative to bulk transistors, exhibits a better electrostatic immunity which enables higher performances. Moreover, the reduction of the Random Dopant Fluctuation allows excellent variability immunity for the FDSOI technology due to its undoped channel. It leads to a yield enhancement and a reduction of the minimum supply voltage of SRAM circuits. The variability has been analyzed deeply during this thesis in this technology, both on the threshold voltage (VT) and on the ON-state current (ISAT). The correlation between the electrical characteristics of MOSFETs devices (i.e., the threshold voltage and the standard deviation σVT) and SRAM cells (i.e., the SNM and σSNM) has been investigated thanks to an extensive experimental study and modeling. This purpose of this thesis is also to analyze the specific FDSOI variability source: silicon thickness fluctuations. An analytical model has been developed in order to quantify the impact of local TSi variations on the VT variability for 28 and 20nm technology nodes, as well as on a 200Mb SRAM array. This model also enables to evaluate the silicon thickness mean (µTsi) and standard deviation (σTsi) specifications for next technology nodes.La miniaturisation des transistors MOSFETs sur silicium massif présente de nombreux enjeux en raison de l'apparition de phénomènes parasites. Notamment, la réduction de la surface des dispositifs entraîne une dégradation de la variabilité de leurs caractéristiques électriques. La technologie planaire totalement désertée, appelée communément FDSOI (pour Fully Depleted Silicon on Insulator), permet d'améliorer le contrôle électrostatique de la grille sur le canal de conduction et par conséquent d'optimiser les performances. De plus, de par la présence d'un canal non dopé, il est possible de réduire efficacement la variabilité de la tension de seuil des transistors. Cela se traduit par un meilleur rendement et par une diminution de la tension minimale d'alimentation des circuits SRAM (pour Static Random Access Memory). Une étude détaillée de la variabilité intrinsèque à cette technologie a été réalisée durant ce travail de recherche, aussi bien sur la tension de seuil (VT) que sur le courant de drain à l'état passant (ISAT). De plus, le lien existant entre la fluctuation des caractéristiques électriques des transistors et des circuits SRAM a été expérimentalement analysé en détail. Une large partie de cette thèse est enfin dédiée à l'investigation de la source de variabilité spécifique à la technologie FDSOI : les fluctuations de l'épaisseur du film de silicium. Un modèle analytique a été développé durant cette thèse afin d'étudier l'influence des fluctuations locales de TSi sur la variabilité de la tension de seuil des transistors pour les nœuds technologiques 28 et 20nm, ainsi que sur un circuit SRAM de 200Mb. Ce modèle a également pour but de fournir des spécifications en termes d'uniformité σTsi et d'épaisseur moyenne µTsi du film de silicium pour les prochains nœuds technologiques

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

Simulation study of scaling design, performance characterization, statistical variability and reliability of decananometer MOSFETs

This thesis describes a comprehensive, simulation based scaling study – including device design, performance characterization, and the impact of statistical variability – on deca-nanometer bulk MOSFETs. After careful calibration of fabrication processes and electrical characteristics for n- and p-MOSFETs with 35 nm physical gate length, 1 nm EOT and stress engineering, the simulated devices closely match the performance of contemporary 45 nm CMOS technologies. Scaling to 25 nm, 18 nm and 13 nm gate length n and p devices follows generalized scaling rules, augmented by physically realistic constraints and the introduction of high-k/metal-gate stacks. The scaled devices attain the performance stipulated by the ITRS. Device a.c. performance is analyzed, at device and circuit level. Extrinsic parasitics become critical to nano-CMOS device performance. The thesis describes device capacitance components, analyzes the CMOS inverter, and obtains new insights into the inverter propagation delay in nano-CMOS. The projection of a.c. performance of scaled devices is obtained. The statistical variability of electrical characteristics, due to intrinsic parameter fluctuation sources, in contemporary and scaled decananometer MOSFETs is systematically investigated for the first time. The statistical variability sources: random discrete dopants, gate line edge roughness and poly-silicon granularity are simulated, in combination, in an ensemble of microscopically different devices. An increasing trend in the standard deviation of the threshold voltage as a function of scaling is observed. The introduction of high-k/metal gates improves electrostatic integrity and slows this trend. Statistical evaluations of variability in Ion and Ioff as a function of scaling are also performed. For the first time, the impact of strain on statistical variability is studied. Gate line edge roughness results in areas of local channel shortening, accompanied by locally increased strain, both effects increasing the local current. Variations are observed in both the drive current, and in the drive current enhancement normally expected from the application of strain. In addition, the effects of shallow trench isolation (STI) on MOSFET performance and on its statistical variability are investigated for the first time. The inverse-narrow-width effect of STI enhances the current density adjacent to it. This leads to a local enhancement of the influence of junction shapes adjacent to the STI. There is also a statistical impact on the threshold voltage due to random STI induced traps at the silicon/oxide interface