Intrinsic Parameter Fluctuations Research Articles

A Machine Learning Approach to Modeling Intrinsic Parameter Fluctuation of Gate-All-Around Si Nanosheet MOSFETs

The sensitivity of semiconductor devices to any microscopic perturbation is increasing with the continuous shrinking of device technology. Even the small fluctuations have become more acute for highly scaled nano-devices. Therefore, these fluctuations need to be addressed extensively in order to continue further device scaling. In this paper, we mainly focus on three intrinsic parameter fluctuation sources, work function fluctuation (WKF), random dopant fluctuation (RDF), and interface trap fluctuation (ITF) for gate-all-around (GAA) silicon nanosheet (NS) MOSFETs. Generally, the effect of these fluctuations is analyzed using a time-consuming device simulation process. A machine learning (ML) based powerful and efficient artificial neural network (ANN) model is used to accelerate this process. Firstly, the effects of fluctuation sources are analyzed individually by using the ANN model and results have been presented that show the WKF variations dominate the threshold voltage (<i>V_TH</i>), off-state current (<i>I_OFF</i>), and on-state current (<i>I_ON</i>) variations among other fluctuation sources. Next, we examine the combined effect of all three fluctuation sources. It is crucial because considering only one fluctuation can result in unexpected variations due to other fluctuations present in the device. Therefore, the ANN model is used to estimate the combined effects as well. The results show that the proposed model predicts the outputs with an R²-score of 99% and an error rate of less than 1%. In addition, the ML is also utilized to calculate the permutation importance of input variables as a measure to investigate the contribution of each fluctuation source.

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.

Investigation of metal-gate work-function variability in FinFET structures and implications for SRAM cell design

In sub-20 nm CMOS technology nodes, the parameter variability has become a main hurdle during the scaling of devices. Recently, the use of metal gate stacks in nano-scale FinFET structure has a significant impact on the intrinsic parameter fluctuations due to the granular nature of metals. A 14 nm FinFET structure has been considered to study the impact of metal-gate work-function variability by using the 3-D device and mixed mode circuit simulation. The present work investigates the impact of work function variability (WFV) on electrical characteristics of various possible FinFET architectures followed by the regression model. Further, the investigation has been extended to study the stability performance of 6-T SRAM cell under the influence of WFV. It is observed that work function variation may result in considerable performance degradation in device as well as SRAM operation in nano domain.

Systematic Design of a Metal Ion Biosensor: A Multi-Objective Optimization Approach.

With the recent industrial expansion, heavy metals and other pollutants have increasingly contaminated our living surroundings. Heavy metals, being non-degradable, tend to accumulate in the food chain, resulting in potentially damaging toxicity to organisms. Thus, techniques to detect metal ions have gradually begun to receive attention. Recent progress in research on synthetic biology offers an alternative means for metal ion detection via the help of promoter elements derived from microorganisms. To make the design easier, it is necessary to develop a systemic design method for evaluating and selecting adequate components to achieve a desired detection performance. A multi-objective (MO) H2/H∞ performance criterion is derived here for design specifications of a metal ion biosensor to achieve the H2 optimal matching of a desired input/output (I/O) response and simultaneous H∞ optimal filtering of intrinsic parameter fluctuations and external cellular noise. According to the two design specifications, a Takagi-Sugeno (T-S) fuzzy model is employed to interpolate several local linear stochastic systems to approximate the nonlinear stochastic metal ion biosensor system so that the multi-objective H2/H∞ design of the metal ion biosensor can be solved by an associated linear matrix inequality (LMI)-constrained multi-objective (MO) design problem. The analysis and design of a metal ion biosensor with optimal I/O response matching and optimal noise filtering ability then can be achieved by solving the multi-objective problem under a set of LMIs. Moreover, a multi-objective evolutionary algorithm (MOEA)-based library search method is employed to find adequate components from corresponding libraries to solve LMI-constrained MO H2/H∞ design problems. It is a useful tool for the design of metal ion biosensors, particularly regarding the tradeoffs between the design factors under consideration.

