Percolation Framework and Monte Carlo Techniques for Improved Probabilistic Design of Variability in Products and Systems

Variability is an inherent feature of any manufactured product or system that can be caused by process inhomogeneity, environmental perturbations, operating stress-induced degradations or unexpected human interference during fabrication and use. The design of a product or system should account for and model the probabilistic nature of these variations that have an impact on its yield, reliability and robustness. This study is intended to present a combination of a theory based on the concept of “percolation” and an algorithm based on Monte Carlo that can serve as a key model-based design tool to quantify the variability in performance/lifetime/material properties/time-based events and identify the possible root cause(s) for the variance so that the design process could be refined to improve and optimize the homogeneity of the population of devices that would be manufactured on a large scale from the evolving product design stage.

[1]  J. Stathis Percolation models for gate oxide breakdown , 1999 .

[2]  Abhijit Chatterjee,et al.  An overview of spatial microscopic and accelerated kinetic Monte Carlo methods , 2007 .

[3]  K. Kaski,et al.  Kinetic Monte Carlo simulation of nucleation on patterned substrates , 2000 .

[4]  Hyohyun Nam,et al.  Comparative study in work-function variation: Gaussian vs. Rayleigh distribution for grain size , 2013, IEICE Electron. Express.

[5]  David J. Srolovitz,et al.  Kinetic Monte Carlo Simulation of Chemical Vapor Deposition , 2002 .

[6]  Charles M. Eastman Design for X: Concurrent Engineering Imperatives , 1996 .

[7]  H E Stanley,et al.  Order propagation near the percolation threshold , 1981 .

[8]  S. Redner,et al.  Introduction To Percolation Theory , 2018 .

[9]  Arthur F. Voter,et al.  Introduction to the Kinetic Monte Carlo Method , 2007 .

[10]  Kurt Binder,et al.  Monte Carlo Simulation in Statistical Physics , 1992, Graduate Texts in Physics.

[11]  Gerard T. Barkema,et al.  Monte Carlo Methods in Statistical Physics , 1999 .

[12]  J. McPherson,et al.  UNDERLYING PHYSICS OF THE THERMOCHEMICAL E MODEL IN DESCRIBING LOW-FIELD TIME-DEPENDENT DIELECTRIC BREAKDOWN IN SIO2 THIN FILMS , 1998 .

[13]  Jinhong Yuan CMOS RF Circuit Design for Reliability and Variability , 2016 .

[14]  A.T. Krishnan,et al.  Analytic Extension of the Cell-Based Oxide Breakdown Model to Full Percolation and its Implications , 2007, 2007 IEEE International Reliability Physics Symposium Proceedings. 45th Annual.

[15]  Kurt Binder,et al.  Monte Carlo Simulation in Statistical Physics , 1992, Graduate Texts in Physics.

[16]  J. Hoshen,et al.  Percolation and cluster structure parameters: The enhanced Hoshen-Kopelman algorithm , 1997 .