Mingzhi Gao

First-principles investigation on bonding formation and electronic structure of metal-graphene contacts

Metal-graphene contacts play a critical role in graphene-based electronics. It is found through first-principles calculation of contacts between graphene and 12 different metals that there exist two types of contacts depending on the strength of interaction between d-orbitals in metals and pz-orbitals in graphene. Fermi level shift in the contacted graphene from the freestanding one is investigated, and the electronic structure and electrostatic potential are calculated. The carrier transport through these contacts is calculated using the extended Huckel theory-based non-equilibrium Green’s function formalism, and one type of contact is shown to have less contact resistance than the other.

A comprehensive model for gate delay under process variation and different driving and loading conditions

Gate delay models taking process variation into account are an essential part of ascendant statistical static timing analysis (SSTA). The statistical gate delay models in being, most of which take the forms of low order polynomials, are suffering from either enormous characterization cost or poor accuracy. We propose in this paper a statistical comprehensive gate delay model including both the effects of process variation and operating conditions, i.e. input slope and output load. With the help of effective dimension reduction, we can use only a couple of random variables to present the effect of process variation, which enables a simple modeling methodology as well as a cheap characterization process. This model can be changed into the polynomial forms required in some block based SSTA or directly used in Monte Carlo based SSTA. The error of the model is shown well below 5% compared with golden Monte Carlo data.

A novel framework for passive macro-modeling

Passivity enforcement is an important issue for macro-modeling for passive systems from measured or simulated data. Existing convex programming based methods are too expensive and thus are ruled out for realistic application. Other methods based on iteratively fixing the passivity through perturbing the eigenvalues of the Hamiltonian matrix either suffer from convergence issue or lack optimality which will sometimes lead to unacceptable error. In this paper we propose a novel framework for macro-modeling. In addition to the traditional two-stage (fixing plus enforcement) schemes, we propose a post-enforcement optimization, which takes a passive, while potentially not-so-accurate model, as the starting point, and performs local search to find the local optimum with passivity constraint or build-in passivity guarantee. A simple yet stable passive modeling generator is proposed to produce the starting model for optimization. Two algorithms are proposed for performing constrained and unconstrained optimizations. Experiments show that the accuracy of passivity-fixed model can be significantly improved with the proposed methods.

Efficient tail estimation for massive correlated log-normal sums: with applications in statistical leakage analysis

Existing approaches to statistical leakage analysis focus only on calculating the mean and variance of the total leakage. In practice, however, what concerns most is the tail behavior of the sum distribution, as it tells that to what extent the design will be safe or reliable. However, computing the tail distribution is much more difficult than computing the mean and variance. In this paper, we tackle this problem by making use of the recent developments in the area of financial and insurance analysis, as well as the fast evaluation algorithm for the variance of spatially correlated random sums. The proposed algorithm is provably of O(N) complexity. Experiments show that the algorithm provides 1% accuracy in modeling the tail behavior and it is 10X more accurate compared with existing methods that approximate the distribution by matching the moments of a lognormal distribution.

Molecular Dynamics Models of Several Hundreds of Atoms for Back-End-of-Line Dielectrics

Porous low-k materials with low dielectric constant and reasonable mechanicalal property are demanded urgently for microelectronic applications. In this paper, we proposed a pure classic Molecular Dynamics based simulation method to create atomic models for amorphous dielectrics. Elaborately generating an initial guess for the Molecular Dynamics relaxation allows the structure to contain several hundreds of atoms and realizes pores and special local features at one time. Both mechanical and dielectric properties can be obtained within the classic Molecular Dynamics framework. This method has been applied to the modeling of the microstructure of SiO2 system as an example. In a 360-atom SiO2 system, we can modify the size of the pores inside the dielectrics. The calculated properties are consistent with the experimental results.

Atomistic Simulation of plasma enhanced chemical vapor deposited SiCOH dielectrics

As microelectronic circuits become smaller, inter-metal insulators in the circuits need to have lower dielectric constant (low-k) for preventing signal delays and cross talks. Theoretical studies on the mechanical and dielectric properties of low-k films of silicon oxycarbide materials (SiOCH) are highly desired. In the paper, we used a new method to investigate SiCOH films properties at the atomistic level. By the method, one can construct the atomic structure of SiCOH film using available experimental data. To illustrate the method, we applied the method to construct atomic structure of a typical SiCOH film which is made by plasma-enhanced chemical vapor deposition (PECVD). We confirmed that the method creates reasonable SiCOH structures that can explain experimental results of density, Fourier transform infrared (FTIR) spectrum, elastic and dielectric properties. Limitations and problems of the method are discussed

Efficient Full-Chip Statistical Leakage Analysis Based on Fast Matrix Vector Product

Power consumption has become a major concern since the integrated circuit industry entered the nanometer design regime. Due to the increasing process variation, deterministic leakage power analysis becomes inadequate and thus statistical analysis is required. The challenges of statistical leakage analysis are that the huge number of random variables make trivial computation of the variance in O(N2) time impractical for realistic designs and that knowing only the first two moments is not sufficient to obtain the distribution of the full-chip leakage. In this paper, we introduce efficient linear time algorithms for statistical leakage analysis. To enable those algorithms, a fast matrix vector product technique is crucial, being applied not only to compute the second moment of the total leakage, but also, combined with a comonotonic approximation, to estimate the distribution function of the total leakage power. The computational complexity of the proposed algorithms is provably O(N), and the experimental result is presented with detailed discussion, indicating promising improvement in terms of accuracy.

Robust spatial correlation extraction with limited sample via L1-norm penalty

Random process variations are often composed of location dependent part and distance dependent correlated part. While an accurate extraction of process variation is a prerequisite of both process improvement and circuit performance prediction, it is not an easy task to characterize such complicated spatial random process from a limited number of silicon data. For this purpose, kriging model was introduced to silicon society. This work forms a modified kriging model with L1-norm penalty which offers improved robustness. With the help of Least Angle Regression (LAR) in solving a core optimization sub-problem, this model can be characterized efficiently. Some promising results are presented with numerical experiments where a 3X improvement in model accuracy is shown.

First-principles calculations for effects of Fluorine impurity in GaN

Fluoride-based plasma treatment has been approved to be an effective technique to make E-Mode GaN-based HEMT by experiments. However, the detailed effect of Fluorine doping in GaN and AlGaN is still unclear and has never been studied theoretically. In this paper, F impurity in GaN material is firstly studied by First Principle Calculations. Sound explanations for the experimental phenomenon are derived from the results and further discuss is presented.

First-principle calculation for luminescent-effects of Si and Zn impurities in GaN

Phosphor-free GaN-based white-light LED which is usually generated as a combination of the blue bandedge emission and a yellow-green broad-band emission has been approved to be more reliable than phosphor-based white-light LED. First-principle method was employed to investigate the luminescent-effects of Si and Zn impurities in phosphor-free GaN-based LED. By explicitly calculating the formation energies and defect levels, the origin of yellow-green broad-band emission in Si and Zn co-dopoed InGaN/GaN multiquantum wells(MQWs) were discussed and determined. We propose that the electron transition between Zni/ZnN-SiN D-A pairs are responsible for the observed yellow-green broad-band emission.