Variability Analysis of Discharge Characteristics and Impedance Spectra in Li-Air Batteries

Electrochemical energy storage devices are well known to show large variation in discharge characteristics, even though, they are fabricated using the same technological processes and much care is given to produce identical cells. This variation causes the discharge voltages, specific capacity, energy and power density of energy storage devices to vary significantly from one cell to another. Among the current energy storage devices, metal-air batteries suffer from a particularly large variability due to a number of extrinsic and intrinsic parameter variations (1). On the one side, the continuously changing atmospheric conditions such as operating temperature, humidity, outside pressure, and wind, as well as random instrumentation errors, induce a significant variability of reported cell characteristics in the literature. On the other side, intrinsic parameter variations such as different microstructural properties that arise from the cathode preparation techniques that make it impossible to fabricate identical porous materials also result in different cell characteristics. The goal of this presentation is twofold: First, we analyze the effects of intrinsic and extrinsic parameter fluctuations on the discharge characteristics and impedance spectra of Li-air batteries. Second, we develop a small-signal perturbation technique for the characterization of the variability in Li-air batteries. The analysis of the parameter fluctuation induced effects is done using Monte-Carlo methods. For this purpose, we generate a large number of the devices with identical macroscopic but different microscopic configurations and simulate each device independently. In this way we accumulate statistics for the parameters of interest – in our case the discharge curves, specific capacity, and impedance spectra – and compute the average values and standard deviations of these parameters. The Monte-Carlo method is computationally very expensive because it requires simulating many different devices. If we assume the parameter fluctuations are normally distributed, the error in computing the standard deviation of one parameter of interest is approximately equal to 1/sqrt(2N), where Nis the number of devices simulated. This equation shows that in order to compute the standard deviation of a parameter with an error of less than 5% we need to perform at least 200 device simulations. For this reason, we develop a new technique for the analysis of variability in electrochemical devices based on small-signal perturbations. We assume that the fluctuating parameters that appear in the transport equations are random variables characterized by some average values and given standard deviations. Then, we expand the transport equations in power series and find analytical equations for the average values and fluctuations of the parameters of interest. These equations are then solved either analytically or using finite element models and the results are compared the Monte-Carlo method. The computational complexity of perturbation techniques is much smaller than the complexity of Monte-Carlo methods because perturbations techniques require solving the (nonlinear) transport system of equations once and a few linear systems of equations. At the conference we will present results for the discharge characteristics obtained using the finite element model presented in (2). The impedance spectra are modeled using both the compact model presented in (3) and the finite element approach presented in (4). Our initial results show a very good agreement between the Monte-Carlo simulations and the linearization approach. A detail discussion of the variability of the discharge characteristics, specific capacity, and impedance spectra in Li-air will be presented at the conference. References L. D. Griffith, A. E. S. Sleightholme, J. F. Mansfield, D. J. Siegel and C. W. Monroe, ACS Appl. Mater. Interfaces, 7, 7670 (2015).P. Andrei, J. P. Zheng, M. Hendrickson and E. J. Plichta, J. Electrochem. Soc., 157, A1287 (2010).M. Mehta, G. Mixon, J. Zheng and P. Andrei, J. Electrochem. Soc., 160, A2033 (2013).M. Mehta and P. Andrei, ECS Trans., 61(15), 39 (2014).

The Stochastic Evolutionary Game for a Population of Biological Networks under Natural Selection

In this study, a population of evolutionary biological networks is described by a stochastic dynamic system with intrinsic random parameter fluctuations due to genetic variations and external disturbances caused by environmental changes in the evolutionary process. Since information on environmental changes is unavailable and their occurrence is unpredictable, they can be considered as a game player with the potential to destroy phenotypic stability. The biological network needs to develop an evolutionary strategy to improve phenotypic stability as much as possible, so it can be considered as another game player in the evolutionary process, ie, a stochastic Nash game of minimizing the maximum network evolution level caused by the worst environmental disturbances. Based on the nonlinear stochastic evolutionary game strategy, we find that some genetic variations can be used in natural selection to construct negative feedback loops, efficiently improving network robustness. This provides larger genetic robustness as a buffer against neutral genetic variations, as well as larger environmental robustness to resist environmental disturbances and maintain a network phenotypic traits in the evolutionary process. In this situation, the robust phenotypic traits of stochastic biological networks can be more frequently selected by natural selection in evolution. However, if the harbored neutral genetic variations are accumulated to a sufficiently large degree, and environmental disturbances are strong enough that the network robustness can no longer confer enough genetic robustness and environmental robustness, then the phenotype robustness might break down. In this case, a network phenotypic trait may be pushed from one equilibrium point to another, changing the phenotypic trait and starting a new phase of network evolution through the hidden neutral genetic variations harbored in network robustness by adaptive evolution. Further, the proposed evolutionary game is extended to an n-tuple evolutionary game of stochastic biological networks with m players (competitive populations) and k environmental dynamics.

The intrinsic parameter fluctuation on high-κ/metal gate bulk FinFET devices

In this work, based on the experimentally calibrated 3D device simulation, we for the first time estimate the impact of intrinsic parameter fluctuation on the electrical characteristic of 16-nm-gate TiN/HfO2 bulk FinFETs. The sources of intrinsic parameter fluctuation include the random discrete dopants, interface traps and work function differences, simultaneously. The full 3D simulated threshold voltage fluctuation, induced by the aforementioned random sources simultaneously, is 26.2 mV for the N-type bulk FinFET (and is 55.5 mV for the planar N-MOSFET). For the N-type bulk FinFET, the statistical sum of these fluctuations is 9.5% (and is 12.3% for the planar device) overestimation, compared with the full 3D simulation. One of the main reasons is the independence assumption on these random variables is destroyed owing to interactions to different extents among RDs, ITs and WKs. The coupled surface potentials cannot be simply estimated by using their statistical sum of individual random source. Under the same threshold voltage, compared with the result of the planar MOSFETs, more than 50% reduction on the threshold voltage fluctuation of the explored bulk FinFETs is observed owing to the benefit of 3D structural nature.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology.

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Tradeoff on Phenotype Robustness in Biological Networks Part II: Ecological Networks.

In ecological networks, network robustness should be large enough to confer intrinsic robustness for tolerating intrinsic parameter fluctuations, as well as environmental robustness for resisting environmental disturbances, so that the phenotype stability of ecological networks can be maintained, thus guaranteeing phenotype robustness. However, it is difficult to analyze the network robustness of ecological systems because they are complex nonlinear partial differential stochastic systems. This paper develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance sensitivity in ecological networks. We found that the phenotype robustness criterion for ecological networks is that if intrinsic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations and environmental disturbances. These results in robust ecological networks are similar to that in robust gene regulatory networks and evolutionary networks even they have different spatial-time scales.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view.

Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing

BackgroundCollective rhythms of gene regulatory networks have been a subject of considerable interest for biologists and theoreticians, in particular the synchronization of dynamic cells mediated by intercellular communication. Synchronization of a population of synthetic genetic oscillators is an important design in practical applications, because such a population distributed over different host cells needs to exploit molecular phenomena simultaneously in order to emerge a biological phenomenon. However, this synchronization may be corrupted by intrinsic kinetic parameter fluctuations and extrinsic environmental molecular noise. Therefore, robust synchronization is an important design topic in nonlinear stochastic coupled synthetic genetic oscillators with intrinsic kinetic parameter fluctuations and extrinsic molecular noise.ResultsInitially, the condition for robust synchronization of synthetic genetic oscillators was derived based on Hamilton Jacobi inequality (HJI). We found that if the synchronization robustness can confer enough intrinsic robustness to tolerate intrinsic parameter fluctuation and extrinsic robustness to filter the environmental noise, then robust synchronization of coupled synthetic genetic oscillators is guaranteed. If the synchronization robustness of a population of nonlinear stochastic coupled synthetic genetic oscillators distributed over different host cells could not be maintained, then robust synchronization could be enhanced by external control input through quorum sensing molecules. In order to simplify the analysis and design of robust synchronization of nonlinear stochastic synthetic genetic oscillators, the fuzzy interpolation method was employed to interpolate several local linear stochastic coupled systems to approximate the nonlinear stochastic coupled system so that the HJI-based synchronization design problem could be replaced by a simple linear matrix inequality (LMI)-based design problem, which could be solved with the help of LMI toolbox in MATLAB easily.ConclusionIf the synchronization robustness criterion, i.e. the synchronization robustness ≥ intrinsic robustness + extrinsic robustness, then the stochastic coupled synthetic oscillators can be robustly synchronized in spite of intrinsic parameter fluctuation and extrinsic noise. If the synchronization robustness criterion is violated, external control scheme by adding inducer can be designed to improve synchronization robustness of coupled synthetic genetic oscillators. The investigated robust synchronization criteria and proposed external control method are useful for a population of coupled synthetic networks with emergent synchronization behavior, especially for multi-cellular, engineered networks.

Multiobjective H2/ H∞ synthetic gene network design based on promoter libraries

Some current promoter libraries have been developed for synthetic gene networks. But an efficient method to engineer a synthetic gene network with some desired behaviors by selecting adequate promoters from these promoter libraries has not been presented. Thus developing a systematic method to efficiently employ promoter libraries to improve the engineering of synthetic gene networks with desired behaviors is appealing for synthetic biologists. In this study, a synthetic gene network with intrinsic parameter fluctuations and environmental disturbances in vivo is modeled by a nonlinear stochastic system. In order to engineer a synthetic gene network with a desired behavior despite intrinsic parameter fluctuations and environmental disturbances in vivo, a multiobjective H 2/ H ∞ reference tracking ( H 2 optimal tracking and H ∞ noise filtering) design is introduced. The H 2 optimal tracking can make the tracking errors between the behaviors of a synthetic gene network and the desired behaviors as small as possible from the minimum mean square error point of view, and the H ∞ noise filtering can attenuate all possible noises, from the worst-case noise effect point of view, to achieve a desired noise filtering ability. If the multiobjective H 2/ H ∞ reference tracking design is satisfied, the synthetic gene network can robustly and optimally track the desired behaviors, simultaneously. First, based on the dynamic gene regulation, the existing promoter libraries are redefined by their promoter activities so that they can be efficiently selected in the design procedure. Then a systematic method is developed to select an adequate promoter set from the redefined promoter libraries to synthesize a gene network satisfying these two design objectives. But the multiobjective H 2/ H ∞ reference tracking design problem needs to solve a difficult Hamilton–Jacobi Inequality (HJI)-constrained optimization problem. Therefore, the fuzzy approximation method is employed to simplify the HJI-constrained optimization problem to an equivalent linear matrix inequality (LMI)-constrained optimization problem, which can be easily solved by selecting an adequate promoter set from the redefined promoter libraries using the LMI toolbox in Matlab. Based on the confirmation of in silico design examples, we can select an adequate promoter set from the redefined promoter libraries to achieve the multiobjective H 2/ H ∞ reference tracking design. The proposed method can reduce the number of trial-and-error experiments in selecting an adequate promoter set for a synthetic gene network with desired behaviors. With the rapid increase of promoter libraries, this systematic method will accelerate progress of synthetic biology design.

Simulation Study of Intrinsic Parameter Fluctuations in Variable-Body-Factor Silicon-on-Thin-Box Metal Oxide Semiconductor Field Effect Transistors

The effects of intrinsic parameter fluctuations, including line-edge-roughness (LER), silicon-body thickness variation (STV) and work-function variation (WFV), in 20-nm-gate variable-γ silicon-on-thin-box (SOTB) metal oxide semiconductor field effect transistors (MOSFETs) have been investigated and compared with those of the conventional SOTB. Results show that the variable-γ SOTB offers not only an enhanced Ion but also a reduced Ion fluctuation with a small increase in the active-state Ioff fluctuation. The Vth-roll-off value in the variable-γ SOTB can be reduced by adopting a reverse-biased side gate to optimize the short channel effect, but the variability of the DIBL effect is enlarged. It is expected that a thinner silicon body can be used to reduce the dominant variability sources.

Variability Induced by Line Edge Roughness in Double-Gate Dopant-Segregated Schottky MOSFETs

Intrinsic parameter fluctuations introduced by process variations, such as line edge roughness (LER), create an increasing challenge to the CMOS technology scaling. In this paper, variations in double-gate dopant-segregate Schottky (DSS) MOSFETs, caused by LER of silicon-fin, are systematically investigated using statistical technology computer-aided design simulations. The impact of LER on both Schottky barrier and DSS-MOSFETs are examined contrastively. The results show that DSS-MOSFETs offer a larger and more uniform drive current, but suffer a more serious Vt fluctuation. The cause of such larger Vt flutuation is also analyzed, thus providing a good starting point to propose way to solve this problem.

Robust model matching design methodology for a stochastic synthetic gene network

Synthetic biology has shown its potential and promising applications in the last decade. However, many synthetic gene networks cannot work properly and maintain their desired behaviors due to intrinsic parameter variations and extrinsic disturbances. In this study, the intrinsic parameter uncertainties and external disturbances are modeled in a non-linear stochastic gene network to mimic the real environment in the host cell. Then a non-linear stochastic robust matching design methodology is introduced to withstand the intrinsic parameter fluctuations and to attenuate the extrinsic disturbances in order to achieve a desired reference matching purpose. To avoid solving the Hamilton–Jacobi inequality (HJI) in the non-linear stochastic robust matching design, global linearization technique is used to simplify the design procedure by solving a set of linear matrix inequalities (LMIs). As a result, the proposed matching design methodology of the robust synthetic gene network can be efficiently designed with the help of LMI toolbox in Matlab. Finally, two in silico design examples of the robust synthetic gene network are given to illustrate the design procedure and to confirm the robust model matching performance to achieve the desired behavior in spite of stochastic parameter fluctuations and environmental disturbances in the host cell.

Robust Design of Biological Circuits: Evolutionary Systems Biology Approach

Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise.

Robust Optimal Reference-Tracking Design Method for Stochastic Synthetic Biology Systems: T–S Fuzzy Approach

At present, the development in the nascent field of synthetic gene networks is still difficult. Most newly created gene networks are nonfunctioning due to intrinsic parameter fluctuations, uncertain interactions with unknown molecules and external disturbances of intra and extracellular environments on the host cell. How to design a completely new gene network, that is to track some desired behaviors under these intrinsic and extrinsic disturbances on the host cell, is the most important topic in synthetic biology. In this study, the intrinsic parameter fluctuations, uncertain interactions with unknown molecules and environmental disturbances, are modeled into the nonlinear stochastic systems of synthetic gene networks in vivo. Four design specifications are introduced to guarantee the stochastic synthetic gene network, which can achieve robust optimal tracking of a desired reference model in spite of these intrinsic and extrinsic disturbances on the host cell. However, the robust optimal reference-tracking design problem of nonlinear synthetic gene networks is still hard to solve. In order to simplify the design procedure of the robust optimal nonlinear stochastic-tracking design for synthetic gene networks, the Takagi-Sugeno (T-S) fuzzy method is introduced to solve the nonlinear stochastic minimum-error-tracking design problem. Hence, the robust optimal reference-tracking design problem under four design specifications can be solved by the linear matrix inequality (LMI)-constrained optimization method using convex optimization techniques. Further, a simple design procedure is developed for synthetic gene networks to meet the four design specifications to achieve robust optimal reference tracking. Finally, an eigenvalue-shifted design method is also proposed as an expedient scheme to improve the stochastic optimal-tracking design method of synthetic gene oscillators.

Impact of Attractive Ion in Undoped Channel on Characteristics of Nanoscale Multigate Field Effect Transistors: A Three-Dimensional Nonequilibrium Green's Function Study

A rigorous simulation study of the impact of a single attractive ion in undoped channel multigate field-effect transistors is presented using a new three-dimensional nonequilibrium Green's function technique. A single donor induces threshold voltage shift, and its impact is most significant when the donor is located at the top of the potential barrier. On the other hand, on-current is not affected so much because of the electrostatic screening by the electron bound around the positively charged ion. To reduce the intrinsic device parameter fluctuation, a gate-all-around structure has better robustness than the double gate structure.

The impact of high-frequency characteristics induced by intrinsic parameter fluctuations in nano-MOSFET device and circuit

This work estimates the influences of the intrinsic parameter fluctuations consisting of metal gate workfunction fluctuation (WKF), process variation effect (PVE) and random dopant fluctuation (RDF) on 16-nm-gate planar metal–oxide–semiconductor field effect transistors (MOSFETs) and circuits by using an experimentally validated three-dimensional device and coupled device–circuit simulations. The dominance fluctuation source in threshold voltage, gate capacitance, cut-off frequency, high-frequency gain, 3 dB bandwidth, unity-gain bandwidth, power, and the power-added efficiency has been found. Similar to the trend of the cut-off frequency, the PVE and RDF dominate both the device and circuits characteristic fluctuations due to the significant gate capacitance fluctuations and the WKF is less important at this simulation scenario.

Statistical variability in FinFET devices with intrinsic parameter fluctuations

High-κ/metal-gate and vertical channel transistors are well-known solutions to continue the device scaling. This work extensively estimates the influences of the intrinsic parameter fluctuations on nanoscale fin-type field-effect-transistors and circuits by using an experimentally validated three-dimensional device and coupled device-circuit simulations. The dominance fluctuation source in threshold voltage, gate capacitance, cut-off frequency, delay time, and power has been found. The emerging fluctuation source, workfunction fluctuation, shows significant impacts on DC characteristics; however, can be ignored in AC characteristics due to the screening effect of the inversion layer.